esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 3 Description of the Proposed Algorithm  
### Overview of GPSO-\(\omega\)  
GPSO-\(\omega\) was suggested in [59] by expanding the functionality of PSO. GPSO-\(\omega\) initializes \(N\) distinctive solutions with \(N\) distinctive velocity vectors. In order to identify the most optimum point in each situation, the GPSO-\(\omega\) search begins with exploring random locations as a possible solution to the problem. Evaluating these random locations/solutions for the desired objective sets directions for developing new solutions, often referred to as updated solutions [60]. The updated solutions further recursively undergo an evaluation process till a termination criterion is reached.  
The equation presented below demonstrates that positioning vectors are upgraded towards the most optimum solution on the basis of their personal positioning along with the positioning of the best possible solution. \(c_{1}\) and \(c_{2}\) can be used to control an individual'sshare to the total propagation of solutions. The solutions are updated using velocity and position updates based on the following equations:  
\[\begin{array}{c}V_{i}(k)=\omega(k)\times V_{i}(k-1)+c_{1}r_{1}(k)\times\left[P_{ B}(k-1)-X_{i}(k-1)\right.\\ \left.+c_{2}r_{2}(k)\times\left[G_{B}(k-1)-X_{i}(k-1)\right]\right.\end{array} \tag{13}\]  
\[\begin{array}{c}X_{i}(k)=X_{i}(k-1)+V_{i}(k)\end{array} \tag{14}\]  
where \(c_{1}\) and \(c_{2}\) denote accelerating constants, \(r_{1}(k)\) and \(r_{2}(k)\) denote two random numbers, \(\omega\) indicates inertial weight, \(P_{B}\) describes the particle's personal best position, \(G_{B}\) shows the particle's best position within the whole swarm, and \(V_{i}(k)\) and \(X_{i}(k)\) denote the velocity and positioning of the \(i^{\text{th}}\) solution in \(k^{\text{th}}\) iteration, respectively.
## 3 Related Work
In [18], a binary linear programming model was proposed to manage the switches and SDN controllers based on different metrics, including traffic on the link, several flows at the network edge, and the distance between switches and controllers. The results in [18] revealed that the proposed approach saves up to 40% of the energy consumption. [PERSON] et al. [19] proposed an SDN-based resource management approach. In particular, the SDN enabled the cloud to manage resources efficiently, thus consuming less energy. The results in [19], obtained using a data center example, showed that the proposed SDN-based resource management strategy reduces 60% of the power consumption. Finally, in [22; 23; 24], SDNs were used to build routing protocols for different types of networks. In [24], a recent literature review studying the most relevant SDN-based routing protocols proposed in the literature for wireless ad-hoc networks (WANETs) can be found. In particular, the current challenges in the field as well as the most concerning research gaps were discussed. In [23], an SDN-based link quality-aware routing protocol for WSNs based on the cognition of real-time network data was proposed to improve real-time data transmission. Results in [23] showed that the proposed approach achieves good performance in terms of real-time data transmission and reliability. In [22], an intelligent routing protocol based on SDN and RL was proposed. In particular, the proposed approach included RL in the SDN controller to make better decisions in terms of the different QoS issues and thus improve the routing. Results in [22] showed that the proposed approach outperforms baseline routing protocols in terms of stability, delay, and loss rate. As discussed above, SDNs have been used in the literature to reduce energy consumption in different applications [18; 19] and build QoS-aware routing protocols for different types of wireless networks, including WANETs and WSNs [22; 23; 24]. Nevertheless, none of these works have exclusively addressed--and solved--the energy consumption issue in IIoT and Industry 4.0 by developing energy-aware routing strategies based on SDNs. In this paper, an SDN-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. In particular, the SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the shortest-path criterion. In this way, by balancing the energy consumption of all the nodes in the WSN, critical nodes do not drain their batteries. Consequently, the transmission lasts longer, and the need for replacing nodes' batteries is avoided.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 3 Results
### Results of Phenological Responses to Climate  
We used SHAP analysis to reveal the effects of each climatic factor on the phenology (SOS/EOS) of deciduous forests in mid- and high-latitude regions of the Northern Hemisphere extracted from 1-km and 5-km SIF data, and the results are shown in Figures 12 and 13. We plotted beeswarm plots of SHAP values and histograms of feature importance to show the contribution of each climatic factor to the model predictions and its average feature importance, respectively. The beeswarm plots display the distribution of SHAP values for each climatic factor, with colors indicating the magnitude of the values,  
Figure 11: Comparison of trends in deciduous forests’ phenology extracted from three different scales of SIF.  
