esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Issue Negotiation Protocol with Pre-Domain Narrowing
## 5 Experiments
### Discussion for Privacy Issue of Our Proposed Negotiation Protocol  
Our proposed negotiation protocol only uses the mediator in the pre-negotiation phase. The negotiation protocols the mediator is used in the main negotiation phase have the limitation of the agents' privacy because all agents have to disclose their private information by following the mediator and pre-decided negotiation rules. Conversely, the only information an agent must submit to the mediator is \"whether an option may be deleted\". Therefore, private information (e.g., utility information) can be disclosed to the mediator. In addition, the only information the other agent can know is regarding \"the options that both agents agreed to delete\" and \"the options that an opposing agent agreed to delete, but that this agent did not agree to.\" In other words, the other agent cannot know more information unless it increases the options it agrees to delete. Therefore, each agent can efficiently narrow the options in the negotiation domain space while maintaining privacy.  
Figure 8: Results from negotiation with narrowing phase: distance to Pareto optimal solution. The numerical values are ratios where the results of negotiation without narrowing are set to 1. The blue gradation means the magnitude of the values that is less than 1, and the red gradation means the magnitude of the values that is more than 1. (**A**) Simultaneous submission. (**B**) Simultaneous submission + Narrowing of options. (**C**)Pre-negotiation. (**D**) Pre-negotiation + narrowing of options.  
Figure 9: Comparative results of line graphs among approaches. The bar means the average of all scores, and the var means the standard error of all scores.  
Suppose we consider the other existing negotiation protocol with the mediator in the main negotiation phase to solve the multilateral and complex domain cases. In that case, our proposed approach can escape to reveal the private information related to each option, issue, and bid. The reason for this is that negotiation the domain-narrowing phase, the domain size to be negotiated in the main negotiation phase was reduced from 1/100 to 1/1000 compared with the approach without the pre-negotiation phase. From this point of view, our proposed method contributes to solving the privacy issue.
## 6 Roadmap Profile Classification  
Above we have shown that the specific features of the scenarios must be taken into account to make the future proposals more representative and valid. To achieve this goal, maps from several existing cities have been tested to obtain a classification that allows future researchers to determine which scenarios to use in their simulations. In each scenario analyzed, the chosen area tries to represent the overall layout of the streets in each city, and is usually taken from downtown. We selected representative cities from Europe (Berlin, London, Milan, Moscow, Paris, Rome, Seville, Teruel, Valencia), Asia (Beijing, Hong Kong, Istanbul, Kuala Lumpur, New Delhi, Seoul, Shanghai, Taipei, Tokyo), North America (Boston, Chicago, Los Angeles, Manhattan, Mexico City, New York, San Francisco, Washington DC), South America (Buenos Aires, Montevideo, Rio de Janeiro), and Africa (Cape Town, Casablanca, El Cairo, Rabat).  
Figure 9 shows the number of streets and junctions present in a 4 km\({}^{2}\) square area in these cities. In this work, each segment between two junctions is considered a street. As shown, the relationship between the number of streets and the number of junctions is almost linear, in an approximate ratio of 2 streets per junction.  
Results shown in Figure 7 suggest that three different performance profiles can be identified. According to this, we used the well-known _k-means_ clustering algorithm [42] with a number of clusters \(k=3\) to obtain a precise classification of the cities. By using the results of the clustering process in Figure 9, we can classify a new city according to the cluster whose centroid is the nearest (using the Euclidean distance as a measure). We can classify existing cities by their street profiles into:  
* _Simple layouts_: maps with low density of streets and junctions. Usually arranged orthogonally like a Manhattan style grid. Examples of these cities are New York (USA), Moscow (Russia), Los Angeles (USA), and Seoul (South Korea).
* _Regular layouts_: maps with medium density of streets and junctions. Some cities in this group are San Francisco (USA), Madrid (Spain), Washington DC (USA) and Paris (France).
* _Complex layouts_: maps with high density of streets and junctions. Cities that belong to this group are Rome (Italy), London (UK), Valencia (Spain), and Tokyo (Japan).  
Table 11 summarizes the classification process of the studied cities and shows the location of the centroid of the cluster assigned to each profile.  
Previous results (in Section 5) showed that the roadmap that serves as scenario for the warning dissemination has a considerable influence on the effectiveness of the process. Moreover, we can differentiate three groups of roadmap profiles in which the propagation process is likely to behave in a similar way. Thus, we consider that researchers must carefully determine the scenarios to assess their proposals since the obtained results will be directly affected by the roadmap used. In particular, we recommend to test with at least one map for each roadmap profile to make sure that results are representative and conclusions sufficiently generic.
