esa-sceva/satcom-chunk-collection huggingface dataset:
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
###### Abstract
Reference [22] proposed an accelerometer-assisted spoofing detection algorithm, which compares the difference between accelerometer output and the GPS output to detect anomalies caused by spoofing interference. Reference [23] proposed a joint spatial consistency check method that judges whether the positioning solution meets the distance constraint to counter GNSS spoofing attacks according to the known position provided by the GNSS receiver. Reference [24] proposed a method that fuses the GNSS absolute positioning data with the data of the vehicle speed sensor, acceleration sensor, and steering wheel angle sensor so that it can still provide accurate vehicle positioning even when the GNSS signal is interrupted for a short period of time. Reference [25] proposed a method for predicting the position deviation of UAVs under GPS spoofing attacks based on innovative particle filters. The integrated architecture of GPS/Loran-C/INS improves the accuracy of UAV's true position prediction. These methods mentioned above all use measurement navigation information provided by auxiliary navigation sensors for signal attacking detection without increasing the user's hardware cost. However, when induced spoofing attacks occur, the calibration feedback information of the Kalman Filter system in these methods will be biased due to GNSS spoofing. Therefore, reference [26] proposed a MEMS-INS/GNSS tightly coupled spoofing identification method based on the estimation of the spoofing contour: it reconstructs and analyzes the spoofing distribution to predict GNSS attacks in the signal domain and effectively identifies and eliminates induced spoofing attacks. In response to the problems discussed above, the purpose of this article is to design a simple and effective navigation algorithm to detect and mitigate different kinds of GNSS attacks, including GNSS jamming or GNSS spoofing, and then obtain a credible navigation result in the positioning domain. Therefore, a credible navigation algorithm for GNSS attacks detection using an auxiliary sensor system (ACNA) is proposed. By monitoring the measurement information of the GNSS and the auxiliary navigation sensors in the location domain during the credible verification window, the ACNA method analyzes and determines whether the mobile terminal suffers a GNSS attack. According to the verifications, the algorithm can adaptively select an updated model for measurement correction, and output credible navigation results. Finally, through the data sets collected by the self-developed mobile terminal, the analysis and verification of the algorithm performance are verified.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 7 Discussion and Conclusions
\begin{tabular}{l l} LM & Lockheed Martin \\ LTE & Long-Term Evolution \\ M2M & Machine-to-Machine \\ MAC & Medium Access Layer \\ MEC & Multi-access Edge Computing \\ MEO & Medium Earth Orbit \\ MILSATOM & Military Satellite Communications \\ MPCQUIC & Multipath version of the Quick UDP Internet Connections Protocol \\ NB & Narrow Band \\ NGC & Next-Generation Core Network \\ NGCN & Next-Generation Core Network \\ NR & New Radio \\ NTN & Non-Terrestrial Network \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OSI & Open Systems Interconnection Model \\ PCF & Policy Control Function \\ PDCP & Packet Data Convergence Protocol \\ PER & Package Error Rate \\ PHY & Physical Layer \\ PL & Payload \\ PLMN & Public Land Mobile Network \\ PNT & Position, Navigation, Timing \\ RAN & Radio Access Network \\ RAR & Random-Access Response \\ RF & Radio Frequency \\ RN & Relay Node \\ RRC & Radio Resource Control \\ RTT & Round-Trip Time \\ SAT & Satellite \\ SATCOM & Satellite Communication \\ SD-WAN & Software-Defined Wide Area Networks \\ SES & Satellite Earth Stations and Systems \\ SMF & Session Management Function \\ SNR & Signal-to-Noise Ratio \\ SWAP & Size, Weight, and Power \\ TA & Timing Advance \\ TCP & Transport Control Protocol \\ THEF & Trucking and Head-End Feed \\ TN & Terrestrial Network \\ TR & Technical Report \\ TRANSEC & Transmission Security \\ UAV & Unmanned Aerial Vehicle \\ UDM & Unified Data Management \\ UDP & User Datagram Protocol \\ UE & User Equipment \\ Urllc & Ultra-Reliable and Ultralow Latency Communications \\ UPF & User Plane Function \\ UT & User Terminal \\ V2I & Vehicle-to-Infrastructure \\ V2V & Vehicle-to-Vehicle \\ VDC & Virtual Data Center \\ VLEO & Very Low Earth Orbit \\ VNF & Virtualization of Network Functions \\ VPN & Virtual Private Network \\ VSAT & Very Small Aperture Terminal \\ VSNF & Video-Segment scheduling Network Function \\ WAN & Wide Area Network \\ \end{tabular}
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 3 Results
### Discussion on Practical Application  
In addition to studying the performance of AFGN on three real datasets, it is also worth exploring its practicality in real-world tasks. For example, in medical image classification tasks, medical imaging data is often scarce, especially annotated data, and the labeling process by medical professionals is complex and time-consuming. The modeling of non-linear dependency relationships within image features by AFGN can capture the differences among diseases, effectively enhancing feature expression capability, and improving classification performance. In addition, in satellite image classification tasks, the cost of acquiring satellite images is high, data is scarce, and the relationships between objects within the images (such as farmland, buildings, and vegetation) are complex. AFGN can capture the complex dependency relationships between these objects, constructing a suitable graph for the model. The filters designed in the model can effectively aggregate discriminative information from neighboring nodes, thereby enhancing classification accuracy. Therefore, the method we proposed can effectively address certain practical scenario tasks.
