esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal and System Model  
The signals received from an antenna of a GNSS receiver pass through the radio frequency (RF) front-end and are down-converted to intermediate frequency (IF). Before the ADC, they can be written as  
\[X_{IF}(t)=\sum_{l=1}^{N_{s}}S_{IFJ}(t)+J_{IF}(t)+N(t) \tag{1}\]  
where \(N(t)\) is the additive white Gaussian noise term with two-sided power spectral density \(N_{0}/2\) and zero mean, \(J_{IF}(t)\) is the jamming signal. \(N_{s}\) is the number of visible satellites, \(S_{IFJ}(t)\) is the signal transmitted by the \(l\)th satellite, which can be defined  
\[S_{IFJ}(t)=\sqrt{2A_{l}}C_{l}(t-\tau_{l})D_{l}(t-\tau_{l})\cos\Bigl{[}2\pi \Bigl{(}f_{IF}+f_{dJ}\Bigr{)}t+\varphi_{IFJ}\Bigr{]} \tag{2}\]  
where,  
* \(A_{l}\) is the received GNSS signal power from the \(l\)th satellite;
* \(C_{l}(t-\tau_{l})\) is the pseudorandom noise sequence, and \(\tau_{l}\) is the code phase delay;
* \(D_{l}(t-\tau_{l})\) is the navigation data message signal;
* \(f_{IF}\) is GNSS signal intermediate frequency;
* \(f_{dJ}\) is the Doppler-affected frequency;
* \(\varphi_{IFJ}\) is the carrier phase delay.  
The jamming signal \(J_{IF}(t)\) defined in Equation (1) is redefined  
\[J_{IF}(t)=\sum_{m=1}^{L}\sqrt{2A_{J,m}}\cos\Bigl{[}2\pi(f_{ins,m}(t))t+\theta _{J,m}\Bigr{]} \tag{3}\]  
* \(A_{I,m}\) is the power of the \(m\)th jamming signal;
* \(f_{ins,m}(t)\) is the \(m\)th jamming instantaneous frequency;
* \(\theta_{J,m}\) is the phase delay of the \(m\)th jamming signal;
* \(L\) is the number of interferences.  
For the linear chirp signal, the instantaneous frequency \(f_{ins,m}(t)\) linearly evolves with the time interval \(\Bigl{[}f_{begin},f_{end}\Bigr{]}\). Therefore, the \(f_{ins,m}(t)\) is written as follows  
\[f_{ins,m}(t)=f_{begin}+kt,\ 0\leq t\leq T_{j} \tag{4}\]where \(T_{j}\) is the sweep period, \(f_{begin}\) is the initial frequency, and \(k\) is the frequency change rate, also named chirp rate, defined as follows  
\[k=\frac{f_{end}-f_{begin}}{T_{j}}=\frac{B_{sweep}}{T_{j}} \tag{5}\]  
where \(B_{sweep}\) represents the sweep bandwidth. The \(m\)th jamming power to the \(l\)th GNSS signal power ratio (JSR) is written as follows  
\[JSR=10\log_{10}\left\{\frac{A_{l,m}}{A_{l}}\right\} \tag{6}\]  
The jamming power to noise power ratio (JNR) for the \(m\)th jamming signal is written as follows  
\[JNR=10\log_{10}\left\{\frac{A_{l,m}}{N_{0}B_{IF}}\right\} \tag{7}\]  
where \(B_{IF}\) is the RF front-end bandwidth. The analytical expression of the received signal in Equation (1) is defined as follows  
\[X_{a}(t)=X_{IF}(t)+j\hat{X}_{IF}(t) \tag{8}\]  
where the \(\hat{X}_{IF}(t)\) is the Hilbert transform of \(X_{IF}(t)\); the use of the analytic signal \(X_{a}(t)\) can avoid the presence of cross-terms which could be generated by the interaction between positive and negative frequency components [19].