## 3 Materials and Methods
### Genetic Encoding
#### 3.2.1 Chromosome for the Two Technical Indicators  
We selected two of the most widely used technical indicators: the Moving Average of the Convergence-Divergence indicator (MACD) and the Stochastic Oscillator indicator (SOI). Given a time series \(Y(t)=\{P(t_{0}),P(t_{1}),\cdots,P(t_{l-1})\}\) of prices \(P(t_{i})\), MACD is obtained in a plot with two lines given by Equations (1) and (2):  
\[MACD(t)=EMA(a,Y(t))-EMA(b,Y(t)),\]\[SIGN(t)=EMA(c,MACD(t)), \tag{2}\]  
where \(EMA(a,Y(t))\) indicates the exponential moving average over time series \(Y(t)\) with period \(a\). Therefore, three parameters are necessary to define the MACD indicator: a, b, and c.  
On the other hand, the SOI is a momentum indicator that uses support and resistance levels. The term _stochastic_ refers to the point of a current price about its price range over a previous period. It attempts to predict value turning points by comparing the closing price to its price range. Two levels based on the history of the closing values delimit the price range. Traditionally, the content is fixed between 20% (overbuy) and 80% (oversell) of the maximum value over a period. In this paper, dynamic bands adapt their position to the recent price behavior, replacing those levels with \(SOI_{a}\) and \(SOI_{b}\). In addition, we need another parameter to calculate the indicator, \(SOI_{c}\). This indicates the period to apply Equations (3) and (4). \(K\) generates an anticipatory signal, and \(D\) is a signal confirming the trend in the change.  
\[K=100\cdot\frac{Price-LowestPrice(SOI_{c})}{HighestPrice(SOI_{c})-LowestPrice (SOI_{c})} \tag{3}\]  
\[D=SMA_{3}(K), \tag{4}\]  
Figure 1: Overview of the investment process under both approaches, A and B. The Pareto front found on Day 1 is the initial population for optimization on the next day, and so on.  
where \(SMA_{3}\) is the 3-period simple moving average of \(K\), and Price is the last closing price. In summary, the first chromosome represents six parameters to be optimized: \(a,b,c,SOl_{a},SOl_{b}\), and \(SOl_{c}\).
# ResADM: A Transfer-Learning-Based Attack Detection Method for Cyber-Physical Systems
## 3 ResADM Architecture
### Transfer-Learning Module
#### 3.2.2 The Feature Selection Layer  
Effective feature selection could significantly reduce data dimensionality and enable the model to discover underlying patterns and structures in the data more efficiently, mitigating the impact of data complexity and noise. In this study, a feature-selection method based on feature importance was designed, leveraging the efficient graph-computing algorithm in the light gradient-boosting machine (LightGBM). The LightGBM employs a gradient-based optimization sampling algorithm to efficiently reduce the number of data instances and features for each feature. This process helped to narrow down the search space for split points [17]. The algorithm sorted all the data in descending order based on the absolute value of the gradient. Subsequently, a subset \(P\) was extracted by selecting the data with the maximum gradient, while a random subset \(Q\) was randomly chosen from the remaining data. The gain was then calculated from the subset \((P\cup Q)\), and the variance gain \(G_{f}(i)\) for feature \(f\) at split point \(i\) was defined according to Equations (2)-(4), where \(S\) represents the number of instances in the subset \((P\cup Q)\) and \(S_{l}(i)\) and \(S_{h}(i)\) represent the number of instances with feature \(f\) values less than or greater than \(i\), respectively. Additionally, \(g_{k}\) represents the negative gradient of instance \(X_{k}\), where \(P_{l}=\{X_{k}\in P:X_{kj}\leq v\}\), \(Q_{l}=\{X_{k}\in Q:X_{kj}\leq v\}\), \(P_{h}=\{X_{k}\in P:X_{kj}\leq v\}\), and \(Q_{h}=\{X_{k}\in Q:X_{kj}\leq v\}\).  
\[G_{f}(i)=\frac{1}{S}(G_{f1}(i)+G_{f2}(i)), \tag{2}\]  
\[G_{f1}(i)=\frac{1}{S_{l}(i)}(\sum_{X_{k}\in P_{l}}g_{k}+\frac{1-a}{b}\sum_{X_{ k}\in Q_{l}}g_{k})^{2}, \tag{3}\]  
\[G_{f2}(i)=\frac{1}{S_{h}(i)}(\sum_{X_{k}\in P_{h}}g_{k}+\frac{1-a}{b}\sum_{X_{ k}\in Q_{h}}g_{k})^{2}, \tag{4}\]  
The decision model in LightGBM was constructed based on the aforementioned steps for feature set \(F_{i}=\{f_{1},f_{2},...,f_{m}\}\), where \(i=\{1,2,3,...,m\}\). The feature importance score \(FIS_{i}\) was calculated by considering the number of times each feature had been used to split the training data across all decision models, as shown in Equation (5). In this formula, \(w_{i}\) represents the weight assigned to each feature, and \(f_{i}\) represents the feature set.  
