esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, many studies are now addressing greenhouse gas reductions based on life cycle assessment (LCA) [11; 12]. However, the development of marine engines is progressing more slowly than expected, making it difficult to switch to carbon-neutral fuels. Therefore, this study presented and compared methodologies for setting operating conditions to minimize CO\({}_{2}\) emissions for each ship on actual routes in order to present operating conditions for economic operation based on bunker A and diesel as commonly used fuels. Many studies on pollutants emitted from vessels use AIS data because it is difficult to obtain operational data from ship engines like engine rpm, power, fuel consumption, etc. [PERSON] et al. acquired operational data from AIS for ships operating in local Chinese waters to derive a power-based CO\({}_{2}\) emission factor and build an emission inventory [13]. [PERSON] et al. installed an energy efficiency management system (EEMS) on operating ships and used a PEMS to measure various pollutant emissions and derive a power-based CO\({}_{2}\) emission factor [14]. Most studies focus on power-based emission factors. For standardized vehicles such as cars and heavy equipment, using power-based emission factors is effective for compiling an overall emission inventory. However, for most ships, excluding those used in some studies, obtaining engine data to derive power-based emission factors is difficult. Thus, a fuel-based emission factor is considered more efficient. Unlike previous studies, this study did not use a ship's data acquisition system but instead installed sensors on ships to measure fuel flow and used a PEMS to measure CO\({}_{2}\) emissions, thereby deriving a fuel-based emission factor. This study characterizes CO\({}_{2}\) emissions from vessels in operation and presents a methodology for deriving fuel-based emission factors. In addition, we presented a method for setting optimal operating conditions to minimize CO\({}_{2}\) emissions. Based on this study,the results can be used as basic research data to present a CO2 emission map from vessels in a particular region or worldwide and examine ways to minimize CO2 emissions from vessels. The structure of this paper is as follows: Section 2 describes the specifications of the vessel, engine, PEMS, and equipment used in the experiments in detail and presents a schematic of the emission measurement methodology. In Section 3, a correlation between the engine parameters and CO2 emissions is identified, and the results of CO2 emissions from ships operating at different engine rpm values are analyzed. The fuel-based CO2 emissions of each ship according to these experimental results are calculated and compared with the results of other studies, and a method for setting optimal operating conditions for ships is presented. Section 4 presents the conclusions of this study and mentions directions and goals for further research.
# Fine-Grained Mapping Between Daily Activity Features in Smart Homes
## 4 Results and Evaluation
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Approach** & \begin{tabular}{c} **HH109-** \\ **HH114** \\ \end{tabular}  & \begin{tabular}{c} **HH114-** \\ **HH109** \\ \end{tabular}  & \begin{tabular}{c} **HH109-** \\ **HH123** \\ \end{tabular}  & \begin{tabular}{c} **HH123-** \\ **HH109** \\ \end{tabular}  & \begin{tabular}{c} **HH114-** \\ **HH123-** \\ **HH123** \\ \end{tabular}  & \ \begin{tabular}{c} **HH123-** \\ **HH114** \\ \end{tabular}  & \begin{tabular}{c} **HH123-** \\ **HH114** \\ \end{tabular}  \\ \hline \multirow{3}{*}{BN} & C & 75.0\% & 71.3\% & 83.4\% & 74.6\% & 83.4\% & 78.7\% \\ \cline{2-8}  & CF & 31.1\% & 47.5\% & 46.1\% & 43.2\% & 61.6\% & 57.3\% \\ \cline{2-8}  & PA & **77.8\%** & **74.8\%** & **87.0\%** & **79.7\%** & 87.2\% & **81  
The average accuracy achieved by PA increased by 4.26% (using BN), 4.75% (using NB), and 2.97% (using RF) compared to CF. Compared to C, the increase was 13.88% (using BN), 14.93% (using NB), and 4.32% (using RF). BN achieved the best average precision and accuracy in most of the comparisons. For Group B: The average precision achieved by PA increased by 3.59% (using BN), 1.85% (using NB), and 2.58% (using RF) compared to CF. When compared to C, the increase was 33.52% (using BN), 31.73% (using NB), and 34.95% (using RF). The average accuracy achieved by PA increased by 4.05% (using BN), 2.01% (using NB), and 3.14% (using RF) compared to CF. Compared to C, the increase was 32.79% (using BN), 29.73% (using NB), and 28.27% (using RF). Again, BN achieved the best average precision and accuracy in most comparisons. Each smart home contains n sensors (averaging around 80). Each sensor has approximately m successor sensors (averaging around 3). The process iterates approximately k times (3 times), so the average time complexity is O(mnk). The space complexity for storing sensor relationships is O(n\({}^{2}\)), while the space complexity for storing the PageRank value of each sensor is O(n). The proposed approach (PA) consistently outperforms both the category-based (C) and category-and-function-area-based (CF) mapping approaches. Additionally, the performance is significantly enhanced when using BN as the classifier across both groups, achieving the best average precision and accuracy.
