esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Trusted Network Secure Routing Algorithm
### Residual Energy  
In a wireless sensor network, the power of some nodes on a path may be greatly reduced because of excessive use of that path, and these nodes will then fail because of their low residual energy. Therefore, the routing algorithm must consider the remaining energy levels of the nodes. In this paper, the energy consumptions of the nodes are calculated using the wireless communication model presented in [25]. Two communication models are considered in this paper: a free-space model and a multi-path attenuation model. When the communication distance is greater than \(d_{0}\), the multi-path attenuation model is adopted; otherwise, the free-space model is adopted. The initial energy of a node is denoted by \(E_{i}\), and the residual energy of the node at time \(t\) is denoted by \(E_{re}\); the residual energy can be obtained using the following equations:  
\[E_{re}=E_{i}-E_{c}(t) \tag{12}\]  
\[E_{c}(t)=E_{tx}+E_{rx} \tag{13}\]  
\[E_{tx}=\left\{\begin{array}{l}L*E_{de}+L*\varepsilon_{fs}*d^{2},d<d_{0}\\ L*E_{ele}+L*\varepsilon_{mp}*d^{4},d\geq d_{0}\end{array}\right. \tag{14}\]  
\[E_{rx}=L*E_{ele} \tag{15}\]  
In these formulas, \(E_{tx}\) is the total transmission energy consumption of the sender, \(E_{rx}\) is the total energy consumption of the receiver, \(E_{de}\) represents the energy consumed for the transmission and reception of data over a wireless transmission line, and \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the energy consumption parameters for free-space and multi-path communication, respectively. \(L\) is the size of the transmitted data, and \(d\) is the distance between the sender and receiver.
# Improvement Effect of Membrane-Free Stem Cell Extract on
## 3 Results
### Serum Levels of IgE and Inflammatory Cytokines in HDM-Sensitized NC/Nga Mice  
To investigate the effect of MFSCE on systemic allergic response, serum from the cardiac blood of all mice in each group was collected at 4 weeks, which is the day of dissection, and serum IgE (Figure 4) and inflammatory cytokines (Figure 5) were measured. Serum IgE levels in the VEH group (277.59 \(\pm\) 149.61 ng/mL) were considerably higher (approximately six times) than those in the NOR group (47.18 \(\pm\) 27.88 ng/mL). This result indicated that HDM sensitization induced AD-like allergic responses in NC/Nga mice; therefore, the AD murine model in the present study was appropriate to verify the effect of MFSCE on AD. In addition, serum IgE in MFSCE-treated groups (MFSCE5, 150.18 \(\pm\) 55.84 ng/mL; MFSCE10, 101.71 \(\pm\) 39.39 ng/mL; MFSCE20, 134.07 \(\pm\) 16.86 ng/mL) was decreased, similar to the serum IgE level of DEX group (177.54 \(\pm\) 39.41 ng/mL).  
Figure 4: Effect of MFSCE on serum IgE increase in HDM-sensitized NC/Nga mice. Values are mean \(\pm\) SD. \({}^{\text{a}-\text{c}}\) Means with different letters are significantly different (\(p<0.05\)) as determined by [PERSON]’s multiple range test. The mice were grouped and treated as described in Figure 2.  
Figure 3: Effect of MFSCE on AD-like symptoms in HDM-sensitized NC/Nga mice. (**A**) Dermatitis scores. (**B**) Scratching frequency. Values are mean \(\pm\) SD. \({}^{\text{a}-\text{d}}\) Means with different letters are significantly different (\(p<0.05\)) as determined by [PERSON]’s multiple range test. The mice were grouped and treated as described in Figure 2.  
