esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Verification
### Availability Aspect
#### 4.2.2 Communication Method/Step for Encryption Key Recovery  
Theory  
In the event of packet loss, \(\text{Device}_{\text{sensor}}\) and \(\text{Device}_{\text{Gateway}}\) generate the same encryption key through asynchronous 1 step communication.  
Proof  
1. In the event of packet loss, \(\text{Device}_{\text{sensor}}\) and \(\text{Device}_{\text{Gateway}}\) are asynchronous or synchronous if there is a request/response for recovery.
2. Communication Steps for \(\text{Device}_{\text{sensor}}\) and \(\text{Device}_{\text{Gateway}}\) to recovery in the event of packet loss.
3. \(\text{Device}_{\text{sensor}}\) and the \(\text{Device}_{\text{gateway}}\) generates the same encryption key as (1) (2).  
(1) In case of; \(\text{Device}_{\text{sensor}}\) sends \(\text{Frequency}\text{Table}_{\text{sensor}}\) and \(\text{TargetValue}_{\text{sensor}}\) to key generation notification if \(\text{BlockCondition}_{\text{K}}\) is satisfied considering packet loss. It is an asynchronous method because there is no response to the loss of packets from \(\text{Device}_{\text{Gateway}}\). \(\text{Device}_{\text{gateway}}\) generates the same encryption key from \(\text{Frequency}\text{Table}_{\text{sensor}}\), \(\text{TargetValue}_{\text{sensor}}\) even if loss packet. (2),(3) In conclusion, \(\text{Device}_{\text{sensor}}\) and \(\text{Device}_{\text{gateway}}\) generate the same encryption key through asynchronous 1 step communication for recovery.
# Genesis and Magmatic Evolution of the Gejiu Complex in Southeastern Yunnan, China
## 5 Discussion
### Genesis of the Gejiu Complex
#### 5.2.3 Genesis of Intermediate Rocks (Diorite and Monzonite)  
The genetic model for intermediate rocks is similar to that of gabbro, with two primary mechanisms proposed in previous studies [59; 60; 61]: (1) partial melting of mafic-to-intermediate rocks in the lower crust, and (2) crustal contamination of mantle-derived mafic magmas. Research has shown that magmas derived from the partial melting of felsic crust typically exhibit high SiO\({}_{2}\) and low MgO contents [57]. In contrast, the Gejiu intermediate rocks were characterized by relatively low SiO\({}_{2}\), high MgO, and enriched Cr and Ni. Their Nb/Ta ratios (average: 25.55) were close to those of primitive mantle and chondrites (17.5 \(\pm\) 2.0), significantly differing from rocks formed by crustal partial melting, indicating a mantle-derived source. Additionally, the enrichment of Ba and light rare earth elements (LREEs), as well as the crystallization of K-feldspar and plagioclase (Figure 3), suggests the incorporation of crustal components. Therefore, the Gejiu intermediate rocks are proposed to have originated from mantle-derived mafic magmas that experienced significant crustal contamination.  
[PERSON] (1989) [62] proposed that basaltic mafic magmatism can trigger the generation of felsic magmas in the lower crust, accompanied by assimilation and mixing processes.  
[PERSON] and [PERSON] [63] demonstrated that the interaction of felsic melts with peridotite can form orthopyroxene-rich reaction zones. [PERSON]'s study [57; 61] of the Cortlandt complex further confirmed that even at temperatures below 900 \({}^{\circ}\)C, mantle-derived basaltic magmas can induce partial melting of lower crustal rocks, forming hybrid monzodiorite. Therefore, it is inferred that the Gejuu intermediate rocks resulted from the selective contamination of mantle-derived mafic magmas by crustal felsic magmas. During the Early Paleozoic, the subduction of oceanic slabs led to the formation of juvenile lower crust, which reacted with basaltic magmas, causing widespread partial melting of lower crustal rocks. This process ultimately formed the Gejuu diorite and monzonite, a genesis consistent with that of the monzonite in the Cortlandt complex of New York [60; 61].
## 2 Related Works and the Key Contribution of This Paper
### Key Contributions of This Paper  
1. Combination of HM and IL-FEC: This paper specifically integrates HQAM and IL-FEC to address the unique dual-layer data structure of 3D video in the MVD format. This combination provides differentiated protection levels for color and depth data, which is essential for maintaining the visual quality of 3D video under various transmission conditions.