Figure 10: Trends in deciduous forests’ phenology extracted from 50-km SIF data across the Northern Hemisphere’s middle and high latitudes (2008–2017).  
distinguishing between facilitating and inhibiting effects. The histograms show the average SHAP values for each climatic factor, which serve as the basis for the importance ranking.  
Overall, temperature (both air and soil temperature) is the key factor driving the variation in SOS in mid- to high-latitude deciduous forests in the Northern Hemisphere, followed by radiation and precipitation. For EOS, temperature, moisture (soil water content and precipitation), and radiation are the key meteorological factors.  
Among the climatic factors affecting the SOS extracted from the 1-km SIF, soil temperature in layer 4 (TS4) was the most critical factor, closely followed by climatic factors such as T2M, soil temperature in layer 3 (TS3), D2M, and PAR. Among these climatic factors, TS4, T2M, TS3, and D2M had a significant negative effect on SOS, suggesting they contribute to its advancement. In contrast, variables such as PAR, PRE, and soil water content in layer 1 (SWC1) inhibited the advancement of SOS. For EOS, TS4 was again the primary influencing factor, followed by climatic factors, including D2M, the soil water content in layer 4 (SWC4), VPD, PRE, and PAR. Among these factors, TS4, D2M, SWC4, PRE, and PAR tended to delay EOS, while VPD promoted the advancement of EOS. Compared with the 1-km results, the evaluation of the phenological metrics (SOS/EOS) derived from the  
Figure 12: Influence of various climatic factors on the phenological metrics SOS/EOS extracted from 1-km SIF.  
Figure 13: Influence of various climatic factors on the phenological metrics SOS/EOS extracted from 5-km SIF.  
5-km SIF, as influenced by climatic factors, showed that the response mechanism of each climatic factor to the phenological metrics remained unchanged. However, there was a change in the order of importance: among the SOS indicators, soil temperature in layer 3 (TS3) was more important than T2M, while among the EOS indicators, soil water content in layer 4 (SWC4) was more important than D2M.
## 2 The IDIVS Concept and Application Domains
### Application Areas
#### 2.4.2 Smart Mobility  
To estimate the CO\({}_{2}\) footprint of an individual caused by urban transportation, we need to know which means of transportation a person is currently using. To estimate the current mode of travel, an IDIVS could be integrated into a person's smartphone using, e.g., position and accelerator data as input data, and leverage user feedback to improve its accuracy in estimating the current mode. Another example of an output of an IDIVS in smart mobility is the number of people in a vehicle, e.g., in a bus or a train, based on multiple sensors, e.g., door entries and Wi-Fi units connected. This, in turn, can be used for, e.g., guiding travellers to a specific bus departure to avoid crowding. In this case, drivers of vehicles can occasionally provide the correct count of passengers to the IDIVS. In general, there are ample examples of IoT-based services in the domain [17; 18] that could also be relevant for the IDIVS concept.
