esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussions
### Verification of Voltage Fluctuation Suppression Effect of Distribution Network  
Figure 8 shows the voltage changes of each node of network 3 at different time periods throughout the day before and after FMSS is connected. Figure 7(a) shows the situation when FMSS is not connected, and Figure 7(b) shows the situation when FMSS is connected. Where the \(X\)-axis represents the time, the \(Y\)-axis represents the node number, and the \(Z\)-axis represents the per-unit value of node voltage:  
The simulation results show that the voltage fluctuation of the distribution network has a gentle approach when FMSS switch is connected. It is shown that FMSS can increase the consumption capacity of the distribution network to DG by regulating the active power flow between the feeders and the reactive power output from ports. At the same time, the feeder load balance is achieved and the node voltage offset is reduced as a whole.  
When the capacity of FMSS is 3 MVA, the active and reactive power instructions of each port in the optimization process are shown in Figure 9 and the ordinate unit is MW/MVar. P1, P2 and P3 respectively represent the active output values of three ports of FMSS. Q1, Q2 and Q3 respectively represent the reactive output values of three ports of FMSS. Among them, the active and reactive power instruction values of each port for the first 4 times are shown in Table 2  
Figure 8: The node voltage variation of network 3 before and after access FMSS.  
Although seemingly complicated, the combination of the load variation and the DG output of the real time curve in Figure 7, indicates that a complex distribution network system with a varying load can optimize the DG output under this strategy by stable and as large as possible real-time instructions.  
Therefore, this algorithm has a good real-time performance. It can adjust the power of FMSS by tracking the change of the load in real time during the system operation to improve the consumption of DG. Meanwhile, FMSS can adjust the active power flow and reactive power flow. This is also the advantage compared to traditional flexible power devices, such as the static var compensator (SVC) which can only regulate the reactive power flow of the line.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### Current Research on Kitchen Fire Prevention
#### Video Frames and Process Images Technologies  
Traditional single-point heat and smoke detectors are widely used, and they are usually only responsible for detecting limited areas in space. In large rooms and high-rise buildings, smoke particles and heat can take a long time to be detected. In the detection of fires, we prefer to use video-based fire detectors because it can effectively perform large-scale room and high-rise building fire monitoring, and even perform outdoor fire detection needs [60; 61]. Currently most video fire detections use the visual characteristics of fire, including color, texture, geometry, flicker and motion. [55; 62] The gas detector can detect the sign of the disaster even before the fire occurs [56]. Several smoke detection studies have been published using cameras to detect smoke. Since the smoke is grey and translucent, the edges of the high frequency image frame lose clarity and become an indicator of smoke. The distinction between different types of smoke is through the examination of changes in background tones, smoke pixels, blurred backgrounds, and illumination segmentation [51; 63]. Due to the risks associated with the use of video frame detectors, this study used a combination of sensors to detect fires.
# Organic Electrochemical Transistor Microplate for Real-Time Cell Culture Monitoring
## 3 Results and Discussion
A period of 5 s was set in order to get the source current \(I_{\mathrm{s}}\) to a sufficiently steady state (Figure 4, medium time range) for the deduction of the time constant \(\tau\) of the gate circuit. The medium current response is almost not influenced by material relaxation. The \(\Delta I_{\mathrm{s}}\) response on the modulation represents a consequence controlled by charging and discharging the EDL capacitance at the electrolyte--PEDOT:PSS interface. At the moment of the gate pulse edge, a short source current \(I_{\mathrm{s}}\) spike appears with an exponential decay that is constant at about 3 ms. This is considered the effect of the hole extraction/injection process in the PEDOT:PSS channel, as was already described by [PERSON] and [PERSON] [1]. Figure 4: Response of output voltage of the preamplifier - \(I/V\) converter (calibrated in drain current units) on the gate input voltage at \(V_{\mathrm{ds}}=-0.735\) V and variable gate voltage \(V_{\mathrm{gs}}\) modulated by rectangular pulses of voltage 10 mV\({}_{\mathrm{pp}}\) and a 5 s period. The aging of the OECT structure under the voltage load described above was performed over a period of 28 h (see Figure S5). A gradual, approximately 25% decrease of \(I_{\mathrm{s}}\) and the transconductance \(g\) was recorded. The biocompatibility of the sensor was tested by 3T3 fibroblasts. These cells were able to grow within sensors to the same extent as in the control (standard cell culture plastics, not shown). The viability was comparable to the control and it typically reached 90-95%. This indicated that a high level of biocompatibility was reached. Indeed, cells were able to form a confluent layer within 48 h (Figure 5A). A proof-of-concept experiment for the sensor function