## 2 Literature Review
### The Digital Twin Applications in the Product Lifecycle  
The digital twin system has been widely used in product design and production because it can help with understanding customer demands quickly, identifying or even foreseeing model weaknesses early, controlling production processes to quickly adapt to changing environmental conditions, and providing useful recommendations to optimize plant operation and maintenance before failure occurs [48; 49]. In other words, the foundation for lining up the real world with virtual components is real-time data received from physical items. Automatic issue detection and performance evaluation allow for the formulation of optimized remedies that can be implemented in time to reap the rewards of increased dependability and efficiency [38]. A digital twin-based framework designed for the petrochemical industrial IoT and machine learning was presented to visualize the data exchange loop between the physical and visual model and production control optimization [48]. A digital twin-driven optimization method was presented to simulate a two-stroke heavy fuel engine, optimize its controlling parameters, and monitor its performance and manufacturing in real-time in a virtual environment [30; 50]. A digital twin-driven intelligent predictive maintenance model was proposed to evaluate and monitor the degradation process and predict the remaining life of an aeroengine by employing deep learning [29]. Deep cyber-physical integration of intelligent manufacturing is now being pursued to increase production management flexibility, adaptability, and predictability [38].  
Using smart inspection and component life prediction through digital twins benefits construction tunnel projects by reducing construction costs and labor efforts [51]. In another study, the prefabricated components of a tunnel were investigated to ease component life prediction by estimating displacement based on fitted data [33]. For unique circumstances, such as a fire breaking out in a tunnel, a digital twin-based robot system has been proposed to identify and track the location and status of the fire [33]. A real-time monitoring application for a TBM's interaction with the surrounding rock was developed to monitor cutterhead vibrations and shield jamming warnings [52]. However, there is still hesitation to apply DTs in the TBM tunneling industry, and there is still a gap in the literature regarding the visualization, monitoring, and performance prediction of TBM tunneling in a single platform.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation
### Analysis Scenario Evaluation of TCP/UDP/RUDP Scenario  
The end-to-end delay in the TCP scenario showed that the delay time had increased when the packet loss situation occurred, whereas the UDP scenario showed that the delay time had remained constant. However, the packet loss was compensated to a certain degree and its delay time remained in the value range between TCP and UDP. As a result, the delay times were UDP > R UDP > TCP, starting from the lowest value.  
Meanwhile, the end-to-end delay of each protocol is shown in Figures 11 and 12, respectively. There were some packet-loss situations in the UDP scenario but none for the TCP scenario as the TCP model had adopted a reliable re-transmission algorithm which activated in the event of any packet losses. The UDP model did not have such a function embedded in its algorithm so that it could not ensure 100% packet delivery. The R UDP model in terms of packet delivery performance remained between the other two models. Although the number of re-transmissions times was set at 3 in the scenario to improve the success rate of delivery, the larger number would have produced a better result, even to a 100% success rate.  
The performance evaluations also revealed that the TCP model was reliable but slower whereas the UDP model was faster but lacked the reliability in packet delivery. This may be due to the characteristics of TCP which require separate connection management for each link between server and client. By contrast, the R UDP model mainly deals with the communications between clients through the server to ensure communication reliability but this process also reduces the speed as well, showing a lower speed than the speed achieved by the UDP models. Another disadvantage of the R UDP model is that the server could be overloaded when there are too many clients. In any case, R UDP is well suited for communication systems that require a steady performance in terms of both speed and reliability. Indeed, each protocol has its own merits but as this study focuses on their performances in network games, an R UDP-based communication algorithm/system is recommended.  
