esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results Analysis  
### System Losses  
The parameters of the simulated high inertia FESS are presented in Table 5. Combined losses due to windage, bearing friction, winding resistance and stray losses are calculated as described in Section 5. Figure 8 shows the relationship between power loss of the system and angular speed due to aforementioned losses.  
Figure 6: MATLAB-Simulink model of FESS.  
Figure 7: SVPWM subsystem.  
The drag and bearing friction loss curves indicate variation of losses, due to speed variations under constant temperature (40 \({}^{\circ}\)C) and pressure (100 Pa) conditions. Under these conditions, the mechanical loss is 214 W, which accounts for approximately 75% of the total system losses. However, these losses will be much smaller under normal conditions, since the flywheel will be enclosed in a vacuum chamber, and temperature would hardly reach 40 \({}^{\circ}\)C. Figure 9 shows combined total power loss and other losses as a function of angular velocity.  
Figure 8: Power loss vs speed: windage loss (top left); bearing friction loss (top right); resistive loss (bottom left); stray loss (bottom right).  
Figure 9: Windage, bearing friction, resistance, stray loss, and total power loss.  
The effect of pressure on mechanical loss is significant for high speed applications. Figure 10 shows windage loss as a function of pressure for five different operating angular velocities. It indicates that for higher speeds (15,000 rpm and beyond) the pressure needs to be kept at a lower level to limit the losses at an adequate level. To achieve lower than 100 W at 20,000 rpm, a pressure of less than 75 Pa needs to be reached, leaving a maximum of 124 W for bearing loss. For the same amount of loss, the pressure must be further reduced to approximately less than 20 Pa, if the flywheel is operating at 25,000 rpm.
# Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
## 3 Results and Discussion
### Photocatalytic Degradation of Methylene Blue
The OH radicals are known to attack the C-S*=C functional group of MB, which results in the opening of the central aromatic ring containing both the heteroatoms. A control experiment was effectuated to study comparatively the degradation efficiency of AgNPs and FH-AgNPs. For AgNPs, a percentage degradation of 40.2% was obtained, which was found to be less when compared to FH-AgNPs wherein a percentage degradation of 67.86% was obtained in 60 min. This in turn proves the enhanced catalytic activity of FH-AgNPs over AgNPs.
## 2 GTLR-BiSAR Imaging Geometry and Signal Properties
### GTLR-BiSAR Signal Model and Properties
They are crucial for the design of the GTLR-BiSAR imaging algorithm for the SS and TOPS modes. We will introduce the specific processing procedures according to the signal properties in the following sections.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 3 Proposed Methodology  
Cluster-based routing is an efficient way of clustering biosensors. Each cluster has a CH that collects and compresses biological data from other nodes before sending them to the sink node. Because CHs are critical for transferring large amounts of biological data, these nodes absorb more power than other biosensors. As a result, this function should be turned frequently among various biosensors.  
The proposed ABBSOA-MSSOA architecture may have life-or-death applications in healthcare, notably WBANs. Continuous patient vital sign monitoring requires these power-efficient small sensor networks. These help detect diabetes, high blood pressure, and heart disease early. Energy restrictions in WBAN sensors may reduce their long-term dependability. The research proposes a hybrid optimization methodology to address these issues. Intelligent routing and clustering assure energy efficiency in this architecture.  
ABBSOA optimizes binary decisions like sensor activation and route selection. MSSOA constantly changes routing and clustering topologies to minimize transmission lengths and balance node energy consumption. Dual optimization extends the lifespan of Wireless body area networks (WBANs), allowing them to operate without sensor replacement. These advances save healthcare expenses and enable medical staff to monitor patients in real time during emergencies, speeding up crisis response. It may be applied to dynamic situations like remote healthcare monitoring systems and wearable gadgets. In contrast to traditional WSNs, body positions such as standing, sitting, walking, running, and sleeping affect the clustering problem in WBANs. It is necessary to maintain network reliability regardless of physical movement. Relay nodes are a useful intermediary between biosensors and receivers, enhancing network flexibility and reducing biosensor energy usage. This protocol takes models of node and network energy usage into consideration. The suggested method's flow is illustrated in Figure 2.