As shown in Figure 5 (A, IL-4; B, IL-10; C, IFN-\(\gamma\); D, TNF-\(\alpha\)), the secretion of inflammatory cytokines, including IL-4 (NOR, 32.93 \(\pm\) 3.32 pg/mL vs. VEH, 41.28 \(\pm\) 5.27 pg/mL), IL-10 (NOR, 3.10 \(\pm\) 0.89 pg/mL vs. VEH, 6.77 \(\pm\) 1.89 pg/mL), IFN-\(\gamma\) (NOR, 19.16 \(\pm\) 9.80 pg/mL vs. VEH, 75.97 \(\pm\) 604 pg/mL), and TNF-\(\alpha\) (NOR, 92.26 \(\pm\) 5.57 pg/mL vs. VEH, 112.64 \(\pm\) 16.40 pg/mL), was stimulated by repeated HDM sensitization; however, MFSCE prevented the production of IL-4 (MFSCE, 32.01 \(\pm\) 1.46 pg/mL; MFSCE10, 35.18 \(\pm\) 2.85 pg/mL; MFSCE20, 30.11 \(\pm\) 2.90 pg/mL), IL-10 (MFSCE, 4.67 \(\pm\) 1.12 pg/mL; MFSCE10, 4.50 \(\pm\) 1.25 pg/mL; MFSCE20, 134.07 \(\pm\) 16.86 pg/mL), IFN-\(\gamma\) (MFSCE, 56.46 \(\pm\) 8.13 pg/mL; MFSCE10, 31.29 \(\pm\) 1271 pg/mL; MFSCE20, 18.26 \(\pm\) 9.44 pg/mL), and TNF-\(\alpha\) (MFSCE, 150.18 \(\pm\) 55.84 pg/mL; MFSCE10, 3.96 \(\pm\) 0.48 pg/mL; MFSCE20, 134.07 \(\pm\) 16.86 pg/mL). These data suggest that MFSCE application reduced AD-like allergic responses in HDM-sensitized NC/Nga mice.
# Transcriptome Landscape Variation in the Genus _Thymus_
## 2 Material and Methods
### Identification of Orthologous Genes and Comparative Phylogenetic Analysis  
As an initial scenario, OrthoFinder was used [34] with the default parameters to identify orthogroups among predicted protein sequences obtained from Transdecoder v2.0.1 for the five assemblies. We also broadened the sampling by including _Salvia splendens_[35] and _Mentha spicata_ (Bioproject ID PRJNA359989) as outgroups. Orthogroups with only single-copy genes were retained for further analysis. The nucleotide sequences for each group were collected into a FASTA file and were multiply aligned using MUSCLE v3.8.31 [36]. Output alignment files were further applied to filter out the poorly aligned regions by the trimAl v1.4 [37] with the parameter \"-gt 0.8 -st 0.001\". For each alignment sequence, RAxML v8.2.11 [38] was used to reconstruct the maximum likelihood (ML) tree under the GTRGAMMA model with 100 bootstrap replicates. All gene trees were fed into ASTRAL-II [39] to infer the species phylogeny under a multi-species coalescent model. In addition to ML analysis, a Bayesian methodology using BEAST v.2.5.2 [40] was applied as an alternative method to infer the taxa relationship and estimate divergence time based on all single-copy genes. To generate input files for BEAST, the BEAUti interface was used in an HKY model with four \(\gamma\) categories, a strict molecular clock, and a calibrated Yule process of speciation with two calibration points: Mentha-Thymus at 50 MYA (M = 3.88, L = 0.25), and Salvia-Thymus at 30.6 MYA (M = 3.443, L = 0.15). The calibration dates were obtained from TimeTree ([[http://timetree.org](http://timetree.org)]([http://timetree.org](http://timetree.org))). The chain length was set to 10 million. The trees were then interpreted by the program TreeAnnotator v1.6.1 prior to visualization in the program FigTree v1.4.0 [41].
## 3 Results and Discussion
### Hydrochemical Characteristics  
The chemistry of the groundwater samples in 2013, 2016, and 2019 are listed in Table 1. These values indicate that the dominant cation was sodium during the three years and that the dominant anion was chloride, giving water and drinks a salty taste [39].  