Figure 10: Results of R UDP performance evaluation scenario.  
Below, Table 2 shows the total accumulated number of received packets. TCP demonstrates a 100% success rate while R UDP and UDP demonstrate approximately 99.8% and 98.5%, respectively. Comparing the success rates based on the simulation results, R UDP had a lower success rate than TCP but higher than that of UDP. This demonstrates that R UDP is more adoptable than UDP in terms of reliability.  
Figure 11: End-to-end delay in each protocol model (1): \(X\)-axis: time, \(Y\)-axis: end-to-end delay.  
Figure 12: End-to-end delay in each protocol Model (2): \(X\)-axis: time, \(Y\)-axis: end-to-end delay.
## 5 Identification Experiments with Different Sensors in the Same Area
### Zy3-02 Tms  
Figure 7a is sourced from the TMS sensor on the ZY3-02 satellite, with a resolution of 5 m and a size of 429 \(\times\) 429 pixels. Due to the high spatial resolution, each pixel represents a smaller ground area, allowing for the capture of finer surface features. As shown in Figure 7c and Table 5, the results identified by EAEND model (10) demonstrate high values in OA, PA, and AA values and Kappa coefficient, but there are still some minor noise points on the right side. Even in smaller ice floes and broken ice areas, details are well-preserved, indicating that EAEND model (10) maintains details when processing high-resolution data. Figure 7d demonstrates that the SVM method effectively identifies the Arctic sea ice. Notably, the identification accuracy on the right side of the image surpasses that of EAEND model (10). However, the SVM method encounters difficulties in accurately segmenting fragmented ice on the left side, leading to blurred boundaries in the identified sea ice. The results using the Otsu thresholding method, as observed in the image, are marred by increased noise. This suggests that the identification process of this method is susceptible to noise interference. Figure 7f reveals the performance of the MD method in identifying the Arctic sea ice. Here, not only are the ice edges blurred to varying extents, but there is also notable misidentification. Non-ice regions with spectral similarities are incorrectly identified as sea ice, resulting in an overall unstable identification performance.
## 2 Materials and Methods
### Phase Change Model  
The Lee model is employed to account for evaporation-condensation. Evaporation occurs when the temperature of the liquid exceeds the boiling/saturation temperature at a given pressure. Condensation is the reverse process of evaporation, where the vapor converts back into liquid by virtue of heat removal. Thus, condensation takes place when the vapor temperature drops below the saturation temperature. The mass transfer from liquid to vapor and vapor to liquid during evaporation and condensation, respectively, is described by the following Lee model:\[S_{m}=\left\{\begin{array}{l}\gamma_{l}\alpha_{l}\wp_{l}\frac{T-T_{sat}}{T_{sat}},T \geq T_{sat}\ for\ evaporation\\ \\ \gamma_{\sigma}\alpha_{v}\wp_{v}\frac{T-T_{sat}}{T_{sat}},T<T_{sat}\ for\ condensation\\ \end{array}\right. \tag{7}\]  
where \(T_{sat}\) is the saturation temperature corresponding to pressure, and \(\gamma\) is the adjustable mass transfer factor.  
The mass transfer coefficient \(\gamma\) in the Lee phase-change model (unit: s\({}^{-1}\)) governs the rate of phase transition across the vapor-liquid interface. Reported values of \(\gamma\) in the literature vary widely, typically ranging from 0.01 to 10, depending on the specific application and flow conditions [20; 21]. [PERSON] et al. [22] investigated this parameter in the context of full-scale LNG storage tank simulations and found that varying \(\gamma\) had negligible influence on the computed evaporation rate. Based on these findings and consistent with established practice in related studies, a value of \(\gamma\) = 0.1 is adopted in the present work. This choice effectively maintains the interface temperature near saturation while ensuring numerical stability.  