was carried out. Due to extensive ion exchange, the cells growing at the transistor channel modulate the current within it; hence the removal of cells will result in changes to the channel current. Thus, the confluent layer of cells within the sensor was treated with trypsin, an approach to detach cells from their support [9]. Indeed, the absolute value of the current decreased in parallel with the trypsin-mediated detachment of cells (Figure 5B, see video S1). Finally the function of the sensor was verified by means of a spike of KCl (Lachema, Brno, Czech Republic), which resulted in a major dropdown of the absolute current value. The main idea of the final proof of concept experiment with cells was to demonstrate that the sensor can host the living cells efficiently and an electrogenic event related to cell physiology could be recorded.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Symmetric Topologies
#### 7.1.1 Scenario S1: ZigBee-Only, One-Hop Routes
As discussed in Section 5, the LQI-based procedure produced the lowest variation in all, with the median of \(\sigma_{3,2,0}^{(LQI)}\approx 0\); however, it was insensitive to the traffic load, as shown in Figure 4a: the median of \(\overline{\epsilon}_{3,2,0}^{(LQI)}\approx 2\) for all traffic loads. From Figure 4b, it is possible to see that, while all procedures showed \(\sigma_{3,2,0}\approx 0\) at low traffic loads, the U-RR procedure consistently produced cost estimates with less variation than the LS-based procedure as the traffic load increased above 2 packets/s. The better performance of the U-RR procedure in comparison with the LS-based procedure is justified by the U-RR use of both LS and unicast packets to estimate the probability of successful transmission in links. When the traffic load was low, the U-RR procedure relied mostly on the LS transmissions to estimate the link cost, as can be observed in (11). When the traffic load increased, the number of unicast transmissions became much higher than the number of LS transmissions and the U-RR procedure used these additional transmissions to reduce the variation of cost estimates. It is interesting to observe in Figure 4b that the median of \(\sigma_{3,2,0}^{(URR)}\) increased after 2 packets/s, reached a peak at 5 packets/s, reduced to 0 at 9 packets/s, and again increased after 9 packets/s. To understand this behavior, recall that estimated costs are rounded to the nearest integer. If the true cost is between two integers, the estimated cost would be alternating between the two integers, increasing the standard deviation of cost estimates. This rounding effect can be seen in Figure 4a,b: at 5 packets/s, the median \(\overline{\epsilon}_{3,2,0}^{(URR)}\approx 2.5\) and the median \(\sigma_{3,2,0}^{(URR)}\) increased; at 9 packets/s, the median \(\overline{\epsilon}_{3,2,0}^{(URR)}\approx 3\) and the median \(\sigma_{3,2,0}^{(URR)}\) was close to 0. Figure 4: The median and the 15 th to 85 th percentiles of (**a**): the cumulative route cost of route 3,2,0 (\(\overline{c}_{3,2,0}\)), (**b**): the standard deviation of the estimated costs measured at node 3 for the route 3,2,0 (\(\overline{c}_{3,2,0}\)), (**c**): the ratio of times that route 3,2,0 was chosen (\(\overline{r}_{3,2,0}\)), and (**d**): the number of times that node 3 had to retransmit for every 1000 messages for the symmetric scenario S1. Figure 4c shows how often route 3,2,0 was chosen by each procedure, illustrating that all procedures chose route 3,2,0 around 50% of the times. This behavior was expected since \(c_{3,1,0}=c_{3,2,0}\). Since route decisions can impact the probability of retransmissions, we also computed the number of times that node 3 had to retransmit a packet every 1000 messages generated above the network layer. Since \(c_{3,1,0}=c_{3,2,0}\) in this scenario, all of the procedures showed similar results, as illustrated in Figure 4d.
## 4 Reliability Prediction for New Prefabricated Track Structures
### Reliability Prediction  
According to the Key Supported Project of the National Natural Science Foundation of China and the High-Speed Railway Joint Fund, U1434204, Research Report on the Long-Term Behavior of Ballastless Track-Bridge System for High-Speed Rail [34], the initial reliability indicators for the CRTS II slab track structure are 5.890 for the track slab, 3.964 for the CA mortar layer, and 3.273 for the baseplate.  
The calculated value of failure probability \(P_{f}\) and the reliability indicator \(\beta\) satisfy [35]  
\[\mathbf{P_{f}}=\mathbf{\Phi}(-\beta) \tag{15}\]  
where \(\Phi(\cdot)\) represents the standard normal distribution function.  
The relationship between failure probability \(F(t)\) and reliability \(R(t)\) is [36]  
\[F(t)+R(t)=1 \tag{16}\]  
The reliability \(R_{s}(t)\) of a series system and the reliability \(R_{i}(t)\) of a unit satisfy [14]  
\[\mathbf{R_{s}}(t)=\prod_{i=1}^{n}R_{i}(t) \tag{17}\]The track slab, CA mortar layer, and baseplate of the CRTS II slab track structure are regarded as a series system. Based on Equations (15)-(17), the reliability of the CRTS II slab track structure is \(R_{0}=0.9994\). Based on the analysis in Section 3 of this paper, the overall criticality value of the CRTS II slab track structure is \(C_{0}=34.7464\) and the overall criticality value of the modular assembled track structure with built-in position retention is \(C_{1}=33.5803\). Based on Equation (14), the reliability of the modular assembled track structure with built-in position retention can be predicted to be \(R_{1}=0.9994\).
