esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Encapsulation Efficiency Analysis  
Firstly, the surface oil content (SOC) was determined by the extraction of 2 g of powder with 15 mL of n-hexane for 2 min at room temperature. Then, the solvent was filtered, and the residue was rinsed three times with 25 mL of n-hexane. The solvent was evaporated using a rotary evaporator (Buchi Labortechnik AG, Flawil, Switzerland). The extracted oil was weighed, and the SOC was expressed as a percentage of the powder. The encapsulation efficiency (EE) was calculated based on Equation (1), where the total oil content (\(TOC\)) is the amount of oil in the anhydrous fraction of prepared emulsion (40%) calculated based on the emulsion formulation (Table 1).  
\[EE\ (\%)=\frac{(TOC-SOC)}{TOC}\cdot 100 \tag{1}\]
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Surrogate Modeling for Space Design Exploration
#### 5.3.3 Construction of Surrogate Models and Prediction of Target Variable at Verification Points  
This work has selected three surrogate models: Polynomial Response Surface (PRS), Radial Basis Function (RBF), and Kriging model. These have already been described in Section 3.3. These three different surrogate models \(s\) were constructed for each of the variables defined in Section 5.1.2, seeking to minimize the error between the metamodeled variable \(\hat{Z}_{m,tra}=g_{s,m}(X_{tra})\) and the actual values \(Z_{m,tra}\) at the training dataset \(X_{tra}\).  
An example is presented applying the surrogate Polynomial Response Surface model (\(s=PRS\)) for the multi-fidelity intermediate variable \"condensation difference\" (\(m=\Delta\)) This model determines the coefficients \(\beta\) of Equation (4) that minimize the error of comparing \(\hat{\Delta}\) (the metamodel of \(\Delta\)) against \(\Delta\) itself at the set of training points. A \(\hat{\Delta}\) value of 0.89 kg/h is predicted for case 24 among the training data when applying the surrogate model. Comparing this value with the actual \(\Delta\) for such a working point (0.96 kg/h), an error of 7% appears between the observed and predicted values.
# Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review
###### Abstract  
The remote monitoring of maritime signaling facilities is one of the marine navigation safety rules essential for ensuring global maritime traffic. Some maritime signaling facilities have not yet implemented remote monitoring systems. This challenge is posed by factors such as insufficient signal range, limited availability of electrical energy, or various economic reasons. Therefore, this paper reviews the current and relevant scientific literature on 10 communication technologies for maritime signaling facilities in the last two decades using PRISMA guidelines. PRISMA 2020 represents guidelines for conducting systematic review papers using mixed methods, including their applicability to various reviews. In addition, this paper analyzes the selection of the best-suited communication technology for communication between maritime signaling facilities. The results show that, initially, 214 papers met the specified criteria, and after applying the filtering, it was narrowed to 29 relevant papers for the research topic. Surprisingly, almost half of them were found in databases other than WOS, SCOPUS, and GOOCLE SCHOLAR. Also, LoRa WAN is the most energy-efficient and cost-effective option, with a consumption rate 2.14 times lower than AIS and NB-IoT. To summarize, it has been found that LoRa WAN represents the optimal communication technology for transmitting data from maritime signaling facilities across long distances.  