The source term of the energy equation is identified as follows, based on the latent heat that is either released or absorbed at the phase transition:  
\[S_{h}=-S_{m}h_{lp} \tag{8}\]
###### Abstract  
The main purpose of this study was to research and develop maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) with partial module shading and sudden changes in solar irradiance. Modified cat swarm optimization (MCSO) was adopted to track the global maximum power point (GMPP) of the PVMA. Upon a sudden changes in solar irradiance or when certain modules in the PVMA were shaded, the maximum power point (MPP) of the PVMA will change accordingly, and multiple peak values may appear on the power-voltage (P-V) characteristic curve. Therefore, if the tracking pace is constant, the time required to track the MPP might extend, and under certain circumstances, the GMPP might not be tracked, as only the local maximum power point (LMPP) can be tracked. To prevent this problem, a maximum power point tracker based on MCSO is proposed in this paper in order to adjust the tracking pace along with the slope of the P-V characteristic curve and the inertia weight of the iteration formula. The initial voltage for tracking commencement was set to 0.8 times the voltage at the maximum power point of the PVMA under standard test conditions. Firstly, MATLAB 2022a was used to construct the four-series, three-parallel PVMA model under zero shading and partial shading. The feedback of PVMA voltage and current was obtained, where the GMPP was tracked with MCSO. From the simulation results, it was proven that, under different shading percentages and sudden changes in solar irradiance for partial modules in the PVMA, the MCSO proposed in this paper provided better tracking speed, dynamic response, and steady performance compared to the conventional CSO.  
modified cat swarm optimization; partial shading; photovoltaic module array; maximum power point tracking; global maximum power point; local maximum power point; tracking speed; dynamic response and steady performance
## 4 Results and Discussion
### Flight Characteristics of Falling Debris
#### 4.1.1 Falling Debris Translation
For instance, at a train speed of 300 km/h, the average velocity of falling debris in the \(X\) direction is 2.77 times greater than the corresponding value in the \(Z\) direction. Contrastingly, in the scenario of trains crossing, the flight velocity of falling debris in both the \(X\) and \(Z\) directions fluctuates around zero. At a train speed of 300 km/h, the average velocities in the \(X\) and \(Z\) directions are \(-1.36\times 10^{-3}\) and \(-0.11\times 10^{-4}\), respectively. Importantly, the flight velocity of falling debris under the trains-crossing conditions is notably lower than that under the single-train conditions. This discrepancy signifies that the flight distance of falling debris is much smaller in the trains-crossing scenario compared with the single-train scenario. To quantify this difference, consider the example of a train speed of 300 km/h; the average flight velocities of falling debris in the \(X\) and \(Z\) directions under the single-train conditions are 5.74 times and 25.02 times, respectively, the corresponding values under the trains-crossing conditions. This emphasizes the impact of the crossing-trains scenario on the flight velocity and, consequently, the distance traveled by the falling debris.
# Development of a Mouthwash Using Freeze-Drying Technique: An Optimization Study
## 4 Conclusions  
The current work describes the application of the QbD approach in the development of freeze-dried mouthwashes. To obtain an optimal formulation of freeze-dried mouthwash, both qualitative and quantitative factors that may impact the main characteristics of the final product were investigated. A full factorial design with three factors and two levels was conceived and eleven mouthwash samples were prepared and analyzed. The results revealed the positive influence of mannitol and methylcellulose to reinforce the structure of matrices and the positive effect of methylcellulose in preserving the integrity of the samples and avoiding material loss when manipulated. Finally, an optimal formula was generated and characterized showing the good predictive capacity of the statistical model. Although the freeze-dried mouthwash formulations with _Cetraria islandica_ extract were not yet tested for their in vitro or in vivo efficacy, data regarding freeze-drying process leads to optimistic assumptions. This study offers the methodology for the preparation and optimization of solid mouthwashes loaded with herbal extracts, which could contribute to the development of a whole new area of the oral care industry that addresses the needs of consumers looking for stable, eco-friendly, light weight, easily transportable products.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Experimental**} & \multirow{2}{*}{**Residual**} \\  & **Value(Mean \(\pm\) \(\sigma\))** & **Value(Mean \(\pm\) \(\sigma\))** & \\ \hline Fracturability (g) & 1826.85 & 1951 \(\pm\) 123 & \(+\)124.15 \\ Weight loss (\%) & 2.59 & 2.39 \(\pm\) 0.91 & \(-\)0.20 \\ Reconstitution time (s) & 74.71 & 80 \(\pm\) 6.56 & \(+\)5.29 \\ Viscosity (cP) & 9.84 & 8.42 \(\pm\) 0.28 & \(-\)1.42 \\ \hline \hline \end{tabular} \(\sigma\) = standard deviation.  
