esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Spectrum Allocation (SA)  
The SE and EE enhancement is a significant research problem in designing appropriate transmission strategies and SA methods in a HetNet. Allocating the capacity of the links with distributed network nodes efficiently requires optimal utilization of spectrum resources in a HetNet. The SA schemes that are typically involved in a HetNet are listed in Table 5. Figure 8 shows the spectrum ranges in HetNet.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 2 Overview of ZigBee
### IEEE 802.15.4 MAC and Physical Layers  
The IEEE 802.15.4 MAC layer has two operation modes: beacon and beaconless modes [3, 29, 30]. In this paper, we focus on the beaconless mode, which is more suitable for multihop communications. In the beaconless mode, devices associate with a coordinator device and new devices join the network by requesting beacons from devices already associated, extending the range of the network.  
The IEEE 802.15.4 MAC layer controls the access of the channel through a carrier sense multiple access with collision avoidance (CSMA-CA) process. As detailed in Section 6.2.5 of [3], such a CSMA-CA procedure contains mechanisms for channel monitoring, random backoff, and retransmission.  
As in other wireless protocols using CSMA-CA, ZigBee networks are prone to the hidden node problem [31; 32], in which devices fail to sense each other transmissions (being hidden from each other) and may transmit at the same time, causing interference in the receiving node. For instance, in Figure 1, nodes 4 and 5 are far from nodes 0 and 3. When nodes 4 or 5 transmit, the energy received at nodes 0 and 3 is too low to trigger their carrier sense mechanism. If nodes 0 or 3 have a packet to transmit, they would then transmit at the same time as nodes 4 and 5, causing interference in the reception at nodes 1 and 2.  
The IEEE 802.15.4 physical layer specifications enable operation in the unlicensed frequency spectrum at 2.4 GHz, specifying 16 channels of 2 MHz with carriers spaced by 5 MHz. As will be discussed in this paper, because WiFi IEEE 802.11 networks also operate in this band, interference and packet losses may occur in ZigBee communications when both networks operate in overlapping channels [8; 33; 34].  
The IEEE 802.15.4, the physical layer also specifies that, whenever it sends a packet to the MAC layer, it also sends a _link quality indicator_ (LQI). The LQI is a number between 0 and 255 that reflects the quality of the received symbols, being correlated with the SINR of the received packet. The LQI is also forwarded to the network layer, allowing the network layer to estimate the cost of the link, as discussed in Section 3.3.
## 1 Introduction
These drawbacks render their extensive application in coal mines and other monitoring domains challenging. Hydraulic supports typically operate in underground coal mine environments characterized by restricted space and intricate structures. They require installation at various critical components of the hydraulic support system to monitor its attitude. Conventional Fiber Bragg Grating (FBG) inclination sensors are challenging to miniaturize due to their internal architecture, which encompasses fiber-optic gratings, encapsulation materials, and signal-processing circuitry. During the actual installation procedure, constraints often exist regarding the installation location. For instance, in some narrow connection parts of the hydraulic support or in areas with compact space adjacent to the hydraulic system, it is arduous to accommodate the relatively large FBGtilt sensors. This situation consequently impedes their comprehensive deployment in the monitoring of coal mine hydraulic supports. Therefore, there is an urgent need to develop a novel, flexible, convenient, environmentally adaptable, and cost-effective Fiber Bragg Grating (FBG) inclinometer for hydraulic supports. Building upon previous research, this paper harnesses FBG sensing technology to offer a new monitoring tool and approach for the attitude monitoring of hydraulic supports--the hydraulic support FBG inclinometer. A newly designed hydraulic support FBG inclinometer is proposed, which is capable of conducting real-time dynamic monitoring of the inclination angles of the roof beam and base of the hydraulic support. This new design not only enables the real-time dynamic monitoring of the tilt angles of the roof beam and base of the hydraulic support but also allows for the comprehension of related roof deformations based on the changes in the tilt angle of the roof beam. This novel design of the hydraulic support FBG inclinometer outperforms the previous counterparts substantially. In dimensions, precision, and stability, it showcases marked improvements compared to existing products. Its enhanced characteristics are of paramount significance for ensuring the safety of coal mines and the well-being of workers. The compact size allows for easier installation in the confined spaces of underground coal mines, while the improved accuracy and stability enable more reliable monitoring of hydraulic support conditions, thereby contributing to a safer mining environment. In addition, through the combination of theory and practice, the design and development of the hydraulic support FBG inclinometer and the field industrial test were carried out [14; 15; 16].