marine aids to navigation; communication technologies; remote monitoring; navigation safety  
2025153452  
2025153452  
2025153452
## 5 Conclusions  
Our research uses an ensemble model based on stacked long short-term memory, which combines the LSTM with attention and gradient boost regression models to predict the cycle life of lithium-ion batteries. The model hyperparameters are obtained by using the DE algorithm. The performance of the proposed model is compared with a single model using single and multiple cells for training. The first experiment used data from seven cells to verify the performance of the proposed model, where 70% of the data was used for model training, and the remaining data were used for model verification. The second experiment used one cell or two cells as the training set, and other cells to verify the model's predictive ability. In most cases, the comparison results verify that the proposed model is superior to the single model in predicting the capacity decline trend. In the first experiment, the maximum APE value for predicting cycle life is 0.4504. In the second experiment, the average APE values of GBR, LSTM with attention, and the proposed model are 28.6580, 1.7813, and 1.5789, respectively. These results show that the proposed model has a better cycle life prediction performance than other models. In addition, the prediction variance of the model can be obtained using the MC dropout technique, which can provide prediction uncertainty. From the analysis results, we conclude that the proposed model can provide more accurate and reliable prediction results; however, the calculation time required for the ensemble model is longer than that of the single model. In the future, the capacity degradation and cycle life prediction of other ensemble learning models in different types of lithium-ion batteries are worth investigating. In addition, the number of single models used in ensemble models can be increased to more than five. In Table 5, we found that two cells as training data provide better prediction accuracy than one cell; however, this result comes from a small experiment. Further large-scale experimental data analysis is worthy of studying the results of transfer learning. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cells**} & \multirow{2}{*}{**ACL**} & \multicolumn{2}{c}{**GBR Model**} & \multicolumn{2}{c}{**LSTM with Attention**} & \multicolumn{2}{c}{**Ensemble Learning**} & \multicolumn{2}{c}{**Proposed Model**} \\ \cline{3-10}  & & **PCL** & **APE** & **PCL** & **APE** & **PCL** & **APE** & **PCL** & **APE** \\ \hline
2\_5 & 444 & 471 & 6.0811 & 453 & 2.0270 & 471 & 6.0811 & 446 & 0.4504 \\
2\_6 & 480 & 507 & 5.6250 & 484 & 0.8333 & 507 & 5.6250 & 479 & 0.2083 \\
2\_11 & 561 & 596 & 6.2389 & 565 & 0.7130 & 589 & 4.9911 & 561 & 0.0000 \\
2\_12 & 477 & 505 & 5.8700 & 474 & 0.6289 & 492 & 3.1447 & 475 & 0.4192 \\
2\_14 & 483 & 520 & 7.6605 & 486 & 0.6211 & 505 & 4.5549 & 481 & 0.4141 \\
2\_18 & 494 & 525 & 6.2753 & 490 & 0.8097 & 509 & 3.0364 & 492 & 0.4049 \\
2\_19 & 487 & 523 & 7.3922 & 497 & 2.0534 & 521 & 6.9815 & 486 & 0.2053 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The cycle life prediction results of different models using 70% of the total data as training data.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 5 Experiment and Result Analysis
### Performance Analysis of SDADM  
#### 5.2.1 Generation Duration of Transition Space  
In the process of path generation, to solve the problem of the high complexity of SMT constraint solving, this paper proposes a method based on DFS to solve the path between two points. This section analyzes the superiority of the method designed in this paper by comparing the time of generating path transition space under different network topologies. This section selects 6 different complexity network topologies from the article on the topology zoo (2018). The relevant data are shown in Table 3. The corresponding experimental results are shown in Figure 6. As can be seen from the figure, when the network topology is relatively simple, the time of the two methods is basically similar. However, as the complexity of the topology increases, the solution time of SMT grows exponentially; for the algorithm of SDADM designed in this paper, although also gradually increasing, the increase is relatively stable, and the gap between the two is getting bigger and bigger. Moreover, the SDADM algorithm only needs to calculate once at the beginning  
Figure 5: Comparison of effectiveness in resisting link flooding attacks.  
of communication when generating the path transition space, while the SMT constraint solving algorithm, due to changes in factors such as path bandwidth and path similarity, needs to recalculate based on the constraint conditions each time the transition is made. Therefore, considering comprehensively, SDADM has more advantages.