\end{table}
Table 6: Predictions for the optimal formula and experimental results.  
**Author Contributions:** Conceptualization, C.B., S.M.I. and A.P.; methodology, C.B., S.M.I, D.-R.A., A.P., I.F. and R.C.; software, C.B., S.M.I., D.-R.A., A.P. and I.F.; validation, C.B., S.M.I., A.P., I.F. and M.A.; formal analysis, C.B., S.M.I., D.-R.A., A.P. and I.F.; writing--original, D.-R.A., S.M.I., C.B., A.P. and R.C.; writing--review and editing, D.-R.A., S.M.I., C.B., A.P., I.F., M.L.M., F.L. and M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The author [PERSON] is an employee of MDPI, however, she does not work for the journal Applied Sciences at the time of submission and publication.
## 3 Results
### Remote Sensing in Disease Infestation on Sugar Beet Leaves  
TGI ranged from a few thousand to a few tens of thousands. The lowest value indicated dry leaves, while the highest value indicated leaves in excellent condition. By using the TGI index, all changes in the leaf or chlorophyll can be monitored. Since disease damages the leaf, the chlorophyll will be reduced, and the index can be used for detection because it has the ability to differentiate leaves with greater or lesser damage. Changes in the NIR and green reflectance spectrum change the active surface area formed by the NIR (NIR-infrared) red and green parts of the light spectrum. The damage to the sugar beet leaf was registered on 30 June 2020. The proportion of chlorophyll-free leaves increased. Figure 4 shows that damaged leaves and leaves with spots can be seen all over the plot. It is characteristic and easy to notice that a drastic difference is registered between the spot and the leaf (living and non-living part), which can be easily differentiated. The camera detected damaged and dry leaves. There was detected damage to the leaves; moreover, there are dry leaves, as well as the presence of young, smaller leaves. The second part of the observation was in August. In the first period, out of the total number of plants, 11.58% of plants had either leaf damage and/or dry leaves. Based on the imaging on from 22 August 2020, the situation was more severe. The presence of dry leaves and spots on the leaves ranged from 21.11% to 25.07%.  
Figure 4: TGI index of 30 June 2020 for sugar beet to observe the presence of disease (black pixels mark potential infested area).
## 3 In-Situ Monitoring
### Monitoring Results
#### 3.2.4 Settlement of Buildings around the Foundation Pit  
The excavation process of the foundation pit can cause significant soil deformation and ground settlement under long-term large drawdown conditions. This settlement can lead to cracks and deformations in the surrounding buildings, affecting their stability during subsequent excavation and precipitation construction. Therefore, it is particularly important to control the settlement of the buildings around the pit. Each observation point had completed buildings located about 30-50 m away.  
The variation curves of the buildings around the foundation pit at the monitoring points W1, W2, W3, W4, and W5, with the same time interval of observations, were recorded as AW1, AW2, AW3, AW4, and AW5 curves, respectively (see Figure 7). The observation time was consistent with Figure 4. The surrounding buildings outside observation point W3 were monitored every 10 days after the end of observation at other observation points.  
The settlement displacements of the surrounding buildings had evident differences at the beginning, gradually increased, and then entered a stable stage with smaller and smaller differences, as seen from Figure 7. This is attributed to the unloading caused by soil excavation and precipitation in the initial stage of construction, leading to a sharp increase in the settlement around the foundation pit. In the later construction stage, the horizontal  
Figure 6: Variation curves of vertical displacement of the top of the enclosure wall and the number of observations.  
Figure 7: Variation curves of settlement of surrounding buildings and the number of observations.  
cross bracing came into play, and the deformation around the foundation pit reached its maximum, after which the deformation entered the stable period.