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results
#### 5.2.6 Effect of \(\lambda\) on Global Model Integration  
In the cross-domain knowledge learning module, the parameter \(\lambda\) is used to adjust the influence of public data on the global model. As shown in Figure 6, we investigated the impact of varying \(\lambda\) from 0 to 1 on model performance. When \(\lambda=0\), the server aggregated only the structural parameters from local clients, meaning that the global model relied solely on client-specific structural information without incorporating any cross-domain structural knowledge. Under this setting, the model exhibited slightly worse performance, indicating that training based solely on local data may limit the generalization ability of the global model. As \(\lambda\) increased, the influence of cross-domain knowledge gradually strengthened. The results show that the best performance was achieved when \(\lambda=0.3\), suggesting that moderate incorporation of cross-domain knowledge can enhance the model's adaptability and improve its robustness across heterogeneous client data distributions. However, when \(\lambda=1\), the global model was entirely derived from cross-domain knowledge provided by the server, diminishing the role of local network topologies in model updates. This led to a significant drop in performance, likely because excessive reliance on cross-domain knowledge hinders the learning of client-specific structures. The experimental results demonstrate that a small \(\lambda\) value may cause the model to overly depend on local information, limiting generalization, whereas a large \(\lambda\) value may bias the model toward public data, thereby neglecting important local topological features.  
Figure 5: Results for different numbers of clients.  
Figure 6: Experimental results for different \(\lambda\) values.
# The Design of 2 DOF IMC-PID Controller in Biochemical Reaction Networks
## 2 Materials and Methods  
### The 2 DOF IMC Control Theory  
In this part, the fundamental control theory is introduced for designing the 2 DOF IMC-PID controller. The structure of the 2 DOF control system using the IMC controller is shown in Figure 1. The 2 DOF IMC controller is composed of \(Q_{1}(s)\) and \(Q_{2}(s)\), and \(G_{m}(s)\) is the internal mathematical model of the plant \(G_{p}(s)\).  
According to Figure 1, \(r\), \(d\), and \(y\) represent the input signal, output signal, and disturbance signal of the control system, respectively. The relationship between the signals in the control system is derived as  
\[y(s)=\frac{G_{p}(s)Q_{1}(s)}{1+Q_{2}(s)[G_{p}(s)-G_{m}(s)]}r(s)+\frac{G_{p}(s)[ 1-G_{m}(s)Q_{2}(s)]}{1+Q_{2}(s)[G_{p}(s)-G_{m}(s)]}d(s) \tag{1}\]  
The structure in Figure 1 can be simplified to the set-point filter type 2 DOF control structure shown in Figure 2; the 2 DOF controller is formed by \(C_{1}(s)\) and \(C_{2}(s)\).  
The controller can provide 2 DOF in Figure 2, because the set-point input \(r(s)\) and the disturbance input \(d(s)\) can be controlled separately by two individual controllers [39; 40]. On the basis of Equation (1), the following equation can be obtained  
\[y(s)=\frac{C_{1}(s)C_{2}(s)G_{p}(s)}{1+C_{2}(s)G_{p}(s)}r(s)+\frac{G_{p}(s)}{1 +C_{2}(s)G_{p}(s)}d(s) \tag{2}\]  
The transition relationship between 2 DOF IMC controllers and 2 DOF controllers is given by  
\[C_{1}(s)=\frac{Q_{1}(s)}{Q_{2}(s)} \tag{3}\]  
\[C_{2}(s)=\frac{Q_{2}(s)}{1-G_{m}(s)Q_{2}(s)} \tag{4}\]  
Figure 1: The 2 DOF control structure with IMC controller.  
Figure 2: The 2 DOF control structure of set-point filter type.  