## 4 Performance Analysis
### Results and Discussion
#### 4.3.1 NER Task Evaluation
However, different from the EDA, the MDA utilizes the medical knowledge graph to augment the data, which keeps the structure of sentences unchanged and strengthens the semantic information. Hence, the deep learning models with the MDA can effectively capture the features of sentences to extract entities. Moreover, the BITEmrNER dataset that is compared with the CCKS2019 dataset has complex electronic medical records. However, the results of the F1-score in Table 5 show that the EDA outperforms the original data in entity recognition by 0.88% and the MDA achieves 2.84% improvements in F1-score over all models. Hence, the MDA can alleviate the problem of identifying specialized vocabularies better than EDA.
## 11 Ethical AI Governance and Integrated Space-Air-Ground-Sea Networks in 6G
### The 6G Architecture and Space-Air-Ground-Sea Integrated Networks  
Notably, 6G envisions a fully integrated network architecture combining space, air, ground, and sea domains into a unified system to provide seamless, ubiquitous connectivity. This space-air-ground-sea (SAGS) network [289; 290] leverages satellites, aerial platforms, terrestrial infrastructure, and maritime communication systems to enable global coverage, ultra-low latency, and enhanced reliability. Space-based components like LEO satellites willextend connectivity to remote areas and support applications such as disaster management and IoT deployments. Aerial networks, including HAPs and UAVs, will offer dynamic and flexible coverage in emergency and rural scenarios, while advanced terrestrial infrastructure will ensure high-capacity data transmission in urban areas [291]. Maritime systems, such as smart buoys and underwater communication networks, will enable connectivity for shipping, offshore operations, and underwater exploration.  
The integration of these domains will rely on advanced AI-driven resource allocation, seamless handovers, and network slicing to meet diverse performance requirements. This unified framework will support innovative applications like autonomous transportation, large-scale environmental monitoring, and global industrial automation. For instance, autonomous vehicles can utilize combined satellite and terrestrial networks for reliable navigation, while maritime IoT and underwater systems will enhance data collection and monitoring in oceanic environments. By bridging traditional communication silos, SAGS networks will unlock new possibilities for 6G, ensuring robust connectivity across all terrains and scenarios.
## 3 Results
### Quantitative Assessment  
For the quantitative assessment of collagen imaging, the fiber brightness indexes (FBI) were calculated. We saw remarkably significant differences between AK lesions and control skin for both staining protocols. Regarding AK lesions, FCF staining exhibited a clearly higher FBI compared to regular arcidine orange staining (1.89 vs. 1.00; \(p<0.001\)). Calculating the FBI in healthy, non-sun-exposed control skin, we saw similar proportions, with an FBI of 1.98 for FCF and 1.24 for regular arcidine orange staining (\(p<0.001\)) (see Figure 5). These findings align with our visual observations in the EVCM images and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**EVCM FCF Fast Green**} \\  & & PRO I & PRO II & PRO III \\ \hline \multirow{4}{*}{FCF} & PRO I & 6 & 0 & 0 \\  & PRO II & 0 & 11 & 1 \\  & PRO III & 0 & 0 & 7 \\ \hline \multirow{4}{*}{FCF} & & \multicolumn{3}{c}{**EVCM Acridine Orange**} \\  & & PRO I & PRO II & PRO III \\ \hline \multirow{4}{*}{FCF} & PRO I & 6 & 0 & 0 \\  & PRO II & 0 & 8 & 1 \\ \cline{1-1}  & PRO III & 0 & 0 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion tables showing lesion-wise PRO score histological correlation for each staining protocol.  
Figure 3: PRO score assessment of actinic keratosis using digital HE–EVCM. The yellow star shows dermoepidermal junction site (DEJ) with intensified basophilic appearance of the crowding keratinocytes in PRO I (**a**), budding of the keratinocytes in PRO II (**b**), and prolonged undulations protruding into the upper dermis in PRO III (**c**).  
corroborate the reported \"in vitro\" findings between both staining protocols. Regarding PRO scoring or collagen fiber assessment, we saw no significant differences visible between different skin tones or the location of AKs when comparing FCF to acridine orange staining.
# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 3 Principles of LRF and 3D Map Building
### 3D Map Building  
The LRF sensor used by the mobile robot in this study transmits the distance and angle data to the data control platform, which generates x and y coordinates using the angle and distance data as expressed by Equations (2a) and (2b) and builds a 2D map [32; 33].  
\[x_{LRF}=D\times\cos(\theta_{LRF}) \tag{2a}\]  
\[y_{LRF}=D\times\sin(\theta_{LRF}) \tag{2b}\]  
where \(D\) denotes the LRF-to-object distance and \(\theta_{LRF}\) represents the angle of the LRF scan. LRF can scan a range of 1-240\({}^{\circ}\) and scan the surrounding area sequentially rotating by 0.36\({}^{\circ}\).  
The data coordinated by LRF do match the coordinate axis of the mobile robot. Thus, to overcome this problem, coordinate transformation is necessary to match the standard coordinate axis of the mobile robot. Figure 5 shows the coordinate transformation process in which the LRF 2D scan data undergo transformation to match the coordinate axis of the mobile robot. X0, Y0, and Z0 constitute the standard coordinates used for building maps with the coordinate axis of the mobile robot. X1, Y1, and Z1 are the LRF coordinates. The change in the coordinates of the mobile robot and position entails a change in the LRF's coordinate axis; in order to build a 3D map using the LRF-measured data, LRF data should be coordinated to match X0, Y0, and Z0. This requires the movement, rotation, and LRF coordinate axis data; further, Equation (3) using the rotation matrix is applied to implement the transformation of the LRF coordinates.  
\[\left[\begin{array}{c}x_{map}\\ y_{map}\\ z_{map}\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\theta&-\sin\theta\\ 0&\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}x_{Robot}\\ y_{Robot}\\ z_{Robot}\end{array}\right]+\left[\begin{array}{c}x_{LRF}\\ 0\\ y_{LRF}\end{array}\right] \tag{3}\]  
Figure 4: Road scan of LRF.  
Figure 3: Data processing of the LRF.  
where \(x_{Robot}\), \(y_{Robot}\), and \(z_{Robot}\) denote the mobile robot's coordinates; \(x_{LRF}\), \(y_{LRF}\) represent the coordinates of the data scanned by LRF; \(\theta\) denotes the rotational angle of the mobile robot's coordinates around X0; and \(x_{map}\), \(y_{map}\), \(z_{map}\) represent the coordinates of the points for building a map. The coordinates of the LRF data transformed with Equation (3) are used for building a 3D map through a continuous iteration.  
Figure 6 shows a 3D road map exhibiting the principles shown in Figures 4 and 5. This 3D road map represents the LRF-scanned test environment, wherein the red circle denotes a lane. The inset shows the test environment.
## 4 Applications of the Transient Regime
### Analysis of Products Using Transient Cuffp Signals
#### 4.2.2 Polymers  
Transient CWFP signals were used to simultaneously measure the T\({}_{1}\) and T\({}_{2}\) relaxation times during fast polymerization reaction (10 min) of commercial epoxy resin [44]. The advantage of CWFP to measure T\({}_{1}\) and T\({}_{2}\) over conventional methods is that both relaxation times can be measured in a single reaction. The standard T\({}_{1}\) and T\({}_{2}\) methods (inversion-recovery and CPMG) require the use of two separated reaction samples that are not ideal, as the two reactions did not have the exact same rates. [PERSON] et al. [28; 29] applied CWFP sequences to real-time monitoring of the polymerization crosslinking reaction of acrylamide-based hydrogels. The CP-CWFP results showed high sensitivity and time resolution when monitoring important parameters of radical polymerization. [PERSON] et al. [29] used CP-CWFP to study the polymerization of acrylamide using a redox-pair initiator system. They observed that T\({}_{1}\) was more sensitive than T\({}_{2}\) when monitoring the reaction, confirming it is a good probe to monitor monomer conversion in real time in an automated non-destructive fashion.  