2. Appropriate Hierarchical Value (\(\alpha\)): The study uniquely explores the impact of a different \(\alpha\) value in hierarchical modulation on video quality, providing a systematic way to adjust the value to achieve appropriate SSIM in different relay settings.
3. Efficient Use of Relay Resources: By reducing the number of relayed bitstreams by half, the proposed method improves relay efficiency without compromising video quality, which is a significant advancement over previous methods.
4. Comprehensive Performance Evaluation: The use of both SSIM and BER as performance metrics offers a holistic view of the method's effectiveness in ensuring high-quality 3D video transmission.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 6 Discussion and Conclusions  
This paper proposed AD-FG, a cross-domain federated graph representation learning approach for network traffic anomaly detection which effectively addresses the limitations of traditional methods in structural modeling and privacy preservation. The proposed method integrates degree and random walk structural encoding approaches and feature-structure decoupled graph neural network design to explicitly represent and accurately capture structural knowledge to efficiently learn graph structural representations in network traffic in a distributed environment. Unlike traditional approaches, AD-FG shares only structural encoders in a federated learning framework, which avoids the interference of feature information and protects data privacy at the same time. In addition, under the cross-domain knowledge learning module of the server, the client can learn domain-independent structural knowledge, which enhances the global learning capability and improves the generalization ability and robustness of the model. The experimental results demonstrate the effectiveness of the proposed method in distributed network traffic anomaly detection, showing obvious advantages over existing approaches.  
Despite its promising results, AD-FG still has certain limitations. First, the experiments utilized the CIC-IDS 2017 dataset for graph construction. Although the dataset contains multiple types of attacks, the traffic data may not fully capture the complexity and diversity of real-world network environments. Second, the current method constructs static graphs using fixed time windows, which may not comprehensively capture temporal dependencies in dynamic network scenarios. Future work may explore dynamic graph modeling to enhance real-time adaptability. Moreover, we acknowledge the potential domain mismatch between public graph datasets and real-world network traffic graphs. While our method demonstrates robustness, future work could explore domain adaptation techniques or leverage synthetic graph generators tailored to network-specific properties to further reduce this gap.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, Z.L. and J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a key project of the National Natural Science Foundation of China (U22B2057).  
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 declare no conflicts of interest.
## 2 Materials and Methods
### Evaluation of Classification Performance
#### 2.10.2 Comparisons against USDA G-REALM  
The RadWet-L-derived estimates of inundation extent were compared against water level height data from the USDA Global Reservoirs and Lakes Monitor (G-REALM) lake/reservoir monitoring programme [58] at six locations within the central Amazon region. As G-REALM measures lake levels through radar altimetry, to make a comparison between G-realm and the RadWet-L classified products the upslope contributing area for each lake was defined using HydroSheds level 6 and 7 sub-basins [45]. These contributing areas shown in Figure 5 are then used to mask classified outputs where the middle date of ALOS-2 PALSAR-2 ScanSAR acquisition is within 10 days of the G-REALM lake water height measurement. Pearson correlation was used to quantify the relationship between classified sub-basin total wetted area (sum of inundated forest and open water) and lake water height.
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 3 Data Correlation Analysis
All factors, except the average depth of sewer pipelines, the average depth and age of communication pipes, the average depth of heating pipelines, and the average depth of gas pipes, were found to be significantly correlated with ground subsidence among the attribute information of underground utilities. However, most influencing factors (attribute information) did not show a high correlation with ground subsidence. This was because ground subsidence occurred due to complex causes, such as damage to underground utilities, disturbance of the surrounding ground by excavation, and poor compaction [21]. As a result, it is desirable to predict the vulnerability of ground subsidence in urban areas using influencing factors of ground subsidence as much as possible. However, as it was difficult to collect such diverse data, only the attribute information of underground utilities was used as data in this study. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Ground Subsidence** \\ \hline Water pipeline presence & 0.252 *** \\ Water pipeline density & 0.221 *** \\ Water pipeline diameter & 0.071 *** \\ Water pipeline average depth & 0.023 *** \\ Water pipeline age & 0.088 *** \\ Sewer pipeline presence & 0.248 *** \\ Sewer pipeline density & 0.237 *** \\ Sewer pipeline diameter & 0.056 *** \\ Sewer pipeline average depth & \(-\)0.058 *** \\ Sewer pipeline age & \(-\)0.006 \\ Power cable presence & 0.254 *** \\ Power cable density & 0.201 *** \\ Power cable diameter & 0.073 *** \\ Power cable average depth & \(-\)0.044 *** \\ Power cable age & 0.084 *** \\ Communication pipe presence & 0.272 *** \\ Communication pipe density & 0.238 *** \\ Communication pipe diameter & 0.079 *** \\ Communication pipe average depth & 0.006 \\ Communication pipe age & 0.002 \\ Heating pipeline presence & 0.039 *** \\ Heating pipeline density & \(-\)0.002 \\ Heating pipeline diameter & 0.075 *** \\ Heating pipeline average depth & \(-\)0.006 \\ Heating pipeline age & \(-\)0.038 ** \\ \hline \end{tabular}
\end{table} 
Table 2: Results of Pearson correlation analysis.