\[FIS_{i}=\{s|s=w_{i}f_{i}\}, \tag{5}\]  
Based on the above approach, the feature importance of the attributes for different categories of CPS behaviors was calculated during the classification process. All attributeswere then sorted in descending order based on their importance. From the sorted attribute set, the top \(k\) attributes were selected, one by one, to create the feature value set. This feature value set was used as input for retraining the baseline LightGBM model. The value of \(k\) was gradually increased until the maximum classification accuracy was achieved after retraining. The top \(k\) attributes at this point were selected as the feature set for model training.
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 3 Results and Discussion  
### Test of Sulfonamide Degradation in Milkfish Pond Sediments  
The milkfish pond sediments were used to test the biodegradation of the three sulfonamides. Among the three sulfonamides, only SMX could be efficiently degraded. As shown in Figure 1, only SMX was degraded in the milkfish pond sediments under aerobic and anaerobic conditions at the end of a 40-day incubation period.  
The rate constants (\(k\)) and half-lives (\(t_{1/2}\)) of the three sulfonamides in the experiments shown in Figure 1 were estimated and listed in Tables S1 and S2. The remaining percentages of the three sulfonamides in the sterilized milkfish pond sediments under aerobic and anaerobic conditions were ranged from 97.8% to 99.2% at the end of incubation period of 40 days. These results reveal that aerobic and anaerobic SMX degradation were due to microbial actions. The interaction between sulfonamides and microbes may have been affected by the electronegative effects of functional groups of sulfonamides that may have hindered degradation [13]. Therefore, SMX is easier to be degraded than SMZ and SDM in milkfish pond sediments. A concentration of 2 mg L\({}^{-1}\) SMX was used for the following experiments.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
###### Abstract
Vitamin D skin production will therefore be influenced by different factors such as latitude, seasonality, clothing, pollution and the use of UV filters [12; 13]. In summer, 15 min of full-body sunlight at midday is equivalent to 250 \(\upmu\)g of cholecalciferol [14]. However, many factors influence the effectiveness of this combination (Figure 1). Figure 1: Pathway of Vitamin Production. The vitamin D production pathway involves the conversion of 7-dehydrocholesterol in the skin to vitamin D3 via UV-B. Vitamin D3 (and D2 from food) is hydroxylated in the liver to 25-hydroxyvitamin D3 and then in the kidneys to 1,25-dihydroxyrovyvitamin D3, the biologically active form. Compared to synthesis at the skin level, the intake of Vitamin D from foods is lower. The main food source of Vitamin D is foods of animal origin (cholesterolerol), but mushrooms (ergocalciferol) are also an important resource [15]. Vitamin D is a fat-soluble molecule, and therefore for its absorption it requires bile salts, micelles, and chylomicrons for its transport. Oily fish, eggs, and liver also have a high content of Vitamin D. Over the years, many populations have included foods fortified with Vitamin D (ergocalciferol or cholecalciferol) in their diet, mainly milk (cow or vegetable), butter, margarine, and breakfast cereals. Therefore, for those who suffer from Vitamin D deficiency due to insufficient sun exposure or if skin synthesis is reduced (e.g., in the elderly), a supplement should be prescribed [16]. To date, Vitamin D supplementation is considered an effective treatment to produce better health outcomes. Many clinical studies regarding Vitamin D supplementation show beneficial effects on health, but some of them have not shown any positive effects on different types of pathologies [4]. These differences also depend on the methods and times of administration. In fact, many researchers underline the importance of a very long follow-up especially for chronic diseases; the method of administration is also important: some authors prefer intermittent bolus administration and others daily administration (Vitamin D oral intermittent treatment (DO IT) study, a randomized clinical trial with individual loading regimen). Recent data shows that intermittent bolus administration (which is the highest) should be avoided as it can cause harmful health effects [17]. Finally, the doses of administration also play a decisive role: high doses (i.e., ~1000-2000 IU/day) should be used compared to too low doses (i.e., ~400-800 IU/day) in order to observe significant results. Despite these considerations, Vitamin D supplementation has been shown to reduce the risk of lung disease, cancer, type 2 diabetes, and autoimmune diseases [18; 19; 20; 21]. Additionally, individuals with cardiovascular disease may benefit from Vitamin D supplementation, a finding that warrants further investigation [22; 23].