## 3 Proposed Approach
### Data Preprocessing
#### 3.2.4 Train-Test Dataset Split  
The division of the dataset into training and testing subsets plays a crucial role in achieving rigorous evaluation and ensuring model generalization. The training subset allows the model to learn intricate patterns and relationships within the data, while the testing subset, isolated from the training phase, serves as an unbiased benchmark for assessing performance on unseen instances. This approach minimizes overfitting and provides meaningful insights into the model's adaptability and effectiveness in binary and multi-class classification scenarios.  
(i) Binary Classification  
The NF-UNSW-NB15-v2 dataset used for binary classification was divided into training and testing sets, as detailed in Table 10. The Normal class included 85,680 samples, of which 81,320 were utilized for training and 4360 for testing. Similarly, the Attack class consisted of 37,457 samples, with 35,660 used for training and 1797 for testing. This distribution ensured that both classes were adequately represented in both the training and testing phases, facilitating effective model evaluation and generalization.  
(ii) Multi-Class Classification  
The NF-UNSW-NB15-v2 dataset, used for multi-class classification, includes ten classes, comprising the Benign class and nine attack types, such as Exploits, Fuzzers, Reconnaissance, Generic, DoS, Shellcode, Backdoor, Analysis, and Worms. Table 11 outlines the sample distribution across these classes, highlighting the class-wise split between training and testing sets. The largest class, Benign, comprises 81,200 training samples and 4310 testing samples. Exploits and Fuzzers follow with 14,190 and 9653 training samples, respectively. Smaller attack categories, such as Worms and Analysis, include 83 and 306 training samples, respectively, with a limited number of testing samples. This distribution ensures representation of both frequent and rare attack types, enabling comprehensive evaluation of the model's ability to detect diverse attacks effectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Type** & **Train** & **Test** \\ \hline Normal & 81,320 & 4360 \\ \hline Attack & 35,660 & 1797 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sample distribution of the NF-UNSW-NB15-v2 dataset in binary classification.
# Concentration in Saliva and Antibacterial Effect of Xylitol Chewing Gum: In Vivo and In Vitro Study
## 2 Materials and Methods
### Xylitol Concentration In Saliva
#### 2.2.2 Samples Collection and Measurements of the Concentration of Xylitol in Saliva  
The saliva flow rate was measured in the mid-morning at least one hour after each participant had taken any food. Subjects were asked to chew a piece of paraffin wax for 1 min and then spit into graduated tubes for 5 min. Thirty-two subjects showed a saliva flow \(\geq\) 1 mL/min and were finally enrolled.  
Xylitol concentration was determined through a spectrophotometer analysis (Agilent Technologies Cary 60 UV-Vis, using a wave-length of 492 nm) in saliva samples collected before and during chewing gum use at 0.30, 1.00, 2.00, 5.00, and 10.00 min (stimulated saliva sampling); then the chewing gums were discarded and further samples were collected at 15.00, 20.00, and 25.00 min from the start of chewing (unstimulated saliva sampling). A minimum of 0.5 mL of saliva was collected at each time point.  
The concentration of xylitol was determined by the oxidation of nicotinamide-adenine dinucleotide (NAD+) to D-xylulose in the presence of xylitol dehydrogenase (SDH) with the formation of reduced nicotinamide-adenine dinucleotide (NADH) [15]. Saliva samples were first de-proteinased adding to a 200 \(\mu\)L of saliva an equal volume of ice-cold 1 M perchloric acid and centrifuged at 1500 g for 10 min and the supernatant was then neutralized with 1 M KOH. The sample obtained was treated as follows: 200 \(\mu\)L of the sample was added to 10 \(\mu\)L of distilled water in a sterile 1.7-mL Eppendorf tube followed by 50 \(\mu\)L of buffer solution (pH 8.6). Then 20 \(\mu\)L of NADH iodonitrotetrazolium chloride and 0.2 \(\mu\)L of diaphorase were put into the solution. The reaction started with the addition of 0.5 \(\mu\)L of SDH. The absorbance of the solutions was read at the end of the reaction (approx. 15 min). Formazan crystals were dissolved by addition of 0.5 mL of lysing solution, and after a 3-h incubation at room temperature, 100 \(\mu\)L of the suspension was added to each sample that was measured by the increase in absorbance at 492 nm. The smallest differentiating absorbance for the assay is 0.005 absorbance units. All salivary Xylitol concentration data were expressed in \(\mu\)g/mL.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.3 Scale and Spatial Resolution  
Wildfire evacuations often involve large geographic areas with varying terrain and infrastructure complexities. Some software packages, particularly macroscale models, might not capture the fine-grained details needed to represent local evacuation dynamics accurately. Conversely, microscale models, while more detailed, may lack the computational capacity required to simulate large-scale evacuations. Finding the right balance between scale and spatial resolution is a significant challenge in wildfire evacuation modeling.