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 1 Introduction
The most intended features were ease of operation with minimum actuators. However, it is difficult to design a mechanism which will exactly achieve desired motion regardless of the path shape. So, there should be an orientation toward a proper solution by means of which the required motion can be approximately realized. The most common mechanism studied is the four-bar linkage as the base of all mechanical linkage exoskeletons including function, motion, and path generation [31; 32; 33; 34; 35]. Higher-order linkages can be generated by adding one, two or three dyads to an initial four-bar mechanism [36]. Assur graphs were used by [PERSON] et al. [37] to obtain a group of available linkages with both revolute and prismatic pairs and mechanism inversion of planar mechanisms as well. From previous literature, there are two basic steps to design a planar exoskeleton mechanism; namely type synthesis followed by dimensional synthesis. However, there is no clear database of linkages that can be used to achieve a required number of angular outputs. [PERSON] et al. [20] used kinematic linkage diagrams to generate planar linkages for up to three angular outputs. In this study, a new graphical methodology is developed for type synthesis of exoskeletons to obtain a complete set of planar linkages with up to five angular outputs (i.e., utmost five outputs). The proposed methodology facilitates the process of selecting a proper linkage mechanism for a rehabilitation robot with simple, effective, and computerized procedures. The paper is organized as follows: Section 2 illustrates design basics of exoskeletons. In Section 3, a modified graph representation of a planar four-bar linkage is introduced. Type synthesis methods of planar linkages are discussed in Section 4. In Section 5, generation of linkages with multiple angular outputs based on a four-bar linkage is presented. Section 6 illustrates the results obtained by the proposed algorithm. Then, these results and some examples are discussed. Finally, Section 7 summarizes the conclusions of the paper.
# Vibration Response Law of Aircraft Taxing under Random Roughness Excitation
## 1 Introduction
[PERSON] and [PERSON] employed the equivalent linearization method to address the nonlinear parameters of the landing gear and examined random vibration during taxiing using the PSD method [22]. [PERSON] applied the pseudo-excitation method (PEM) to compute the dynamic response of unmanned aerial vehicle landing gear under non-stationary random excitation [23]. [PERSON] et al. obtained the vibration response of a two-degrees-of-freedom aircraft-runway coupling system under roughness excitation using the PEM [24]. However, most of these studies employed simplified aircraft models that were unable to capture the characteristics of a large wheelbase and significant spatial vibration of the aircraft. In this paper, we address the unique structural characteristics of asphalt and concrete pavements by establishing corresponding pavement roughness models using PSD. We derive the motion balance equations of a 6-DOF aircraft under roughness excitation following D'Alembert's principle. We then use the PEM, the method of instantaneous frequency response function with spatial frequency, and the Monte Carlo method to calculate the PSD distribution of acceleration at the center of gravity (CGA), acceleration at the pilot station (PSA), dynamic load coefficient of the nose landing gear (NGDLC), and dynamic load coefficient of the main landing gear (MGDLC) under various operating conditions. This approach allows us to reveal the vibration response laws of aircraft taxiing under different pavement types, motion attitudes, and stochastic structural parameters. The results can be applied to optimize aircraft design methods and improve the accuracy of pavement roughness evaluation.