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 6 Performance Comparison
### Security Requirements Comparisons  
In this section, we have compared our proposed scheme with the existing scheme based on security requirements that are unforgeability against FGR\({}_{1}\)(UF1), unforgeability against FGR\({}_{2}\)(UF2), sender anonymity (SA) at the time of request generation for the partial private key, receiver anonymity (RA) at the time of request generation for the partial private key, and removal of the concept of the secure channel during distribution of the partial private key (PPK). Therefore, we used YES if the scheme satisfies the security requirement and NO if otherwise. Based on Table 2, we can conclude that the proposed scheme satisfies all of the above security claims. In contrast, existing schemes such as [PERSON] et al. [30], [PERSON] and [PERSON] [19], [PERSON] and [PERSON] [28], and [PERSON] et al. [29] do not meet the SA, RA, and PPK requirements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **UF1** & **UF2** & **SA** & **RA** & **PPK** \\ \hline [PERSON] et al. [30] & YES & YES & NO & NO & NO \\ \hline [PERSON] and [PERSON] [28] & YES & YES & NO & NO & NO \\ \hline [PERSON] et al. [29] & YES & YES & NO & NO & NO \\ \hline [PERSON] and [PERSON] [19] & YES & YES & NO & NO & NO \\ \hline Proposed & YES & YES & YES & YES & YES \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security attributes comparison.
## 5 Simulation Results
### The Results and Discussion
#### 5.2.1 Comparative Analysis at \(C\) = 20
This method was developed based on the signed test for paired observations, and is more effective than the traditional test with positive and negative signs alone. The observed data are generally considered to be significantly different when \(p\) is less than 5%. When \(p\) is greater than or equal to 5%, the difference in the data is considered insignificant. The results of the Wilcoxon paired signed test for this paper are given in Table 6, assuming that there is no difference between each pair of algorithms (\(\alpha=0.05\)). \(R+\) in the table represents positive differences and \(R-\) represents negative differences. In the results for each pair of algorithms, the sum of \(R+\), \(R-\) and bonding values is equal to \(n\) in Table 5. It can also be seen in Table 6 that the \(p\)-values in the last column are equal to 0 and all are less than 0.001, indicating that the differences between the algorithms compared are statistically significant with respect to 0. In other words, there is a significant difference between IHBA and the other algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms for Comparison** & **R+** & **R\(-\)** & **Bonding Value** & \(p\)**-Value** \\ \hline IHBA VS CMA & 756 & 3168 & 126 & 0.00 \\ IHBA VS BBO & 2341 & 274 & 1435 & 0.00 \\ IHBA VS EDA & 3840 & 100 & 110 & 0.00 \\ IHBA VS TDIWO & 1791 & 817 & 1442 & 0.00 \\ IHBA VS GA & 3113 & 783 & 154 & 0.00 \\ IHBA VS PSO & 2881 & 829 & 341 & 0.00 \\ IHBA VS BA & 2873 & 907 & 270 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The Wilcoxon paired signed test result at \(C=20\) and \(\alpha=0.05\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & _Rank_ & \(n\) & _Mean_ & _Std. Deviation_ & _Min_ & _Max_ \\ \hline
**CMA** & 4.81 & 4050.00 & 1.17 & 0.92 & 0.00 & 7.05 \\
**BBO** & 4.92 & 4050.00 & 1.47 & 1.30 & 0.00 & 7.70 \\
**EDA** & 7.01 & 4050.00 & 16.89 & 12.80 & 0.00 & 50.37 \\
**TDIWO** & 3.36 & 4050.00 & 0.99 & 0.96 & 0.00 & 4.82 \\
**GA** & 3.99 & 4050.00 & 1.43 & 1.36 & 0.00 & 4.61 \\
**PSO** & 3.69 & 4050.00 & 1.31 & 1.25 & 0.00 & 5.35 \\
**BA** & 3.38 & 4050.00 & 1.00 & 0.95 & 0.00 & 4.47 \\
**IHBA** & 2.60 & 4050.00 & 0.74 & 0.71 & 0.00 & 5.51 \\
**_p-value_** & 0.00 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Descriptive Statistics Achieved by the Friedman Test at C=20 and \(\alpha\)=0.05. The bold figures indicate the comparison between algorithms. Finally, in order to make the observations more visual and relevant, this section analyzes and interprets the results of the ANOVA analysis for the eight algorithms. The methods used are the mean plot and the minimum difference method interval, and Figure 1 shows the plotted plots with 95% confidence level. It can be very clearly seen that the best performance is IHBA, which beats the other comparison algorithms by a more significant margin. The bat algorithm also has some advantages, while GA and PSO continue to perform consistently, and the EDA algorithm has the worst performance, followed by BBO.