The relative order of abundance of the major cations Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\) with an average of 38%, 13%, 48%, and 1% of all cations, respectively, for HCO\({}_{3}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{-}\) anions account for an average of 15%, 49%, and 36% of the total anions, respectively (for the year 2013). The measurements established in 2016 show an abundance of Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\) cations of 30%, 11%, 57%, and 2% of the total cations, respectively, and HCO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\) anions account for an average of 30%, 43%, and 27% of the total anions, respectively. Nonetheless, the chemical composition measured in 2019 shows an abundance of Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\) cations with an average of 28%, 14%, 56%, and 2% of the total cations, respectively, and HCO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{-}\) anions represent an average of 27%, 38%, and 35% of all anions, respectively. Cation abundance shows a decrease in calcium concentrations over time between 2013 and 2019, with higher levels detected at well P6 in 2013 and increasing over time. Regarding magnesium, sodium, and potassium, higher concentrations were found in wells P2, P8, and P1 in 2019. These higher concentrations can be related to water-rock interaction processes, such as the dissolution of carbonate and evaporative minerals and cation exchange. High levels of potassium may be due to the excessive use of potassium-based chemical fertilizers and soil leaching after fertilizer use.  
Analyses of anions show a decrease in chloride concentration over time with a higher concentration in 2013 at well P4. Indeed, an increase in bicarbonate and sulfate levels was detected over time in P14 in 2019. These ion concentrations are mainly controlled by natural and/or anthropogenic mineralization processes related to water-rock interactions, human activities, and climate change.
## 6 Performance Evaluation
### Performance Metrics
#### 6.2.3 Resource Utilization  
Resource utilization denotes the number of resources required during task execution. Resource utilization \(R_{u}\) is expressed as  
\[R_{u}=\frac{T_{c}}{M_{a}\times N} \tag{12}\]  
where \(T_{c}\) represents the time taken to complete a task, \(M_{a}\) represents makespan and \(N\) represents number of resources.
## 3 Results and Discussion  
### Concentration of PM\({}_{10}\) in the Atmosphere  
The annual average concentration of PM\({}_{10}\) at the analyzed stations was 19.29 \(\pm\) 3.01 \(\upmu\)g/m\({}^{3}\). The lowest value was recorded at station 1 with a concentration of 16.09 \(\pm\) 3.09 \(\upmu\)g/m\({}^{3}\), while the highest value was observed at station 6, with a concentration of 25.02 \(\pm\) 4.99 \(\upmu\)g/m\({}^{3}\) (Figure 2). The highest daily PM\({}_{10}\) concentration was recorded in February at station 6, reaching 104 \(\upmu\)g/m\({}^{3}\). According to the EU Air Quality Directive (2008/50/EC) [20], the annual limit for PM\({}_{10}\) is 40 \(\upmu\)g/m\({}^{3}\), and the daily limit is 50 \(\upmu\)g/m\({}^{3}\), which cannot be exceeded more than 35 times per year. However, the WHO recommends an annual limit for PM\({}_{10}\) of 15 \(\upmu\)g/m\({}^{3}\) and a daily limit of 45 \(\upmu\)g/m\({}^{3}\). Station 1 is located in Osieczow, a small town with a lower degree of urbanization and industrialization, which results in lower levels of particulate pollution. In contrast, station 6 is located in Katowice, a city situated in the highly urbanized and industrialized region of Upper Silesia. In Katowice, the main sources of PM\({}_{10}\) emissions are the combustion of fossil fuels in industry, energy production, and emissions from transport, which contribute to higher pollution levels. As of 31 December 2024, 7410 mining plants, including 7276 open-pit mines, were subject to supervision and control by mining offices in Poland (Figure S1, Supplementary Materials).  
Figure 2: PM\({}_{10}\) dust concentration at individual measuring stations.  
It was noted that precipitation had no effect on the value of PM\({}_{10}\) at individual stations (Tables S2-S9, Supplementary Materials). Similarly, wind speed did not affect the value of these parameters. No statistically significant differences were found between the cold and warm seasons (\(p<0.05\)), which is also supported by the results of [PERSON] et al. (2022) [7]. Similar values for PM\({}_{10}\) were recorded in Spain, in the La Mancha region, where the concentrations ranged from 4.3 to 44.7 \(\mu\)g/m\({}^{3}\) ([PERSON] et al., 2022) [7]. The highest average PM\({}_{10}\) concentration was recorded in autumn (22 \(\mu\)g/m\({}^{3}\)), followed by summer (20 \(\mu\)g/m\({}^{3}\)), spring (17 \(\mu\)g/m\({}^{3}\)), and winter (13 \(\mu\)g/m\({}^{3}\)). In other urban areas of Spain, such as Zaragoza, slightly higher average concentrations were observed, reaching 34.4 \(\mu\)g/m\({}^{3}\)[21], and in A Cornua, the concentration was 24 \(\mu\)g/m\({}^{3}\)[22].