**Remark 1**.: _When the internal model is exact, Equation (1) can be further expressed as_  
\[y(s)=G_{m}(s)Q_{1}(s)r(s)+[1-G_{m}(s)Q_{2}(s)]d(s) \tag{5}\]  
_By Equation (5), the set-point following capability of the control system in Figure 1 is determined by \(Q_{1}(s)\), and the disturbance rejection performance is influenced by \(Q_{2}(s)\). Therefore, the set-point input \(r(s)\) of the control system is mainly managed by the set-point filter \(C_{1}(s)\) and the disturbance input \(d(s)\) in the feedback loop is mainly managed by the PID controller \(C_{2}(s)\) in the control system shown in Figure 2. With the 2 DOF control structure shown in Figure 2, the set-point input \(r(s)\) and the disturbance input \(d(s)\) can be controlled separately._
## 2 Methods
### Results of Reliability and Exploratory Factor Analysis  
Table 1 shows the final results of the reliability analysis of subjective survey variables. After conducting a reliability analysis of work environment-related questions, 10 out of 15 questions were eliminated. The final selection consisted of 5 questions addressing vibration, noise, high temperature, low temperature, and handling of heavy objects. The resulting Cronbach's alpha value was 0.738.  
For work-family conflict, the 'worry' item was excluded, and the [PERSON]'s alpha value was 0.847. In musculoskeletal pain, sleep-related problems, and burnout, there were no excluded items, and the [PERSON]'s alpha values were above 0.7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Latent Variable** & **Initial Items** & **Removed Question Item** & **Final Items** & **Cronbach’s Alpha** \\ \hline Work environment & 15 & 10 & 5 & 0.738 \\ Work–family conflict & 5 & 1 & 4 & 0.847 \\ Musculoskeletal pain & 3 & 3 & 0.758 \\ Sleep-related problems & 3 & 3 & 0.875 \\ Burnout & 2 & 2 & 0.891 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Final reliability analysis results using [PERSON]’s alpha.  
The final results of the reliability and factor analyses of the subjective survey questions are presented in Table 2. According to factor analysis using Varimax rotation, the subjective questionnaire items were grouped into five factors: (1) work-family conflict, (2) work environment, (3) sleep-related problems, (4) musculoskeletal pain, and (5) burnout. [PERSON]'s test was statistically significant (\(p<0.001\)), and the Kaiser-Meyer-Olkin (KMO) test also yielded a significant result (\(0.765>\) criteria = 0.60).
## 5 Conclusions  
This study, using the FLUXNET2015 dataset, examined the spatiotemporal variations in \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) and identified the primary environmental factors driving these variations. Our results show that \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) were generally strongly correlated spatially. The correlation was weakest in mixed forests and strongest in broadleaf and needleleaf forests. While \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) displayed notable variation within the same ecosystem, no significant differences were observed. In contrast, significant differences were found across ecosystems, with marked variability in their magnitudes. For instance, shrubland had the highest \(S_{\text{GPP}}\) values, while needleleaf forest had the highest \(S_{\text{ER}}\) values. Temporally, \(S_{\text{ER}}\) showed more pronounced changes than \(S_{\text{GPP}}\). Ecosystem temporal trends varied: shrubland displayed an increasing trend for both metrics, grassland showed a decreasing trend in both \(S_{\text{GPP}}\) and \(S_{\text{ER}}\), and forest maintained stable \(S_{\text{GPP}}\) but showed a downward trend in \(S_{\text{ER}}\). Moreover, \(S_{\text{GPP}}\) exhibited a clear non-linear response to changes in the aridity index (AI), with both metrics rapidly declining before stabilizing. However, \(S_{\text{ER}}\) had a broader adaptive range to precipitation changes. Overall, this study improves our understanding of the spatiotemporal variations in ecosystem carbon dioxide fluxes under changing precipitation patterns.  
Methodology, M.X., K.D., Z.F., Z.H. and C.J.; Software, W.Z., W.C., M.X., K.D., M.F., L.W., M.W., W.Y., H.X., J.C. and Z.F.; Validation, M.X. and M.F.; Formal analysis, M.F.; Resources, K.D., M.W., W.Y. and Z.F.; Data curation, W.Z., W.C., L.W., W.Y., H.X. and J.C.; Writing--original draft, W.Z. and W.C.; Writing--review & editing, Z.H. and C.J.; Visualization, Z.H.; Supervision, C.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019 QZKK0405), the National Natural Science Foundation of China (Grant No. 62472216), the Hainan Provincial Natural Science Foundation of China (Grant No. 423 RC432), and the Start-up Fund for New Talented Researchers of Nanjing Vocational University of Industry Technology (Grant No. YK23-08-01). This work was also promoted by the U.S.-China Carbon Consortium (USCCC).  