## 3 Experimental Results  
### Overall Pump Performances  
According to the similarity criterion, the external characteristic curve of the high-speed centrifugal pumps is dimensionless. The dimensionless flow rate formula and head formula are defined:  
\[\varphi=\frac{Q}{\pi d_{2}b_{2}u_{2}} \tag{13}\]  
\[\psi=\frac{gH}{u_{2}^{2}/2} \tag{14}\]  
where \(\varphi\)--flow rate coefficient, \(\psi\)--head coefficient, \(Q\)--flow rate, \(H\)--head, \(d_{2}\)--outlet diameter of the impeller, \(b_{2}\)--outlet width of the impeller, \(u_{2}\)--exit circumferential velocity of the impeller. The dimensionless pump performance curves at different rotational speeds are shown in Figure 8. \(\varphi_{\text{r}}\) represents the flow rate coefficient at \(Q_{\text{r}}\). It can be seen that the dimensionless head and efficiency performance curves at different rotational speeds have high similarity, which means the internal flow in the pump also follows a similar law. However, the dimensionless head curve decreases faster at a large flow rate with the increase of rotating speed. These trends can be easily explained by the 1D Euler theory and the general knowledge of the flow phenomena taking place in the impeller under off-design operating conditions. The fluid inside the pump flows faster at higher rotating speed, which leads to more head loss. The high-efficiency area of the efficiency curve moves towards the direction of small flow, and the maximum value of efficiency decreases continuously with the decrease of rotational speed. This is because the operating point of the pump is also determined by the resistance of the circulation system, and the operation condition of the pump at low rotational speed offset to small flowrate (relative to the condition with maximum efficiency). Pumps produce more flow disturbances in the flow field under non-designed conditions. The internal flow is similar and that volume loss is less when the pump is working at a high rotational speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density/(kg/m\({}^{3}\))** & **Young’s Modulus/GPa** & **Poisson Ratio** \\ \hline PPS & 1500 & 11.95 & 0.4 \\ ADC & 2700 & 71 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of the structure domain.
## 5 Results
### Topology 2
#### 5.2.2 Scenario 8  
In this scenario, the delay and packet loss were the same as in Scenario 7, but the connection between h29 and h27 had interruptions every other 15 seconds. Hence, we expected the SNN algorithms that selected h29 from h26 to prefer h30 at h29 whenever there was a connection interruption. static was configured to use the left path h26->h29->h27.  
The average response times achieved with the routing methods in this scenario are depicted in Figure 16a. We can see that the average response times increased with all the routing methods compared to Scenario 7 due to the connection interruptions between h29->h27. snnr showed 12-69%, 8-52%, and 17-80% better average response times than snndp, snnd, and snnp. snnr and snndp showed 36-78% and 20-65% better performance than static. snndp showed 8-67% better performance than snnp, but performed worse than snnd.  
Figure 16b shows that the packet loss percentage achieved by snnr was 2-76% and 1-125% worse than snndp and snnd. snnp showed 2-140% better performance than snnr for bundle rates of 1-8 bundles/s, whereas snnr was 18-25% better than snnp at bundle rates of 8-10 bundles/s. Both snnr and snndp were 45% better than static. snndp showed 12-14% better performance than snnd at 1-2 bundles/s, but performed worse at the higher bundle rates, whereas it performed worse at the lower bundle rates than snnp and 20% better at 8-10 bundles/s.  
The packet loss percentage was better than in the previous scenario due to the duplicate transmissions during connection interruptions. The DTN caused the bundles to be stored during connection interruptions and forwarded when the connection was restored.  
Figure 16c shows that the throughput with snnr was 1-25% worse than snndp and snnd. snndp showed up to 33% better throughput at bundle rates of 1-8 bundles/s, and snnr showed 12-21% better throughput at higher bundle rates. snndp showed 1-20% better throughput than static, and snnr showed 10-18% better throughput only at 9-10 bundles/s. snndp showed 2-23% better throughput than snnp at 7-10 bundles/s.  
All SNN methods showed better average response times and packet-loss percentages than the static method because of utilizing the alternate links during connection interruptions.  
Figure 16: Routing performance: Scenario 8. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.  
Figure 15: Routing performance: Scenario 7. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 3 Analysis of IoT Traffic
### Specific Use of Protocols  
Different from PCs, IoT devices show specific characteristics through the use of protocols. For example, we find that SmartThings Hub repeatedly queries the domain name _ntp01.alro.com_ through the DNS protocol and performs time synchronization via the NTP protocol. NetGear Station queries the DNS server, mainly for domain name _gm.iotplatform_, every 300 seconds and sends ten consecutive ping packets every 320 seconds. Moreover, it also broadcasts MDNS packets every 60 seconds, which are usually used for device identification in a local area network (LAN).