[PERSON] et al. used the CP-CWFPx-x sequence to study the coagulation of chitosan (CS) in aqueous solution as a function of pH using T\({}_{1}\) and T\({}_{2}\) values [45]. They observed that the relaxation profile was dependent on CS concentration. The main relaxation differences were observed for the sample with the highest CS concentration of 2.2 g/L. At this concentration, both T\({}_{1}\) and T\({}_{2}\) were sensitive to the coagulation process at pH between 6 and 7. However, T\({}_{1}\) increases with pH and T\({}_{2}\) shows a remarkable decrease. This relaxation results suggest that CS is forming a supramolecular structure (gel) that entraps the water in confined regions, reducing water mobility.
## 3 Results and Discussion
### Comparison of Zr-EPRC Preparation Methods
#### 3.1.2 Determination of Optimum Loads  
As evident from Figures 3 and 4, the influence of zirconium on the modification of fly ash and steel slag is highly significant. As the mass ratio of zirconium chloride octahydrate increases gradually, the adsorption rate and capacity of both modified steel slag and modified fly ash initially exhibit a rapid increase, reaching their peak values at mass ratios of 0.4 and 0.6, respectively. This phenomenon can be attributed to the greater loading of zirconium ions in the form of active zirconium oxides and hydroxides onto the surface and pores of the adsorbent materials as the zirconium content rises. Consequently,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Residual Liquid State** & **Material State after Adsorption** \\ \hline Former EPRC & limpid & No anomalies \\ No. 1 Zr-EPRC & limpid & No anomalies \\ No. 2 Zr-EPRC & slight precipitation, turbidity & Chalking, surface peeling \\ No. 3 Zr-EPRC & limpid & No anomalies \\ \hline \hline \end{tabular}
\end{table}
Table 5: State of each group of materials after adsorption of phosphorus ions.  
Figure 2: Comparison of phosphorus removal effect of different modification methods.  
this results in an increased number of phosphorus adsorption sites, leading to a rapid enhancement in both the adsorption rate and capacity.  
However, with a further increase in the mass ratio of zirconium chloride octahydrate, a surplus of activated zirconium oxides and hydroxides begins to block the pores of the adsorbent material, impeding the diffusion of phosphorus within these pores. Consequently, this leads to a reduction in the overall adsorption capacity. Therefore, it is judicious to conduct the modification of fly ash and steel slag with zirconium chloride octahydrate at mass ratios of 0.4 and 0.6. This approach achieves a balanced loading of zirconium onto the adsorbent substrate, thereby enhancing the pore structure, improving the adsorption efficiency, and minimizing material wastage.
## 5 Conclusions and Future Work  
In this article, we proposed a secure and lightweight key management framework for UWSNs. The proposed framework addressed the challenges of UWSN key management, including limited bandwidth, high error rates, and dynamic topology. Our framework consisted of a key generation algorithm, key distribution mechanisms, key revocation mechanisms, and authentication mechanisms. We also emphasized the importance of lightweight implementation and scalability. The proposed key management framework is a robust and efficient solution for securing UWSNs. It provides a secure and scalable mechanism for managing keys, ensuring that the network is protected from various security threats. The lightweight implementation of the framework makes it suitable for deployment in resource-constrained underwater environments. In future work, our focus is on implementing the proposed framework on real-world UWSNs and conducting further evaluations to validate its effectiveness.  
Conceptualization, S.S. and A.W.; methodology, S.S.; software, A.S.; validation, A.M, S.S. and F.A.; formal analysis, M.M.; investigation, A.A.; resources, S.S.; data curation, S.S.; writing--original draft preparation, A.S.; writing--review and editing, F.A.; visualization, A.W.; supervision, A.M.; project administration, M.M.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Researchers Supporting Project number (RSP2023R476), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors declare no conflict of interest.