## 4 Discussion
### Consistency of Latitude Variation in Vegetation Phenology  
When phenology extraction is based on remote sensing image data, the differences in the extraction methods used are one of the main reasons for the change in accuracy [19], as individual regions have different spatial heterogeneity changes in phenology. Therefore, studying similarities and differences in vegetation phenology under a variety of extraction methods and latitudes plays an important role in accurately extracting regional phenology parameters [15; 48; 49].  
We found that the number of pixels under the five extraction methods was relatively small above 80\({}^{\circ}\)N, making it difficult to evaluate the phenological trend in these regions. The SOS of different vegetation types under the five methods used in this study shows an advancing trend at different latitudes. Among them, the advancing trend of the SOS is more significant between 50\({}^{\circ}\)N and 70\({}^{\circ}\)N (_p_ < 0.05), likely due to spring advancements being more significant in the mid-high latitudes [1; 24; 51; 52]. However, the SOS differs according to latitude. This may be because different vegetation types affect the SOS of the five extraction methods (Figure 6). Moreover, for most land cover types, the EOS shows mainly a delayed trend from 30\({}^{\circ}\)N to 60\({}^{\circ}\)N, which is the same as [PERSON] et al.'s results [15]. However, other than for Method 5, the vegetation phenological trend above 60\({}^{\circ}\)N is primarily an advancing trend, which is consistent with [PERSON] et al.'s finding [2]. The dissimilarities in these results may be due to the use of different image datasets and time periods [41; 53]. Therefore, when extracting vegetation phenological parameters in the future, the effects of different vegetation types on the extraction methods should be fully considered (Figure 7), and the appropriate extraction methods should be selected according to the characteristics of the study area.
## 2 The Background of Copernicus Services and the Workshops  
### The Copernicus Services  
Copernicus's services (CS) are a dedicated component of the Copernicus program, focused on monitoring and forecasting various aspects of Earth's systems. They are managed by the EC in cooperation with the ESA and other key institutions such as: the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), the European Centre for Medium-Range Weather Forecasts (ECMWF), the European Environment Agency (EEA), and the European Union Satellite Centre (SatCen). These services provide specialized products derived from the processing of data from the Sentinel satellite constellation, other contributing missions, and in situ observations. This actionable information offers insights into the atmosphere (CAMS) [23], marine (CMEMS) [24] and land (CLMS) [25] environments, as well as climate change (C3S) [26], emergency management (CEMS) [27], and security (CSS) [28]. With the exception of the CSS, all services are freely accessible, supporting environmental monitoring, climate change mitigation, and public policy, ultimately enhancing the quality of life globally.