## Information Entropy  
### Information Entropy Fundamentals  
Information theory began in 1948 with [PERSON]'s [37] proposed information entropy as a measure of disorder or uncertainty in a message calculated based on the following equation:  
[PERSON]S=-K\underset{i=1}{\overset{n}{\sum}}p(x_{i})\log(p(x_{i})) \tag{1}\]  
where \(S\) is the information entropy, \(K\) is a constant that dictates the entropy units, and \(p(x)\) is a probability mass function, where \(p(x_{i})\)is the probability of a certain value, [PERSON]x_{i}\), present within the message with \(n\) possible values. It should also be noted that the probabilities need to sum to 1. In addition, let \(K=1/\log(2)\) so that the logarithm will have a base of 2 to yield entropy in units of bits [37; 38].  
To better understand the information entropy of a signal, it is helpful to see examples of calculating information entropy from various probability distributions. Consider the probability distribution of flipping a fair coin. A person flipping this coin has little information and is uncertain about whether the outcome will be heads or tails. Let heads be outcome \(x_{1}\), tails be outcome \(x_{2}\), and the probabilities for each outcome be \(p(x_{1})=0.5\) and \(p(x_{2})=0.5\). From this probability distribution, the information entropy can be calculated based on Equation (1) and is found to be 1.00 bit. This distribution and calculation is depicted in Figure 1a.  
Now consider a biased coin that results in tails 70% of the time. A person flipping the coin now has more information and is less uncertain about the possible outcome. A new probability distribution can be constructed to reflect the biased coin, and the entropy of this distribution is less than before, at 0.88 bits as recorded in Figure 1b. Finally, consider a biased coin that is tails 99% of the time when flipped. Knowing this bias, a person flipping the coin would be almost certain that the outcome will be tails. This probability distribution is represented in Figure 1c and results in an even lower entropy value of 0.08 bits. This scenario demonstrates that a more uniform probability distribution representing  
Figure 1: Probability mass functions (PMF) of various weighted coins and the associated calculated entropy: (**a**) PMF of flipping a fair coin and calculated entropy; (**b**) PMF of a biased coin with probability of flipping tails as 0.70 and calculated entropy; and (**c**) PMF of a biased coin with probability of flipping tails as 0 and calculated entropy.  
a highly disordered and more uncertain variable will have a higher information entropy value. In other words, the greater the uncertainty and disorder, the greater the entropy will be.  
Given any variable or signal that is represented by a probability distribution, one can calculate information entropy using Equation (1) as a measure of the variable's disorder or uncertainty. The maximum entropy will be from a distribution with equally likely outcomes, whereas the minimum entropy of 0 bits will be from a deterministic distribution when only one outcome from a sample space is possible.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 6 Adaptive Data Polling for Teri-Mac  
The data polling mechanism is one fundamental problem for the receiver-initiated MAC protocols since the receiver that starts the communication has little information on the transmission requests of senders. More specifically, the data polling problem includes two issues, one is when to poll data, the other is how much data to poll. In this section, with the assistance of link layer traffic estimation, we discuss and propose an adaptive data polling algorithm to solve these two issues. Based on the historical feedback of the packet number on the sender side, the receiver can estimate the traffic from different senders. Traffic estimation can help the receiver to poll data in time and to avoid over-requesting.
## 2 Results and Discussion
### The In Vivo Efficacy of P13#1  
Encouraged by the results obtained in vitro, we sought to assess the antimicrobial efficacy of P13#1 in vivo in an air pouch model, a mouse model of subcutaneous infection  
Figure 8: Effects of P13#1, (P)GKY20 and colistin on the release of NO and on the secretion of IL-6 and TNFa by Raw 264.7 murine macrophage cells stimulated with LPSs from _P. aeruginosa_ 10. The experiments were performed in triplicate, and statistical analyses were carried out by using GraphPad Prism. The data presented are the mean values of three experiments \(\pm\) S.Ds. The analysis was carried out using Student’s \(t\)-test (**\(p<0.01\), ***\(p<0.001\) and ***\(p<0.0001\)) versus untreated cells and the LPS group.  