# Contrails and Their Dependence on Meteorological Situations
## 2 Materials and Methods  
In order to clarify some of the uncertainties about contrails and induced cloud cover, research on contrails is being conducted at the Czech Technical University in Prague (CTU) using a camera system based on collecting video of contrails and matching selected available Automatic Dependent Surveillance-Broadcast (ADS-B) data from the transponder Mode S messages of the aircraft that produced the contrails. These data can be correlated with atmospheric condition data from aerological measurements or with data from aircraft transponder Comm-B Data Selector (BDS) registers, if available [15]. Experience has shown that so far, very few aircraft (units of one percent) support the transmission of meteorological registers; moreover, aircraft are not commonly equipped with humidity sensors. Therefore, aerological data are essentially the only way to associate data on the air mass in the vicinity of the contrail with the captured contrail. The frequency of contrails in the airspace of the Czech Republic was investigated using a database consisting of video recordings, ADS-B reports, and meteorological data from aerological measurements. Furthermore, the conditions of contrail formation and their lifetime are evaluated in comparison with meteorological parameters. The processed data were used for the classification and prediction of contrails in a given area during the year.  
Two research questions were formulated to investigate the relationships between contrails and meteorological conditions. The first question is about a statistically significant dependence between the properties of the contrail, characterized by selected parameters, and the observed meteorological variables. The second question is whether the characteristics of contrails can be estimated from meteorological data based on prediction models. For the research, contrail data were collected using video recordings in the vicinity of Decin and Prague, after which they were paired with relevant flight and aerological data. Advanced statistical methods were used to create classification models that can predict contrails.
## 6 Conclusions
Future research can focus on the following directions: optimizing the real-time performance of MSEChoA in dynamic environments to ensure stability and efficiency in rapidly changing obstacle and target scenarios; integrating deep learning or reinforcement learning methods to further enhance the algorithm's adaptability to high-dimensional and complex scenarios; and conducting deeper investigations into logistics and distribution applications, such as logistics scheduling and traffic information. Further research can explore and improve these issues, enabling MSEChoA to realize its potential in a wider range of practical scenarios, providing more efficient solutions for unmanned delivery, robotic navigation, intelligent logistics, and industrial optimization. Research conceptualization, X.H. Advice and guidance, X.H. Overall direction control, X.H. Investigation, X.H. Resources, X.H. Methodology, X.H. Study design, C.G. Software, C.G. Validation, C.G. Experimental execution, C.G. Data analysis, C.G. Visualization, C.G. Preparation of first draft, C.G. Project management, X.H. Review and revision of the manuscript, X.H. All authors have read and agreed to the published version of the manuscript. The authors thank the National Natural Science Foundation of China for partial support under Grant No. 72071153. The authors also express their gratitude to the Key Laboratory Fund under Grant no. 6142003190204, and the Natural Science Special Program of Xi'an University of Architecture and Technology under Grant no. ZR20048. Not applicable. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. The authors would like to thank their colleagues at Xi'an University of Architecture and Technology for their constructive discussions and feedback during the research. The authors also acknowledge the anonymous reviewers for their valuable suggestions that greatly improved the manuscript. The authors declare no conflicts of interest.
## 4 Key Challenges in the Integration of NTNs in IoRT
_Enhanced Throughput/Transmission Rate:_ As IoT data volumes continue to grow, NTNs need to handle high-speed data transmission to meet the demands of applications. Researchers investigate techniques to increase NTN throughput and support efficient data transfer. 5. _Enhanced Data Transmission Reliability:_ Reliable data transmission is paramount, especially for mission-critical IoT applications. NTN research addresses factors such as interference, fading, and signal attenuation to ensure data integrity and minimize packet loss. 6. _Enhanced and Timely Data Acquisition:_ IoT devices generate vast amounts of data at frequent intervals. Timely data acquisition is crucial for proper processing and analysis. NTN research explores methods to optimize data collection, reduce delays, and ensure timely data delivery, often leveraging advanced Medium Access Control (MAC) protocols to optimize channel access. 7. _Enhancement of Edge Computing Capabilities:_ Edge computing brings processing power closer to IoT devices, reducing latency and enabling real-time decision-making. By addressing these objectives, NTNs for IoRT are poised to revolutionize the way data are collected, processed, and utilized, enabling a wide range of applications in remote and isolated environments.