**Data Availability Statement:** The raw data are available upon request to the authors. Please contact [PERSON]. [PERSON] ([EMAIL_ADDRESS]) to access the data of this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### Principle of Doppler Radar Velocity Measurement  
The Doppler shift, a key parameter in radar measurements, relies on radar frequency and radial velocity. It is directly proportional to the radial velocity and inversely pro  
Figure 1: Flowchart of the RTK UAV-based radial velocity validation of the weather radar.  
portional to the radar wavelength. For a Doppler weather radar system operating at a frequency of \(f_{0}\), its wavelength (\(\lambda\)) is given by  
\[\lambda=\frac{c}{f_{0}} \tag{1}\]  
Radar radial velocity, often referred to as Doppler velocity (\(V_{r}\)), can be derived from measuring the Doppler shift. This is expressed as  
\[V_{r}=\frac{\lambda f_{d}}{2} \tag{2}\]  
Here, \(f_{d}\) represents the Doppler shift. When a target approaches the radar, the Doppler frequency is positive, indicating that the received signal's frequency is higher than the transmitted one. Conversely, when the target moves away, the Doppler frequency becomes negative.  
Additionally, Doppler velocity can be obtained from the rate of change of distance. This calculation is based on the echo phase difference between consecutive pulse pairs, as shown in Equation (3) [16].  
\[V_{\text{max}}=\frac{\Delta R}{T}=\frac{\lambda\Delta\varphi}{4\pi T}=\frac{ \lambda\Delta\varphi F}{4\pi} \tag{3}\]  
where \(F\) stands for pulse repetition frequency (PRF), \(T\) denotes pulse repetition time (PRT), and \(\Delta R\) represents the distance the target moves during \(T\).  
Considering the discretely sampled nature of radar data, \(\Delta\varphi\) can only take values from \(-\pi\) to \(\pi\). The maximum unambiguous velocity is given by [17]:  
\[V_{\text{max}}=\pm\frac{\lambda}{4T}=\pm\frac{\lambda F}{4} \tag{4}\]  
The designation \"+\" indicates movement away from the radar, whereas \"\(-\)\" indicates movement toward the radar.  
In pulse-based radar systems, a critical consideration is the pulse repetition time, in order to ensure that all echoes from one pulse return before transmitting the next. This time is crucial to avoid echoes overlapping, which could compromise ranging accuracy. The distance traveled by the electromagnetic wave before the next pulse emission, following the return of all echoes from the previous pulse, is termed the maximum unambiguous distance.  
\[R_{\text{max}}=\frac{c}{2F}=\frac{cT}{2} \tag{5}\]
## 3 Results and Discussion
### Optimum Treatment Parameters  
There is a pronounced improvement in the contact angle after TMS plasma treatment. The contact angle of the untreated sample is 0\({}^{\circ}\), whereas that of the plasma modified sample, under the optimum treatment parameters (amount of TMS = 0.2 mL; discharge power = 50 W; flow rate of helium = 12.5 LPM; jet distance = 10 mm; and treatment time = 30 s), is 135\({}^{\circ}\) (Figure 7 shows an example of contact angle of plasma-modified sample). By definition, a contact angle greater than 90\({}^{\circ}\) denotes a hydrophobic surface. Based on the experimental results, under the optimum treatment parameters, the surface of synthetic leather can be made hydrophobic.
## 6 SAR for Antennas on Human Body
### Different Techniques to Reduce SAR Value
#### 6.1.3 Reduction in Using Conductive Materials  
A reduced specific absorption rate is important in antenna design because radiation affects tissues. Adsorptive and conductive materials such as metals or carbon-based compounds can reflect electromagnetic waves and lower the SAR. By placing a conductive layer next to the antenna, the SAR can be decreased, directing radiation away from the user's head or body. Conductive coatings applied to an antenna to minimize SAR values can also reduce the electromagnetic field's interaction with surrounding objects. However, employing conductive materials could impact the antenna's functionality, changing its gain and emission pattern, including conductive foam, mesh, and fabric. Wire-mesh and multilayered shields can lower the SAR by shielding back radiation which does not affect the human body. Shields can be made of nickel, copper, or nickel-nickel [102].