in which bacteria are inoculated inside an air pouch obtained by injecting air into the back of a mouse [72]. The air pouch model allows one to mimic a localized infection that can then be treated via the topical administration of antimicrobial agents. As a model pathogen, we choose _S. aureus_ as it is a common skin inhabitant and an opportunistic pathogen that frequently causes wound-related infections. In particular, _S. aureus_ Newman was selected as it is a clinical strain isolated from a human infection [55] and its virulence in animal models is well known [73]. Mice were infected with this pathogen five days after the induction of the air pouch, and a single dose of the chosen antimicrobial, P13#1 (0.5 mM), ampicillin (12.6 mM) or gentamycin (0.46 mM), was injected into the air pouch 2 h after the infection. The mice were sacrificed after 48 h, and the bacterial load was determined via the CFU counts in washes of the air pouches. As shown in Figure 9, P13#1 protected the mice from _S. aureus_ infection, reducing the CFU counts in the air pouch washes by almost 50-fold when compared to the negative control mice (pVal = 0.01).  
Interestingly, similar levels of protection were also observed with two well-known antibiotics, ampicillin, and gentamycin, underlying once more the potential of this compound as antimicrobial agent. The in vivo anti-inflammatory activity of P13#1 was then assessed by measuring the concentration of pro-inflammatory cytokines at the site of infection. As reported in Figure 10, the treatment with P13#1 contributed to significantly reducing the concentrations of several pro-inflammatory cytokines in the skin of the infected mice, and the results were comparable with those obtained when using the two antibiotics.  
Figure 9: Fold reductions in the number of Colony Forming Units (CFUs) counted in air pouch washes with respect to the median value of the CFU counts of the negative control. Mice were inoculated with the _S. aureus_ Newman strain injected into the pouch and then treated with PBS as a negative control (CTL-), P13 #1, ampicillin (Amp) or gentamycin (Gen), which were administered directly into the pouch. Each dot shows data from a single animal, the red line represents the median value and the data are from three independent experiments. The Mann–Whitney U-test was used to assess significance.
# A Study of Current Controlled Discharge in a Nitrogen Filled Tube
## 2 Materials and Methods  
The basis of our experimental setup is in Figure 1. We studied the discharge in an alumina tube with inner diameter 9.2 mm and length 10 cm, filled with nitrogen at a pressure \(P=(30,70,100,130,300)\) Pa. From both sides, the tube is terminated by electrodes sealed by 1 mm thick BK7 optical windows. Gas is filled through a hole in the grounded electrode. The discharge current with the amplitude slightly less than 500 A is formed by a discrete Pulse Forming Line and switched by a t\({}_{1}\) spark gap to the load. The tube is placed in one arm of Mach-Zehnder interferometer illuminated by 800 nm, 60 fs pulses from Ti:Sapphire laser. The fringe pattern produced by interferometer is recorded by a CCD camera and measured phase shift allows us to deduce the radial profile of electron density at the moment of passage of fs laser beam (see Appendix A). The diagnostic femtosecond laser is fired with a selected delay after the start of the current pulse. Discharge current is measured using a pulse current transformer placed around grounding cable. Voltage between electrodes is measured using two Tektronix P6015A probes with common floating grounds. The voltage on the tube's high voltage electrode before gas breakdown is 40 kV. After gas breakdown, it drops to several hundreds volts. In order to get better voltage measurement resolution during discharge, we set the oscilloscope to low volts per division settings. To protect the oscilloscope from excessive voltage at this setting, we limit the voltage on the P6015A probe input using 10 k\(\Omega\) resistor connected to the HV electrode in series with 800 V transil connected to the ground.  
The tube dimensions choice \((0.92\times 10)\) cm was limited by the design of the last (main) pulse driver unit, which was build in our laboratory, and is not discussed in this paper. Filling pressure range \(P=(30,70,100,130,300)\) Pa was chosen around pressure \(P=(70\div 130)\) Pa, where we expect optimal condition for our three stage laser system by preliminary computer simulations, plus two outlaying values \(P=(30\,\text{and}\,300)\) Pa. According to our preliminary simulations, the pre-ionization discharge current of amplitude \(I=500\) A and length of several \(\upmu\)s is sufficient to set suitable pre-ionization condition (as discussed in Section 1) and was extended to \(10\,\upmu\)s in order to study pre-ionization plasma evolution in longer timescale.