# Comprehensive Study of the Mechanical and Tribological Properties of NiCr-Al Detonation Coatings
## 4 Conclusions  
A comprehensive comparison of homogeneous and gradient-structured NiCr-Al detonation coatings has shown that gradient coatings exhibit superior mechanical and tribological performance. These improvements are attributed to favorable microstructural features, such as gradual elemental distribution and enhanced phase homogeneity, which contribute to increased hardness, adhesion strength, and wear resistance. The findings suggest that gradient NiCr-Al coatings are promising candidates for protective applications in high-temperature and high-load environments, including energy, aerospace, and heavy machinery sectors. Future research may focus on the optimization of gradient structures and assessment of their performance under complex service conditions such as corrosion, erosion, and thermal cycling.  
Conceptualization, Z.S. and L.S.; methodology, M.M.; software, S.B.; validation, C.D., Z.S. and L.S.; formal analysis, M.M.; investigation, M.M.; resources, C.D.; data curation, Z.S.; writing--original draft preparation, M.M.; writing--review and editing, B.R.; visualization, Z.S.; supervision, L.S.; project administration, M.M.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23490355).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Coating Structure** & **Phase Composition** & **Adhesion, MPa** & **Microhardness, HV** & **Tribometer TRB3** & **Tribometer T-21** \\ \hline Homogeneous & CrNi\({}_{3}\) & \(25.4\pm 3.1\) & \(363\pm 10\) & \(0.80\pm 0.04\) & \((5.73\pm 0.30)\times 10^{-3}\) & \(0.50\pm 0.03\) & \((512\pm 25)\times 10^{-6}\) \\ \hline Gradient & CrNi\({}_{3}\) Al & \(38.7\pm 6.9\) & \(534\pm 12\) & \(0.74\pm 0.05\) & \((3.13\pm 0.20)\times 10^{-3}\) & \(0.46\pm 0.02\) & \((202\pm 15)\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation table of the obtained results.  
Figure 14: Two-dimensional (**a,c**) and three-dimensional (**b,d**) profiles of the wear tracks of homogeneous (**a,b**) and gradient (**c,d**) NiCr-Al coatings after high-temperature tribological testing.  
**Data Availability Statement:** The original contributions presented in the study are included in this article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company PlasmaScience LLP. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Real-Time Monitoring of Passenger's Psychological Stress
## 2 Results  
### Learning Phase  
#### 2.1.1 Data Acquisition  
The learning phase spread over three consecutive days, when the participant travelled in a foreign city (Bilbao, Spain) to attend a meeting with several partners. During the daytime, he continuously wore a smart wristband recording peripheral signals: heart rate, skin conductance, skin temperature and 3-axis acceleration. Every evening of the journey, the participant was asked to identify (if relevant) one episode of significant stress that happened during the day. On request, he also reported a period of low stress during his stay.  
Altogether, three episodes of stress (their total duration was 162 min, and a stress rate of 6/10 was reported by the participant) and one moment of leisure time with moderate activity (a museum visit: total duration of 120 min, rated 1/10 by the participant) were retained to constitute the learning database. The three stress periods occurred in distinct situations (taking the airport shuttle, giving a stressful oral presentation and dealing with a shortened flight connection), in order to implement an algorithm that is able to estimate stress in any real-life context. Offline, feature extraction was performed on the recorded database, and a psychological stress model was calibrated by supervised learning on the grounds of the four self-identified events.
## 3 Applications of Game Theory in WSN
### Game Theory for WSN Security
#### 3.3.1 Preventing DoS Attacks  
The game types for preventing DoS attacks include non-cooperative game [171; 172; 173], cooperative game [174], and repeated game [100, 101]. The jamming and anti-jamming issues are modeled as a zero-sum stochastic game in literature [175] to defend DoS attack. In this game, the actions of the sensor and jammer are dependent on the current system state. A quadratic function is used as the payoff function, thus facilitating the LQG control of the power system. The NE of the game is analyzed, including the existence and the corresponding computation. Numerical simulations are carried out for a seven-dimensional linear system of power grid and demonstrate the increase of reward when proper anti-jamming actions are taken. [PERSON] _et al_. [176] established an attacking-defending gaming model which can detect active DoS attacks effectively, where the strategy space and payoff matrix are given to both the IDS and the malicious nodes.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion  
### Experimental Environment Configuration  
In this paper, we adopt the way of model training and model testing successively, i.e., the training set completes one round of iteration and then conducts a test on the test set of images in order to obtain the performance evaluation metrics of the test in a timely manner. This method can greatly save the time of model training and testing, and can obtain the parameters of the model in time, so that the optimal solution can be found at any time according to the test results to change the parameters of the model. In order to get a better water body extraction effect, the software and hardware configuration of the experimental environment is shown in Table 1.