## 2 Modeling
### General Model Assumptions
It is important to note here that the subsequent equations refer to an orthotropic plate, which would show the same reaction as the corrugated board to a pure bending load; the equivalent material parameters used here cannot be used to describe in-plane deformation. The stress-strain relation of an orthotropic material within a plane is:  
\[\left( \begin{array}{c}\sigma_{x}\\ \sigma_{y}\\ \tau_{xy}\end{array} \right)=\left( \begin{array}{ccc}E_{1}&E_{2}&0\\ E_{2}&E_{4}&0\\ 0&0&G\end{array} \right)\cdot\left( \begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \gamma\end{array} \right). \tag{1}\]  
As usual, \(\sigma_{s}\), and \(\tau_{xy}\) denote the normal and shear stress, respectively (with the orientation given in the index), \(\varepsilon_{*}\) and \(\gamma\) normal and shear strain, and the matrix includes the elastic parameters of the material. Under the assumptions of Kirchhoff theory, the strain is approximated from the deflection \(w\) by:  
\[ \begin{split}&\varepsilon_{x}=-z\cdot\frac{\partial^{2}w}{ \partial x^{2}},\\ &\varepsilon_{y}=-z\cdot\frac{\partial^{2}w}{\partial y^{2}},\\ &\gamma=-z\cdot\frac{\partial^{2}w}{\partial x\partial y}.\end{split}  \tag{2}\]  
The moments in a plate of thickness \(t\) (aligned with the \(z\)-coordinate as shown in Figure 2b) are given by integrals over the stresses:  
\[ \begin{split}& M_{x}(x,y)=\int_{-t/2}^{t/2}\sigma_{x}(x,y,z) \cdot zdz,\\ & M_{y}(x,y)=\int_{\frac{x}{2}}^{\frac{t}{2}}\sigma_{y}(x,y,z) \cdot zdz,\\ & M_{xy}(x,y)=\int_{\frac{x}{2}}^{\frac{t}{2}}\tau_{xy}(x,y,z) \cdot zdz,\end{split}  \tag{3}\]  
Note that Equation (3) is derived by analyzing an infinitesimal section of the plate. The moments are therefore given per length of the section plane and appear to have the unit of forces. One would obtain the actual moments by integrating the terms above over the length of the respective edge  
Inserting the relations (2) yields:  
\[ \begin{split}& M_{x}(x,y)=\frac{-t^{3}}{12}\cdot\left(E_{1} \cdot\frac{\partial^{2}w}{\partial x^{2}}+E_{2}\cdot\frac{\partial^{2}w}{ \partial y^{2}}\right)\\ & M_{y}(x,y)=\frac{-t^{3}}{12}\cdot\left(E_{2}\cdot\frac{\partial ^{2}w}{\partial x^{2}}+E_{4}\cdot\frac{\partial^{2}w}{\partial y^{2}}\right)\\ & M_{xy}(x,y)=\frac{-t^{3}}{12}\cdot G\cdot\frac{\partial^{2}w}{ \partial x\partial y}\end{split}  \tag{4}\]  
From the equilibrium of forces over an infinitesimal element, one obtains:  
\[\frac{\partial^{2}M_{x}}{\partial x^{2}}+2\cdot\frac{\partial^{2}M_{xy}}{ \partial x\partial y}+\frac{\partial^{2}M_{y}}{\partial y^{2}}=-p(x,y) \tag{5}\]  
Here, \(p\) denotes the surface load of the plate, which consists of the weight of the board and the reaction forces of the underground, as stated above. Combining these equations leads to the behavior of plates under external forces without any internal stress and therefore without warp.
## 5 Ablation Study and Discussions
### Limitations  
The proposed method has the following two limitations. First, the current feature fusion strategy, although it effectively balances efficiency and accuracy, limits the amount of deep feature information being utilized. Second, the method has been primarily evaluated on the RPD dataset, which focuses on textile images. While initial experiments on datasets such as building facades and floor tiles have shown encouraging results, further validation on more and different domains of images is necessary to confirm its generalizability. Additionally, the current method assumes fronto-parallel images as input, and its performance may degrade under perspective distortions, occlusions, or irregular patterns. Extending the method to handle such challenges would broaden its applicability to more complex and diverse real-world scenarios.
# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 4 The Design of the Tracking Algorithm  
### Introduction of the Multi-Target Tracking Switch  
In recent years, many scholars have studied the problems of tracking multiple targets [29; 30; 31; 32]. The research mainly involved solving the challenges of tracking failures caused by the existence of obstructions among multiple targets or the cross motions among similar targets. Most multi-target tracking studies involve relatively static backgrounds. Few research studies have focused on a multi-target tracking switch problem under a dynamic background. The main purpose of this paper was to design an intelligent turntable servo system that automatically tracks airdrops and parachutes and acquires their flight paths and attitudes. The specific tracking process is shown in Figure 5.  
* When the aircraft enters the camera's field of view, TuSeSy automatically captures the aircraft's target by target measurement and then tracks the aircraft using a tracking algorithm, as shown in Figure 5a.
* The system controls the turntable rotation according to the camera return deviation signal, so that the camera is aimed at the target under test. After a period of flight, the aircraft begins an airdrop mission, throwing objects from the aircraft and opening the pilot chute, as shown in Figure 5b.
* It is necessary to make TuSeSy automatically detect the pilot chute, and decide whether to abandon the aircraft's tracking and start tracking the pilot chute instead (Figure 5c).
* When the pilot chute is fully opened, the main chute is thrown from it, as shown in Figure 5d; similarly, TuSeSy automatically detects the main chute and decides whether  
Figure 4: The difference between the center of the current frame and that of the target.  
or not to abandon the tracking of the pilot chute and start tracking the main chute instead (Figure 5e).  
Throughout the tracking process, the tracking tasks of TuSeSy can be divided into three parts: tracking the aircraft, tracking the pilot chute, and tracking the main chute. It is obvious that TuSeSy needs to track multiple targets--unlike the existing multi-target tracking algorithm that tracks multiple targets in the image at the same time. TuSeSy needs one switch after another to track the targets that appear in turn during the airdrop. The algorithm flow chart is shown in Figure 6.  
Figure 5: The process of the airdrop. (**a**) Aircraft came into the view; (**b**) Throw the object; (**c**) Open the pilot chute; (**d**) Open the main chute; (**e**) The main umbrella is fully open.  
Figure 6: The algorithm flow.
# Advances in Search Strategy Using the Set of Brand Considerations in the Web Ecosystem
## 4 Results  
### Characteristics of Online Path to Purchase by Search Strategy Group  
To understand the information on the difference in behavior in the search strategy, we visualized the consumers' online purchase paths through Gephi, an open graph business platform for network analysis, as shown in Figure 1. We also used visit sequences and origin-destination pair set data to extract these customer purchase paths. Figure 1 displays a strong tendency to search for information on search engines, automotive sections of portal sites, and brand homepage sites. The group with a brand message-oriented search strategy was more likely to visit the homepage of the purchased brand than the homepage of other brands if their journey began with a search engine. Figure 1 reveals that the group with the consumer message-oriented search strategy moved differently from the search engine to the next destination. This group tended to move from search engines to consumer-initiated sites, such as online car communities, rather than to brand-initiated sites. Moreover, this group tended to search for information about considered brands more than purchased brands, as evidenced by the flow that they moved from the automotive sections of portal sites to the homepage of considered brands. The group with a consumer message-initiated search strategy tended to visit sites with consumers' voices or sites with considered brand information. Lastly, Figure 1 shows that the group with a third-party message-oriented search strategy tended to search for information on the automotive sections of portal sites and then move to used car dealers' sites. They also frequently moved from search engines to online communities of purchase brands and considered brands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster Size**} & **Brand Message-Oriented** & **Consumer Message-Oriented** & **Third-Party Message-Oriented** & \multirow{2}{*}{**Sig.**} \\  & **Search Strategy** & **Search Strategy** & **Search Strategy** & \\  & **(n = 23)** & **(n = 27)** & **(n = 34)** & \\ \hline Demographics & & & & \\ \% of male buyers & 73.9\% & 96.3\% & 76.5\% & ns \\ Mean of age & 38.4 & 35.0 & 37.7 & ns \\ \hline Automobiles & & & & \\ \% of imported car brand buyer & 4.4\% & 7.4\% & 26.5\% & ** \\ \% of large-car buyers 1 & 21.7\% & 3.7\% & 20.6\% & ns \\ Mean of car price & $27,314 & $23,686 & $29,953 & ns \\ \hline \hline \end{tabular} (Significant Level: **_p \(<0.05\)_) 1 Large car means automobiles with capacity of over 3000 cc.  