# Experimental and Theoretical Study on Anchorage Loss of Prestressed CFRP-Reinforced Concrete Beams
## 1 Introduction
[27] developed a BFRP composite wedge anchor with 85% efficiency. At an axial force of 65 kN, no slippage occurred. In concrete beam reinforcement tests, a pre-tensioning load 1.05 times the predetermined value was applied to compensate for prestress losses. The existing method for tensioning externally prestressed CFRP tendons in concrete beams involves tensioning the anchoring device and then fixing it with a nut, which differs from the tensioning method of steel strands. This method results in much smaller tendon shrinkage values compared with steel strands. However, the anchoring deformation values used for calculating prestressed CFRP tendon anchorage loss mostly adopt those for prestressed steel bars or steel strand clamp-type anchors, deviating significantly from actual conditions. There is also a lack of testing data on the anchorage deformation of prestressed CFRP tendons, leading to less accurate existing calculation methods for CFRP tendon anchorage loss. Therefore, experimental measurements of the anchorage deformation of prestressed CFRP tendons and establishing an anchorage loss assessment method aligned with actual engineering conditions are necessary for more accurate evaluations. To investigate the influencing factors of anchorage loss and the magnitude of anchoring deformation, finite element method (FEM) models and a theoretical calculation system (TCS) were developed based on the loss mechanism. Figure 1 illustrates the overall research process, including the development of FEM models, the establishment of the TCS, and the subsequent steps involved. These simulations and theoretical calculations determined the magnitude of anchorage deformation. Subsequently, practical bridge tests were conducted according to the established model to obtain actual anchorage loss values, total deformation, and the proportion of anchorage loss in total loss under long-term loads. Control variable studies were conducted on the influencing factors to understand their impact on anchorage loss. Based on the experimental results, corrections were made to the errors in the theoretical calculations, providing references for the calculation of anchorage loss in different practical engineering scenarios.
## 4 Discussion  
### Discussion of Findings  
#### 4.1.1 Discussion of Simulation Results  
The simulation phase validated the system's measurement accuracy using a coupled 100 Hz AC signal under non-integer-period sampling conditions (1/3-second duration; 1667 samples; 33.3 signal cycles). Two methods were employed: Fast Fourier Transform (FFT) and Moving Sine Fitting (MSF). Direct FFT analysis yielded a primary frequency of 101.98 Hz, with an error of 1.98%, attributed to spectral leakage caused by non-integer-period sampling [41]. In contrast, the MSF method, incorporating phase continuity restora  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Value** & **Threshold** & **Compliance** \\ \hline Mean & 2069.4 & - & - \\ Skewness (\(\gamma_{1}\)) & +0.65 & [\(-\)0.5, +0.5] & Yes \\ Kurtosis (\(\gamma_{2}\)) & \(-\)0.42 & [\(-\)1.0, +1.0] & Yes \\ Outliers Detected & 4.5\% & \(<\)5\% & Yes \\ Shapiro–Wilk & 0.028 & \(>\)0.05 & No \\ \(p\)-value & & & \\ \hline \hline \end{tabular} Note: “-” denotes no threshold or compliance requirement.  
\end{table}
Table 4: Verification results of Real-Vehicle data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual Speed/(r/min)** & **Calculated/(r/min)** & **Asolute Error/(\%)** \\ \hline
800 & 801 & 0.13 \\
1200 & 1196 & 0.33 \\
1500 & 1505 & 0.33 \\
2000 & 1992 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Magnification calibration.  
tion and local fitting, reduced the error to 0.06% (100.06 Hz) by mitigating spectral leakage through phase compensation. Compared to traditional Hall-effect sensors, which achieve \(\pm\)0.2% accuracy but require mechanical coupling and are susceptible to electromagnetic interference [42], the proposed MSF method demonstrates superior adaptability to non-stationary signals and noise resilience, despite its moderate computational complexity. This aligns with the findings of [PERSON] et al. (2022), who reported that phase-compensated algorithms can reduce frequency errors to below 0.1% in similar applications [43].
## 4 Experiments and Results  
### Experiment Setup  
The encoder-decoder models with a multi-layer structure were evaluated on the MovingMNIST, KTH dataset, which are a spatiotemporal dataset. We compared four different models, three ConvLSTMs and one SA-ConvLSTM, each with a varying number of layers, using the models to predict future videos at different time steps, thereby comparing the similarity of the predicted video frames with the ground truth to assess accuracy. This was performed using MSE, mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) for quantitative and qualitative visual comparisons. Experiments on this dataset yielded the following results:  
* The SA-ConvLSTM is more effective than ConvLSTM in learning spatiotemporal data.
* A multi-layer encoder-decoder structure offers better results than a single-layer encoder-decoder structure.  