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 2 Materials and Methods
### System Model
#### 2.1.1 Offloading Framework  
The computation offloading framework for GEO/LEO hybrid satellite networks is shown in Figure 1. The scenario consists of the GEO satellite network and the LEO satellite network. The on-board missions are generated by LEO satellite observations and are decided by the GEO satellites whether to offload or not, the allocated resources, and the offloading ratios. To guarantee QoS constraints for on-board missions, each LEO satellite can offload the missions entirely or partially to the GEO satellite for processing through inter-satellite links, while the remaining missions are processed on the LEO satellite. It should be clarified that we focus on the scenario of in-orbit mission processing within the framework of the GEO/LEO hybrid satellite networks. All missions in this paper are generated by LEO satellites and processed exclusively using the collaborative computational capabilities of GEO and LEO satellites. Therefore, we only consider the GEO and LEO satellite networks, with missions generated on satellites and not involving terrestrial terminals, and neither are they offloaded to terrestrial networks for processing.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Integrated BDS/GPS Point Positioning Results  
#### 4.2.1 Static Positioning Results  
In satellite positioning, the position dilution of precision (PDOP), which is an important indicator for measuring the ability of satellite positioning, is usually used to represent the quality of the satellite's geometric distribution. Generally, the more visible satellites there are, the better the satellite constellation structure, the better the satellite geometric distribution, the smaller the PDOP and the higher the positioning accuracy. The PDOP and the number of visible satellites during the experimental period are shown in Figure 7.  
The WLS and Kalman filter were performed on collected data, and the results are shown in Figure 8. The positioning errors of WLS and Kalman filter are presented in Table 1. The positioning results show that, compared to the result of WLS, Kalman filter is smoother. In terms of accuracy, the positioning root mean square error of WLS in the X-axis direction is slightly lower than the Kalman filter, while the positioning root mean square error of the Kalman filter in the Y-axis and Z-axis directions is slightly lower than the weighted least square method. One point is that the two methods have little difference in the accuracy of three-dimensional positioning, and the Kalman filter has slightly higher accuracy than WLS. In addition, the positioning error from 12:31 to 13:11 is obviously larger, but there is no significant change in PDOP and the number of visible satellites during this period. According to the analysis, this is due to the obvious large clock deviation of the BDS receiver during this period.  
Figure 6: The dynamic experimental environment.
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 2 Results and Discussion
### Statistical Analysis of the VOCs Obtained via Hydrodistillation  
The PCA results for VOCs of fresh and air-dried \(C\). _spongiosus_ obtained via hydrodistillation are shown in Figure 6a,b. The first two PCs described 76.3% of the initial data variability. The correlation loadings of the first two PCs (Figure 6a) showed correlations between germacrene D and gleenol, and between aliphatic compounds ((\(E\))-heptadec-8-ene, (\(Z\))-heptadec-3-ene and pentadecanl). Epi-bicyclosesquiphellandrene and cembra-4,7,11,15-tetraen-3-ol were the variables with the highest variable contributions, based on the correlations. The PC2 was associated with germacrene D, phytol, and pachydictyl A abundance in the samples. The score plot (Figure 6b) showed the position samples in the multivariate space of the first two PCs. There was a clear separation between June and July samples in the centre right part of the plot, and segregation between May and August samples.  
June and July samples, characterized with higher content of cembra-4,7,11,15-tetraen-3-ol and ethyl icosanoate, and lower percentage of germacrene D and gleenol were positioned in the right part of the plot. The vertical distribution between May and August samples was related to differences in germacrene D, gleenol, pachydictyl A, and phytol abundance. There were more differences in seasonal variation between fresh samples. The distribution was along the PC1 axis and cannot be related to one group of compounds but was in the relation to the abundance of epi-bicyclosesquiphellandrene, cembra-4,7,11,15-tetraen-3-ol, ethyl icosanoate and pentadecanl, while the distribution along the PC2 axis was related to terpen (germacrene D, phytol and pachydictyl A) abundance in the samples. No correlation was found between the compounds' content and temperature change.