## 3 Results
### Norovirus Quantification in Treated and Untreated Oyster Samples  
We determined the virucidal efficacy of the hydrolates by comparing the reduction of RNA concentration of NoV in oysters before and after treatment with respect to controls (without hydrolates). The results for the hydrolates of thyme and lemon at 1% are shown in Figures 1 and 2, respectively. After 24 h, the untreated negative control exhibited a natural  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. Peak** & **Compound** & **RI Cal.** & **RI Ref.** & **Content (\%) 1** \\ \hline
1 & Alpha-pinene & 937 & 939 & 1.25 \\
2 & Camphene & 952 & 953 & 0.31 \\
3 & Beta-pinene & 979 & 975 & 0.26 \\
4 & Beta-myrrene & 992 & 991 & 2.02 \\
5 & Alpha-phellandrene & 1005 & 1005 & 0.39 \\
6 & Cyrene & 1028 & 1026 & 11.23 \\
7 & Gamma-terpinene & 1063 & 1062 & 6.04 \\
8 & Linalool & 1103 & 1101 & 17.11 \\
9 & Carvacrol & 1315 & 1317 & 58.67 \\
10 & Caryophyllene oxide & 1593 & 1583 & 2.73 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Chemical composition of _Thymus serpyllium_ hydrolate. RI cal: calculated retention index; RI ref: retention index from the literature (source: Pherobase).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. Peak** & **Compound** & **RI Cal.** & **RI Ref.** & **Content (\%) 1** \\ \hline
1 & Alpha-pinene & 937 & 939 & 3.12 \\
2 & Beta-pinene & 981 & 975 & 20.60 \\
3 & Beta-myrrene & 992 & 991 & 2.72 \\
4 & Limonene & 1039 & 1031 & 53.45 \\
5 & Gamma-terpinene & 1065 & 1062 & 14.03 \\
6 & Terpinolene & 1090 & 1089 & 0.70 \\
7 & Linalool & 1100 & 1101 & 0.30 \\
8 & Terpinen-4-ol & 1181 & 1178 & 0.10 \\
9 & Alpha-terpineol & 1193 & 1189 & 0.35 \\
10 & Neral & 1245 & 1242 & 1.28 \\
11 & Geralial & 1274 & 1271 & 2.24 \\
12 & Beta-bisabolene & 1512 & 1509 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of _Citrus limon_ hydrolate. RI cal: calculated retention index; RI ref: retention index from the literature (source: Pherobase).  
decay of the NoV GII viral RNA by 26.8%, corresponding to a 0.14-log reduction. This reduction was not observed for NoV GI RNA (Figures 1 and 2). In oysters treated with 1% of C. _limon_ hydrosol for 24 h, the NoV concentration was significantly reduced (\(p\) = 0.0433), compared to the control, for both genogroup II (0.2 log reduction) and the sum of the two NoV genogroups (GI + GII) (0.15 log reduction), while no effect was observed on NoV GI concentration. Treatment with 1% thyme hydrosol for 24 h did not result in a reduction in NoV concentration compared with the untreated control for either genogroup.
# Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures
## 4 Proposed Thermal Properties Relationships
### Thermal Conductivity Equations  
Data generated from the thermal conductivity measurements are used to develop thermal conductivity relationships for LWC. This thermal property is expressed in the form of empirical relationships in the temperature range of 20-300 \({}^{\circ}\)C. A good relation was found between the experimental and the statistical model, as noted by R\({}^{2}\), whose value lay between 0.9 and 1.  