\end{table}
Table 3: Characteristics of demographics and automobiles by search strategy group.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 4 Hops-Based Back-Pressure Routing
### Distributed Routing Algorithm  
Due to the limitations of the platform size and the power consumption of satellites in the LSN, on-board computing and storage resources are tight. The multi-hop network structure enables each satellite to connect to surrounding satellites through ISLs, which is suitable for the distributed deployment of routing protocols.
## 2 Design of the 1 \(\times\) 4 Channel Wavelength Demultiplexer
### Low-Loss Si\({}_{3}\)N\({}_{4}\) Waveguide  
The low-loss single stripe geometry Si\({}_{3}\)N\({}_{4}\) waveguide from the TriPleX photonic platform [13] was chosen to design the photonic components presented in this paper. The waveguide fabrication process is similar to the methods reported in [4]. The width and height of the waveguide was chosen such that minimum propagation loss is achieved at the bending radius equal to or more than 4 mm. The cross-section area of the waveguide is shown in Figure 1a. Figure 1b shows the TE mode profile of the optical waveguide obtained from 2D finite-difference time-domain (FDTD) simulation using the Lumerical Mode software.  
Figure 1: (**a**) Cross-section (not to scale) of the single stripe geometry Si\({}_{3}\)N\({}_{4}\) waveguide, (**b**) Simulated TE mode profile (in linear scale) at 1550 nm wavelength, where the 72 nm thick Si\({}_{3}\)N\({}_{4}\) waveguide is superimposed (drawn to scale).  
We calculate the effective index (\(n_{\textit{eff}}\)) and group index (\(n_{\textit{g}}\)) of the waveguide using the 2D FDTD method over the wavelength range from 1530 nm to 1580 nm. Figure 2a,b shows that the effective index and group index of the waveguide varies by \(\Delta n_{\textit{eff}}=-0.0015\) and \(\Delta n_{g}=-0.006\), respectively, over the wavelength range from 1530 nm to 1580 nm. The change in both the effective index and in the group index of the waveguide with respect to the wavelength results in a wavelength dependency in the transmission response of the demultiplexer, which will be discussed in Section 3.2.
## 3 Materials and Methods
### Preliminaries and Notations
#### 3.1.4 Energy Consumption  
Each sensor node has its battery capacity, and with adjustable transmission power. The energy consumption of a sensor mainly includes the energy consumed on the sensor module, its processor module and its communication module, among which the communication module consumes the most energy. Hence, the energy consumption of a sensor node can be approximated by the communication energy consumption while ignoring its other energy consumptions. According to the typical model of energy consumption of free-space spherical wave, the energy consumption of a sensor node is:  
\[\begin{cases}E_{c}=E_{Rx}+E_{Tx}\\ E_{Rx}=pl\cdot e_{s}\\ E_{Tx}=pl\cdot(e_{s}+e_{r}d^{2})\end{cases} \tag{3}\]  
where \(pl\) is the data volume that a sensor node receives or transmits, in bit; \(e_{s}\) is the circuit energy consumption of emitting or receiving per bit data, in J/bit; \(e_{r}\) is the minimum energy of signal per bit that can be received by sensor nodes or sink nodes successfully, in J/(bit\(\cdot\)m\({}^{2}\)); \(d\) is the communication distance, in meter.
## 6 Conclusions  
In conclusion, this work has shed light on various orbit configuration and release maneuver options for the deployment of satellite constellations using METs. For newer satellite teams wanting to join in the constellation ecosystem, more feasible methods of establishing constellations can be helpful in this effort, with METs being one potential solution to consider. By implementing METs into constellation deployments, satellite teams can more easily handle design, resource, time, orbit delivery, and cost constraints that have been difficult in the past. The material discussed in this article gives a brief insight and demonstration of how METs and satellite constellations can be combined for rapid deployments to produce new mission architectures that could be flown. Future research and publications will include applications of the concepts described in this article to existing real-world constellations and comparing them against current industry norms, and also development and results from a small technology demonstration mission called ADRATEA to be flown in the space environment. Current and future work in these areas will continue with the intent of improving the way constellations of any scale are created at key locations in accessible regions of space.  