## 5 Optimization of LSTM for Fall Detection System
### Summary  
In Section 4, the training data accuracy of STM-LSTM-based fall detection system showed 100% for the single parameter \(P_{G}\) and the multiple parameter \(P_{ALL}\), and thus it may be overfitted in practice. To resolve the overfitting issue and find optimal values of the parameters used in LSTM, the validation data performance calculated from the validation data, using the network parameters obtained from the training data, was investigated with  
Figure 12: Validation data accuracy vs. regularization rate of the STM-LSTM-based fall detection system with respect to different number of hidden layer nodes.  
Figure 13: Highest validation data accuracy and computation time of the STM-LSTM-based fall detection system with respect to different number of hidden layer nodes.  
respect to normalization method, sampling, regularization rate, and hidden layer node, as follows:  
1. As some human activity patterns after the Min-Max or the Z-score normalizations are so similar to each other and are difficult to distinguish, the accuracy of distinguishing falls tends to decrease, and this leads to the conclusion that a normalization is not suitable for this fall detection.
2. When the sampling interval increases, the peak values of accuracy decrease due to overfitting by reduced input data. Therefore, larger sampling intervals require higher regularization to reduce overfitting.
3. As the number of hidden layer nodes increases, both accuracy and overfitting can increase due to the increase of network complexity. Therefore, the higher the hidden layer node and the higher the regularization rate, the higher the accuracy and the lower the overfitting, respectively.
4. In terms of higher accuracy and shorter computation time, the optimal values of the parameters of the LSTM and normalization method are found as follows: Non-normalization and no-sampling with 128 hidden layer nodes and regularization rate of 0.015. This is best accuracy of 98.21% with a relatively short computation time.
## 7 Conclusions and Future Issues  
This paper has presented methods of detecting stray nodes in scuba diving by adjusting transmission power and using multiple hops. Specifically, two methods have been proposed: The autonomous response-type protocol and the request-response-type protocol. Since multiple terminals communicate via a single channel, the paper has also proposed a method of reducing channel contention by using the application layer to stagger transmission timing. To evaluate the proposed methods, an undersea environment has been modeled in a simulator, and propagation attenuation, noise and propagation delay have been implemented in the simulator. The relationship between the transmission output and the packet arrival rate has been investigated. Using the findings from this investigation as evaluation conditions, the proposed collision avoidance methods have been compared with CSMA/CA in terms of the packet loss rate and the stray node detection rate. It has been shown that the proposed collision avoidance using the application layer is more effective than using CSMA/CA only. It has also been shown that transmission of messages in multiple hops is effective.  
Looking forward, we will try to prove that the proposed stray node detection protocols are useful in a more realistic environment that takes the battery levels of terminals into consideration and will evaluate the protocols using more varied parameter values to represent a wide variety of undersea environment. In the present research, the application layer is used to stagger message transmission attempts in order to avoid contention. We will investigate causes for packet loss and relations between them in detail to achieve efficient communication. For the same purpose, we will devise a network routing protocol that reduces message flooding. We will build a mobile ad-hoc network and study the effectiveness of routing-based contention avoidance.  
[PERSON] designed the proposed method, developed the software prototype of the evaluation system, collected the evaluation data and wrote the initial draft of the paper. [PERSON] provided the direction for his research activities and refined the proposed method, the analysis of the evaluation results and the writing of the paper.  
The authors declare no conflict of interest.  
Figure 16: Stray node communication ability rate. (**a**) Stray node communication ability rates of the autonomous response-type stray node detection protocols; (**b**) stray node communication ability rates of the request-response-type stray node detection protocol; (**c**) stray node communication ability rates of the proposed protocols.