## Results and Discussion
### Structure Activity Relationships (SARs) of 6a-j  
The compound, namely (E)-1-benzyl-3-hydrazoinodolin-2-one (**4**) showed moderate antiproliferative activity (14.15 uM) against the non-small cell lung cancer cell lines 'A549' similar to that of gefitinib (15.23 uM), but six-fold less active against human cervical cancer cells lines 'HeLa' (46.03 uM). Compound **4** reacted with 4-substituted aryl aldehydes forming compound **6a** having 4-(_N_,_N_-)-dimethyl group (**6a**), chloro group (**6b**) and methyl group (**6i**) decreased the antiproliferative activity than the gefitinib against both cell lines tested. While substituted by methylthio group (**6i**) shows similar antiproliferative activity comparing gefitinib. Interestingly, as depicted in Table 2, entry **6c**, substituted by hydroxy group at 4-position showed higher antiproliferative activity which is around four fold than the gefitinib against non-small cell lung cancer cell lines 'A549' (4.35 uM) and two fold higher than the HeLa cell lines (4.09 uM). In case of bromo substituents at the 2- and 4-positions of the aryl ring, the compounds **6d** and **6f**, respectively, the antiproliferative activity dramatically increased more than two-fold for A549, and similar for HeLa cell lines in comparison to the gefitinib. While addition at the 3-position by a bromo group (**6e**) and by a methyl group (**6h**) of the aryl ring showed similar or less antiproliferative activity comparing to gefitinib. Interestingly, the methyl group at 2-position (**6g**) increased the activity by two-fold compared to gefitinib, against A549 and similarly against HeLa cell lines. In conclusion, ortho and para substitution by bromo and hydroxy group proved to be the most active potential agents for non-small cell lung cancer cell lines 'A549' among the compound tested.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 3.2.3 XRD Analysis of Zinc Oxide Nanoparticles  
Figure 4 illustrates different intense peaks of Bragg reflections of ZnO-NPs formed using the seaweeds with Bragg angles corresponding to zinc 20 = 102. The XRD analysis verifies the wurtzite structure of the ZnO nanoparticles, and the narrow peak generation with a Bragg's angle of 20 = 102\({}^{\circ}\) indicates that the ZnO nanoparticles are crystalline. The ZnO nanoparticles are suggested to be face-centered cubic at a Bragg's angle of 20 = 100\({}^{\circ}\). The prominent peaks show that the nanoparticles are stabilized by some capping agents. The strong Bragg's angle reflection suggests that the capping agents caused strong X-ray scattering centers in the crystalline phase.  
Figure 4: XRD analysis of ZnO nanoparticles prepared from tested seaweeds: _Ulva lactuca_ (**A**), _Ulva intestinalis_ (**B**), and _Sargassum muticum_ (**C**).
## 4 Results and Discussion
### Relative Air Humidity and Relative Deviation of Relative Humidity  
The relative humidity (\(RH\)) of the air represents the level of partial pressure of water vapor in the indoor air, which has a significant effect on photosynthesis as well as transpiration in plants. According to relevant studies, it is recommended to maintain the air relative humidity between 50% and 70% for optimal growth of green leafy vegetables [57]. Excessively high relative humidity of the air can impede plant transpiration and increase the risk of pathogenic bacterial infections, while excessively low relative humidity of the air can result in leaf burn [15]. Thus, it is essential to analyze the variations in indoor relative humidity distribution.  