Conceptualization, B.C.; methodology, B.C.; software, B.C.; validation, B.C.; formal analysis, B.C.; investigation, B.C.; resources, B.C. and L.D.T.; data curation, B.C.; writing--original draft preparation, B.C.; writing--review and editing, B.C. and L.D.T.; visualization, B.C.; supervision, L.D.T.; project administration, B.C. and L.D.T.; funding acquisition, B.C. and L.D.T. All authors have read and agreed to the published version of the manuscript.  
This research is jointly funded by the NASA Alabama Space Grant Consortium (NASA Grant #80 NSSC20M0044) and the UAH College of Engineering.  
**Data Availability Statement:** No new data was created or analyzed in this study. Data sharing is not applicable to this article. Future supporting information (such as further publications and demonstration flight data) will be available at space.uah.edu and saderaerospace.com.  
**Acknowledgments:** The author thanks his research advisor [PERSON] for supporting this research, along with generous support from the NASA Alabama Space Grant Consortium and the UAH College of Engineering. The author also thanks the student team members from the UAH Terminus Spaceflight Research Group involved in the ADRASTEA mission for assisting in developing new technologies relating to the concepts described in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Generation and Validation of the 0.05-Degree, Gridded GPP Product  
We converted our fine-resolution GOSIF estimates (0.05\({}^{\circ}\) and 8-day) to GPP estimates from 2000 to 2017 using the eight SIF-GPP relationships. Therefore, we produced eight sets of gridded GPP estimates (i.e., an ensemble of GPP estimates), referred to as GOSIF GPP hereafter. We used the ensemble mean to examine the dynamics of GPP and the standard deviation of the ensemble as a measure of uncertainty. Altogether, the ensemble GPP estimates based on these eight SIF-GPP relationships with different forms could to some extent account for the uncertainty of GPP estimates. We also aggregated the 8-day ensemble of GPP estimates to monthly and annual timescales so that GPP could be examined at these timescales.We used the remaining GPP data from the 91 FLUXNET sites to validate the resulting eight 8-day GPP estimates and the ensemble mean. We also compared the ensemble mean GPP against tower GPP at monthly and annual scales. The evaluation of the ensemble mean GPP was also conducted for each biome separately. In addition, we compared our ensemble mean GPP against a gridded GPP data product (EC-MOD) upscaled from flux tower GPP data using a data-driven approach [37; 38] by comparing the pixel-wise difference in annual GPP of 2010 for each biome.  
We further examined how the estimated GPP tracked the interannual variability of tower GPP. We calculated the coefficient of variation (CV) of annual GPP (both tower GPP and estimated GPP) for flux sites with more than three year of data. The CV was calculated as the standard deviation divided by the multiyear mean. This measure was useful to examine whether the large or small interannual variability in tower GPP for each site could be captured by our estimated GPP. In addition, for flux sites with more than three years of data, we calculated the annual anomaly (annual value minus the multiyear mean) for each site and each year, and compared the anomalies of tower GPP versus estimated GPP for different vegetation types. We then averaged the annual anomalies for all the sites in each year. The resulting anomaly characterized the overall variability in each year.
## 3 Methodology
### ReliefF Feature Selection  
ReliefF algorithm was preferred to handle noisy and redundant features in the dataset and to select the most important features. Thus, it is aimed to increase the classification accuracy while reducing the computational complexity. ReliefF is a feature selection technique particularly designed to handle multi-class problems and deal with high-dimensional data [59; 60]. The pseudocode of the ReliefF technique [61] is presented below.  
Inputs: Dataset with instances and features, \(k\).  
Output: Weight features.  
Initialize weights.  
Randomly select a subset of instances.  
Find k nearest hits and misses for each selected instance.  
Update the weights of features using the differences between instances and feature values.  
Normalize the weights to fall within a certain range.