The test data clearly indicate that thermal conductivity is mainly influenced by the porosity of LWC and the temperature range. To reflect this trend, separate expressions are developed for thermal conductivity. Thermal conductivity relationships are presented in three temperature ranges: 20-100, 100-180, and 180-300 \({}^{\circ}\)C. These relations are presented in Equations (21)-(23):  
\[k(T) =\rho_{20^{\circ}\mathrm{C}}\times\left(9.4\cdot 10^{-4}\left[\frac{ \mathrm{Wm}^{2}}{\mathrm{kg}^{\circ}\mathrm{C}}\right]-10^{-6}\left[\frac{ \mathrm{Wm}^{2}}{\mathrm{kg}^{\circ}\mathrm{C}^{2}}\right]\times T(^{\circ} \mathrm{C})\right)\ \left(R^{2}=0.9\right) 20\leq T<100\ ^{\circ}\mathrm{C} \tag{21}\] \[k(T) =\rho_{20^{\circ}\mathrm{C}}\times\left(\frac{e^{-0.01\times T(^{ \circ}\mathrm{C})}}{425}\right)\left(R^{2}=0.97\right) 100\leq T<180\ ^{\circ}\mathrm{C}\] (22) \[k(T) =\rho_{20^{\circ}\mathrm{C}}\times\left(-3\cdot 10^{-7}\left[ \frac{\mathrm{Wm}^{2}}{\mathrm{kg}^{\circ}\mathrm{C}^{2}}\right]\times T(^{ \circ}\mathrm{C})-4.5\cdot 10^{-4}\left[\frac{\mathrm{Wm}^{2}}{\mathrm{kg}^{\circ} \mathrm{C}}\right]\right)\ \left(R^{2}=0.95\right) 180\leq T<300\ ^{\circ}\mathrm{C} \tag{23}\]  
where \(k(T)\) is the thermal conductivity of LWC as a function of temperature, \(\rho_{20^{\circ}\mathrm{C}}\) is the density of LWC at 20 \({}^{\circ}\)C, and \(T\) is the temperature in \({}^{\circ}\)C.  
Figure 11 shows the comparison between the experimental data, the fitted equation, and Eurocodes. Eurocodes underestimate thermal conductivity of LWC up to 150 \({}^{\circ}\)C. However, above 150 \({}^{\circ}\)C, Eurocodes overestimate this thermal property. The trendline for the fitted equation is close to the line depicting results from MTPS data and LFA data.
## 4 Experiment and Results
### Connection Scenario 1: Two Anchors  
In connection scenario 1, four anchor nodes and two target nodes are set up with each target node connecting to two anchor nodes, and the target nodes connecting to each other. Figure 4 depicts the location distribution (a), communication relationship between nodes (a), positioning error of various algorithms (b-e), and overall positioning error to constr(f). The four algorithms can achieve nice positioning and convergence after two iterations, as shown in Figure 4f.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 2 Datasets
### Reference MODIS Data  
Here, MOD02 QKM data (MODIS L1B product, downloaded from [[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 15 October 2022) are used for calculating the normalized difference vegetation index (_NDVI_) reference data. The reason for making the reference data by ourselves is that the daily resolution _NDVI_ products in the region of Taihu Lake are not easy to be found. The MODIS L1B product comes with geographic location information and radiometric calibration parameters; in this way, it can be preprocessed directly by the Georeference MODIS tool in ENVI 5.6. Red light band and near-infrared band are contained in MOD02 QKM data with the original spatial resolution of 250 m \(\times\) 250 m and in the daily resolution. Then, calculating the \(NDVI\) index by the two bands above is as follows:  
\[NDVI=\frac{NIR-R}{NIR+R}, \tag{1}\]  
where \(R\) refers to the reflectance in the red band and \(NIR\) refers to that in the near-infrared band. Pixels with a \(NDVI\) value > 0.05 will be regarded as occupied cyanobacteria bloom. When verifying the correctness of detection, if there are more than 15 bloom pixels in the 5 \(\times\) 5 pixels, the central pixel of which the CYGNSS data point is located, this CYGNSS data point is labeled as a cyanobacteria bloom point.
## 5 Experimental Results
### RFI Emitter-Tracking Scenario  
In this scenario, the high-degree nonlinear tracking filters were applied to estimate the position, velocity, and turn rate of RFI emitter based on extracted data from the Orolia Simulator. Figure 13 shows the emitter-tracking using nonlinear filtering, where Figure 14 presents the zoom of the trajectory and filter tracking. Figures 15-17 show the RMSEs of the position, velocity, and turn rate for the RFI emitter using the proposed filters.  
* **Important notes** It is important to mention that the RFI target-tracking problem in this work is different from other standard problems known in the literature because of the nature of the measurement. TDOA/FDOA measurement is highly nonlinear and different in range and bearing, and is very well analyzed and understood from the RADAR community. Indeed, as nonlinear degree becomes different, observability degree and analysis become different. It is traditionally important to develop particle-filtering methods for target-tracking problems; however, in this paper, we have restricted our analysis to nonlinear derivative-free algorithms such as CKF GHKF of different degrees, as these algorithms present more advantages compared to particle filters and sequential particle filters when speaking about real-time implementation feasibility.