## Materials and Methods  
### Materials  
N-butyl-N-methyl pyrrolinium bis(trifluoromethylsulfonyl)imide ([Pyrr\({}_{14}\)][Tf\({}_{2}\)N], 99.9 %, \(M_{w}=422.41\) g mol\({}^{-1}\)), poly(diallyIdimethyl ammonium) bis(trifluoromethylsulfonyl)imide (P[DADMA][Tf\({}_{2}\)N], 99.9 %, \(\overline{M}_{n}=275000\) g mol\({}^{-1}\)) and zinc bis(trifluoromethylsulfonyl)imide (Zn[Tf\({}_{2}\)N]\({}_{2}\), 99.5 %, \(M_{w}=625.68\) g mol\({}^{-1}\)) were provided by SOLVIONIC under the trade names Pyr0408a, Amsf5408a and M3008c, respectively. All chemicals were used as received, handled concerning the hazard and precautionary statements (warning): H315, H319, H335, P280, P305+ P351+ P338, P310.  
Polypropylene/polyethylene (PP/PE) non-woven fabric Novatexx(r) 2483 was received from Freudenberg (Germany), and Matrimid(r) 9725 was kindly provided by Huntsman (Switzerland). P-xylylenediamine (XDA, \(>98\) %) cross-linker was acquired from Fluka. Polydimethylsiloxane (PDMS, two-component RTV615 silicone rubber compound kit) was purchased from Momentive Performance Materials (Leverkusen, Germany). Methanol (Acros, 99.8 %), N-methylpyrrolidone (NMP, Acros, 99 %), tetrahydrofuran (THF, Acros, 99.5 %), acetone (Merck, 99.8 %), n-hexane (Merck, 99 %), ethanol (Fisher Scientific, 99.5 %) and isopropanol (VWR, 99.5 %) were all used without further purification.  
Carbon dioxide (CO\({}_{2}\), Messer, 99.995 %), nitrogen (N\({}_{2}\), Air Products, 99.9999 %) and helium (He, Air Products, 99.9999 %) were used for gravimetric gas sorption measurements as received. The gases used in the time-lag tests (nitrogen, oxygen, methane, helium, hydrogen and carbon dioxide, all 99.99+%) were supplied by Sapio, Italy.  
Figure 1: Chemical structures of PIL/IL/Zn\({}^{2+}\) composite constituents: (**a**) poly-(diallyIdimethyl ammonium) bis(trifluoromethylsulfonyl)imide (P[DADMA][Tf\({}_{2}\)N]); (**b**) N-butyl-N-methyl pyrrolinium bis(trifluoromethylsulfonyl)imide ([Pyrr\({}_{14}\)][Tf\({}_{2}\)N]) and (**c**) zinc di-bis(trifluoromethylsulfonyl)imide (Zn[Tf\({}_{2}\)N]\({}_{2}\)).
## 3 Risks of Extended Ground Times
### Social and Environmental Impact of Extended Ground Times  
Delay propagation and avoidable costs are not the only risks of extended GTs. Social and environmental factors also constitute risks. The aviation industry is a service industry that is committed to deliver a safe and high-quality product to their customers. Extended GTs lack attractivity of the passenger product, harm the connectivity of flights, and enlarge transfer times of passengers. Waiting times inside the aircraft negatively affect passenger satisfaction and business reputation [19]. Furthermore, aviation GH operations drive climate change. The surface movements of aircraft contribute to overall airport emissions. Aircraft ground operations produce a large share of NO\({}_{x}\) and CO\({}_{2}\) emissions [30]. After the start-up, pilots cannot turn off the engines. Thus, unnecessary idle times increase the local air pollution and the carbon footprint of airports. Every minute the aircraft engines of an A320 run without thrust provision emits up to 30 kg CO\({}_{2}\). For comparison, the climate-compatible annual CO\({}_{2}\) budget of one person is only 1.5 tonnes [31]. The International Civil Aviation Organization encourages aviation stakeholders to commit to the Paris Agreement 1.5 \({}^{\circ}\)C temperature goal. This is especially important to mitigate risks arising from climate change, such as hot temperatures that may threaten daily operations and lead to economic droughts [1]. Since propulsion technologies are not yet sophisticated enough to meet the European goals of reduced emissions for 2050, the reduction in waiting times of aircraft on the ground is even more important for environmental reasons [7].