In this study, we used the GPU Cloud provided by Runpod (Dover, DE, USA). The CPU specs provided by Runpod included two AMD EPYC processors with 48 cores at 3.6 GHz and 283 GB of RAM. The GPU is an AMD Instinct(tm) MI300X with 192 GB of memory. The environment was configured using Python version 3.10.14 and PyTorch 2.4.0. Our model was implemented using the Python and PyTorch frameworks. Table 1 represents system specifications for our experiments.  
We predicted the next frame and fed it back to the network to obtain the next frame, and repeated this for a total of 10 predicted frames. For each model, the Adam optimizer was used with a learning rate of 0.001, and the hidden state size for both ConvLSTM and SA-ConvLSTM was set to 40. For the MovingMNIST dataset, we set the batch size to 2, and for the KTH dataset, we set it to 4, and the MSE loss was employed as the loss function for training. The results were evaluated after 20 epochs.
## 1 Introduction
The practical application of large-scale silver nanowires with high optical transparency, large electrical conductivity and strong mechanical durability is still an open problem, which was effectively solved in 2020 by a process of combining screen printing of Ag NWs with flash sintering (FLS), and the technology was used to realize the design of a new type of antenna for 5G applications [13]. Compared to nanowire, ultra-thin metal films are of higher electrical conductivity and more convenient to manipulate. In [14], the analysis of the results of a dual-band CPW-fed transparent antenna fabricated from AgHT-8 thin film indicated a strong correlation between measurement and simulation at both operating frequencies, demonstrating the usability of ultra-thin metal films for glass antenna fabrication. A significant analysis and discussion on the subject of applying metal grid for antenna applications was proposed in [15], where a glass antenna working at 24 GHz was presented. In addition, micro-copper mesh with its excellent electrical properties has a variety of applications not only in the antenna field but also in frequency selective surfaces [16]. The use of optically transparent glass substrates for energy harvesting applications is of great importance. Several antennas based on glass substrates for infrared energy harvesting applications at 30 THz was presented in [17]. In a more recent case, a broadband fully transparent antenna used for powering low power devices for the Internet of Things was studied by [PERSON] in 2019 [18]. In this design, glass and indium tin oxide were employed as the substrate and transparent conductor, respectively. In a different case, [PERSON] et al. investigated plain silica glass as low cost and transparent ground station substrates to reduce cost for space solar power application [19]. In this study, we further investigate the implementation of glass antennas for X-band application and energy harvesting. Four microstrip glass antennas operating at \(\sim\)8 GHz were designed, fabricated and verified in experiment with various metallization techniques, including conductive copper foil (CCF), conductive silver paste (CSP) and ITO thin film. The reflection coefficients and radiation patterns of the fabricated glass antennas are compared and discussed, providing a guiding direction for the better realization of glass antennas in the future.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods  
### Vegetation Growth Dynamic Model  
In order to discover what factors we need to attribute in vegetation analysis, we apply the vegetation dynamic model mainly based on [PERSON] 2008 [24]. This model describes the vegetation dynamic in dryland areas. Vegetation patterning is generally linked to the mechanism by rainfall infiltrates into soil in combination with low annual rainfall climate conditions [25]. Here, we have a briefly review of the model. The system mainly includes three parts: surface water (\(S\)), soil water (\(W\)) and plant density (\(P\)). Furthermore, plant growth takes the effects of CO\({}_{2}\) fertilization and land use into consideration. The dynamic of water density and vegetation biomass can be modeled in Equation (1):  
\[\begin{cases}\frac{dS}{dt}=R-\alpha S\frac{P+k_{1}W_{o}}{P+k_{2}},\\ \frac{dW}{dt}=\alpha S\frac{P+k_{2}W_{o}}{P+k_{2}}-\beta\frac{W}{W+k_{1}}P-\ r_{w}W,\\ \frac{dP}{dt}=c\beta\frac{W}{W+k_{1}}P-IP.\end{cases} \tag{1}\]  
In the dynamic of surface water, \(\frac{dS}{dt}\) is the distribution of surface water. R (mm/day) is the rainfall. The second term represents the amount of water infiltrating into soil. It is based on the assumption that the infiltration of water is according to the plant density of one area. Transpiration explains the difference between saturated and actual specific humidity. In the dynamic of soil water \(\frac{dW}{dt}\), the first term is the infiltrated water from the surface. The second term is the effects of transpiration based on the theory of saturated and actual specific humidity and \(\beta\) is the transpiration coefficient. The third term represents water loss due to evaporation and drainage. In the vegetation biomass dynamic \(\frac{dP}{dt}\), the first term is the part of water absorbed by vegetation and the second term describes the effects of land use such as grazing on vegetation biomass. \(l\) is the land use rate. More descriptions about the parameters and model can be found in Appendix A and [PERSON] et al. (2008) [24]. The concentration of carbon dioxide is used the current value. The datasets including rainfall and concentration of carbon dioxide, etc., are described in the following section.  