Figures 14 and 15 depict average relative humidity (\(RH\)) and relative standard deviation of relative humidity (\(RSD_{RH}\)) values above the crop in each layer of cultivation trays at different air change rate (\(ACH\)) for the five \(P_{d}\) as concerned. From Figure 14, it can be observed that, in the cases with \(P_{d}\) > 0.5 and \(ACH\) \(\leq\) 60 h\({}^{-1}\), relative humidity of the air above the crop area of each layer of cultivation trays decreases with the increase in the height of cultivation trays, while in the cases with \(ACH\) > 60 h\({}^{-1}\), it appears that average relative humidity of the air above the crop area of the third layer cultivation trays is greater than that of the secondary layer cultivation trays. In the instances where simultaneously  
Figure 12: The \(RSD_{T}\) as a function of \(ACH\) for all different planting density cases.  
Figure 13: Velocity vector diagram for \(P_{d}\) = 0.1.  
satisfying \(P_{d}\leq 0.5\) and \(ACH\leq 40\) h\({}^{-1}\), relative humidity above the crop area in each layer of cultivation trays decreases as the height of the cultivation trays promotes. However, in the case of \(ACH=60\) h-1, volume-averaged relative humidity above the crop area in the third layer cultivation trays is higher than that in the secondary layer cultivation trays. In cases where \(P_{d}>0.5\) and \(ACH>60\) h\({}^{-1}\), average relative humidity above the crop area in the third layer cultivation tray was higher than that in the secondary layer one. Figure 15 shows the overall \(RSD_{RH}\) above the crop at five planting density at different \(ACH\) scenarios. It can be found that \(RSD_{RH}\) decays for all \(P_{d}\) cases as \(ACH\) increases. Overall, relative humidity (\(RH\)) levels above the crops in each layer of the cultivation tray are generally falling within the appropriate range (air relative humidity between 50% and 70%) for all five \(P_{d}\) and different \(ACH\). However, it is worth noting that the air above the crop area of the first layer cultivation tray tends to promote \(RH\) levels.  
Figure 14: The average relative humidity (\(RH\)) above the crop in each layer of the cultivation tray at various Pd as a function of \(ACH\): (**a**) \(Pd=0.9\); (**b**) \(Pd=0.7\); (**c**) \(Pd=0.5\); (**d**) \(Pd=0.3\); (**e**) \(Pd=0.1\).
# Characterization of Chromatic Dispersion and
## 5 Conclusions  
We characterized both the refractive index and the coefficient of chromatic dispersion of PMMA POFs made by two different manufacturers. A tunable femtosecond laser and a Streak Camera were used for this purpose. The characterization technique is based on measuring the time delay between light pulses at different wavelengths when they propagate through POFs. Three different fiber lengths were considered for all the measurements. In all cases, very similar results were obtained, the maximum discrepancy between measurements being close to 3%. The obtained values match satisfactorily with previously reported values, which seems to corroborate both the validity of our measurement technique and our results. As far as we know, this is the first time that this kind of direct characterization from POFs has been carried out.  
This work has been funded in part by the Fondo Europeo de Desarrollo Regional (FEDER); by the Ministerio de Economia y Competitividad under project TEC2015-638263-C03-1-R, and by the Gobierno Vasco/Eusko Jaurlarita under projects IT933-16 and ELKARTEK (KK-2016/0030, KK-2017/00033, KK-2017/00089 and KK-2016/0059). The work of [PERSON] was supported in part by a research fellowship from the Universidad del Pais Vasco/Euskal Herriko Unibertsitate (UPV/EHU), Vicerrectorado de Euskera y Formacion Continua, while working on a Ph.D. degree.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; all authors contributed to the scientific discussion and wrote the paper. The authors declare no conflict of interest.