## 3 Cognitive Radio for Smart City
### Benefits of Cognitive Radio in Smart City  
CR has the potential to flexibly support a wide range of applications and can be useful to deal with several communications challenges in a smart city  
* Communication resource availability. CR improves spectrum utilization and communication capacity to support large-scale data transmissions. Indeed, the unlicensed spectrum (_i.e._, Industrial, Scientific, and Medical, ISM) mainly used in local area connections is becoming dramatically crowded and interfered, while other licensed frequency bands are fixedly assigned and utilized in an inefficient way. In addition, the application of CR can also alleviate the burden of purchasing licensed spectrum for utility providers. CR uses the existing spectrum through opportunistic access to the licensed bands without interfering with the licensed users. CR determines the spectrum portions unoccupied by the licensed users--known as spectrum holes or white spaces--and allocates the best available channels for communicating.
* Heterogeneity. Heterogeneous communication technologies have to be integrated to provide reliable and efficient access to the system elements in different environments. As a consequence, devices should be able to acquire context awareness and to reconfigure themselves. Hardware reconfigurability can help to manage communications in areas where different technologies are present.
* Quality of Service. Communications over white spaces can provide dedicated low-latency communications for critical data.
* Energy consumption. CR can be used to reduce power consumption, and hence to have energy efficient systems, by sensing the environment and then adaptively adjusting the transmission power, avoiding energy waste.
## 3 Results and Discussion
### SEM Analysis  
SEM microscopy has been used to get a detailed characterization of carbon nanotubes in polymer-based nanocomposites [53; 54]. In the present work, a series of SEM micrographs applying different magnifications were made to investigate the GNPs and MWCNTs dispersion state in the composites. Two selected SEM cross-section images of monofiler nanocomposites 9 wt% MWCNT/PLA (Figure 10a) and 6 wt% GNP/PLA (Figure 10b) disclosed a high degree of structure porosity and good uniform insertion of carbon nanofillers within the PLA matrix.  
Figure 9: Tensile test results presenting Young’s modulus vs. carbon nanofiller content for MWCNT/PLA and GNP/PLA composites.
## 3 Results, Discussion and Outlook
### Categories of Research Studies
#### 3.2.3 Methods of Training and Techniques
They emphasize the importance of equipping long-distance mariners with the necessary knowledge and skills, particularly in the use of digital systems, and highlight the need for future training of seafarers to meet the evolving challenges and incorporate new skills such as communication and multi-ship management. Adaptability is needed in the development of maritime education, with a focus on lifelong learning strategies for seamless transitions from shipboard to shore-based work. The emergence of unmanned ship management requires the development of an environment conducive to this new activity [98]. This requires the use of advanced simulation tools and technologies such as virtual and augmented reality to fully prepare naval professionals for the different levels of autonomous ship management. The world merchant fleet is constantly growing, and with it, the need for competent crews, whether on board or ashore. This growth is expected to continue. The shipping workforce of the future is likely to require seamanship and new skills, including human-autonomous collaboration, maintenance and AI mediation [99]. This month, the maritime industry is presenting a new container ocean-going ship with IMO Level 3 autonomy [100]. In addition to the maritime industry, the United States Navy is also making an important contribution to the development of autonomous ships. As one of the driving forces behind this development, the United States Navy analyzes the research and progress of autonomous ships several times a year. The Navy's programs to develop and procure unmanned surface vehicles (USVs) of various sizes include programs to develop two large USVs--the large unmanned surface vehicle (LUSV) and the medium unmanned surface vehicle (MUSV) [101]. USV ships sail across the Pacific, leave California and sail to Hawaii as part of an extensive test program for the Navy's future USV fleet [102]. As the United States Navy spearheads the advancement of autonomous vessels, its ongoing evaluation and investment underscore a pivotal moment in maritime innovation.
## 3 Mismatch Error Estimation and Calibration Method  
In response to the shortcomings of the current offline LS calibration method, this paper proposes a joint error estimation method that is first coarsely estimated offline by the LS method and then accurately estimated online by the RLS method. This method reduces the amount of data storage while avoiding a large number of complex field calculations compared to other traditional methods, thus greatly reducing the demand of the calibration method on system computing power and storage capacity, which improves the efficiency of calibration and allows real-time calibration online.