Equilibrium points of the vegetation dynamic model can be estimate by setting three equations in system (1) equal to \(0\) (\(\frac{dS}{dt}=0\), \(\frac{dW}{dt}=0\) and \(\frac{dP}{dt}=0\)). The model has two types of equilibria: (1) one equilibrium has no vegetation (bare state: \(P^{*}=0\), \(W^{*}=\frac{R}{r_{w}},S^{*}=\frac{R}{\pi R_{0}}\)); and (2) one equilibrium corresponds to strictly positive vegetation biomass (\(W^{*}=\frac{k_{1}}{c\beta-l}\), \(S^{*}=\frac{R}{\alpha}\left(\frac{P^{*}+k_{2}}{P^{*}+k_{2}W_{0}}\right)\), \(P^{*}=\frac{(R-r_{w}W^{*})(W^{*}+k_{1})}{\beta W^{*}}\)). The dynamic of vegetation biomass, surface water and soil water is shown in Figure 1.  
Figure 1: Evolution of vegetation biomass, surface water and soil water. \(R=0.6\), \(l=0.15\), \(C_{a}=400\). Other parameters are shown in Appendix A.
# Three-Dimensional Formation Control for Robot Swarms
## 5 Covering the Sensing Holes Inside the User-Specified Shape
#### Analysis  
Suppose that \(M\) is sufficiently large to cover all sensing holes in the 3D shape. Theorems 1 and 2 are used to analyze Algorithm 3.  
This subsection analyzes the computational complexity of Algorithm 3. \(v_{i}\) finds an AR with a frontierPt, which has the smallest hop distance from \(v_{i}\). This search can be performed utilizing distributed BFS, whose computational complexity is \(O(1)\)[34]. Since \(i\) decreases from \(M\) to 1 in the worst case, the computational complexity of the loop in Algorithm 3 is \(O(M)\).  
**6. MATLAB Simulation Results**  
Table 1 summarized the parameters in our paper.  
\begin{table}
\begin{tabular}{c c} \hline \(Parameters\) & \(Values\) \\ \hline \(N\) (number of ARs) & 100 \\ \(r_{sc}\) (radius of a senseSphere) & 50 (m) \\ \(S_{i}\) (AR’s maximum speed) & 5 (m/s) \\ \(Q\) (number of rays surrounding an AR) & 400 \\ \(dt\) (sampling interval) & 1 (s) \\ \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.  
Initially, the leader \(u_{1}\) is located at the origin. We simulate the case where ARs are randomly deployed inside the restricted space with size \(70\times 70\times 70\) in meters. Thus, the \(i\)-th AR (\(i\in\{2,\ldots,N\}\)) is located randomly as follows:  
\[\mathbf{u}_{i}=[70\times rand,70\times rand,70\times rand]^{T}. \tag{9}\]  
Here, \(rand\) returns a random number in the interval [0,1].  
Under the rendezvous controls in Section 4.2, we first make all ARs rendezvous at the leader located at the origin. Thereafter, Algorithm 1 is applied to make the ARs cover the user-specified 3D shape.  
In practice, there is process noise in the the process model of \(u_{i}\) (5). Therefore, instead of (5), \(u_{i}\) maneuvers under  
\[\mathbf{u}_{i}(k+1)=\mathbf{u}_{i}(k)+\mathbf{V}_{i}(k)\times dt+[N\times randn,N\times randn,N\times randn]^{T}. \tag{10}\]  
Here, \(N\) is the strength of the process noise. We use \(N=1\) meter. In (10), \(randn\) returns a value with zero mean Gaussian noise with standard deviation 1.