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
## 2 Architecture of the Active Phased Array Antenna
### Antenna Design  
The proposed antenna is an 8 by 8 subarray of double-stacked patch radiating elements working from 17.7 GHz to 20.2 GHz. The reason for using this structure is to increase the bandwidth of the antenna by stacking patches. Figure 3 presents the 8 \(\times\) 8 simulation model and the stack up of the structure. The passive antenna is simulated by the commercial software CST Studio Suite 2024; the following figure presents the stack-up of the multilayer antenna and the simulation model of the passive 8 \(\times\) 8 antenna with all the components, including the commercial connector Southwest End Launch (Tempe, AZ, USA). All substrates used in the design of the antenna are from the company from the company Rogers Corporation (Chandler, AZ, USA).The substrates are Rogers RO4350B, with a dissipation factor tan \(\delta\) of 0.0037 @10 GHz and a relative permittivity \(\varepsilon_{r}\) of 3.48. The 1 to 8 power divider is formed on a substrate whose thickness is 0.254 mm thick at the bottom, and metallic vias connect the feed network to the lower patch, as shown in Figure 3. The substrate on which the lower patch is printed is 0.504 mm thick. In order to prevent misalignment errors, this structure is adhered to using a 0.101 mm thin prepreg Rogers 2929, whose relative permittivity \(\varepsilon_{r}\) is 2.94 and dissipation factor tan \(\delta\) is 0.003 @10 GHz. The element spacing is 0.6 \(\lambda_{0}\) at the center frequency (18.95 GHz). The size of the top patch is 5.05 mm in diameter and the bottom patch is 4.4 mm in diameter. The feed network and the upper parasitic patch have been printed at the bottom layer of the substrate Rogers RO4350B, and a foam of 1 mm of height with a permittivity of 1.07 is slipped in between layers to control the separation between active and parasitic patches.  
Figure 1: Schematic of the active phased array architecture.  
Figure 2: Prototype of the active phased array in the anechoic chamber.  
Figure 4 shows the prototype of the array; the front of the antenna is shown in Figure 4a, while Figure 4b illustrates the reverse side of the antenna. As can be seen on the rear side of the antenna, the feed lines from the edge to the antennas are optimized in order to avoid phase difference between the different columns of the antenna subarray. As shown in Figure 4 The connector model used is the Southwest End Launch model (Tempe, AZ, USA). As the connectors are considerably large with respect to the antenna patches, the size of the ground plane has been increased to ensure that the connectors do not influence the radiation pattern of the array. The increased simulation model of passive antenna size is 142 mm \(\times\) 142 mm, as shown in Figure 3. The columns have been enumerated as illustrated in Figure 4.
## 4 Grounding Grid Imaging Results and Evaluation Analysis
### Analysis of Ground Grid Imaging Evaluation
This shows that the improvements made to the generator network in the CGAN architecture produce more accurate and reliable corrosion detection, and its attention module effectively extracts features, highlighting the effectiveness of this method in improving imaging accuracy and corrosion assessment accuracy, which is more conducive to the research of grounding grid corrosion diagnosis. This algorithm performs ground grid imaging after adding different Gaussian noises, as shown in Figure 15. Although noise is added to the data, the noise has little effect on the imaging quality, and the accuracy of the model only slightly decreases. This shows that even in the presence of noise, the detection method can still identify the corrosion area well, indicating that the model is noise robust to a certain extent, and the decrease in accuracy is small, indicating that the interference of noise in the imaging results does not significantly affect the final detection performance. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(A\) & \(R\) \\ \hline CNN & 85.2\% & 25.2\% \\ U-Net & 88.6\% & 18.6\% \\ U-Net-CBAM & 94.3\% & 9.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and diagnostic error of different algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(A\) & \(R\) \\ \hline
2 \(\times\) 3 (Single corrosion) & 96.3\% & 7.6\% \\
3 \(\times\) 3 (Single corrosion) & 95.5\% & 8.1\% \\
2 \(\times\) 3 (Double corrosion) & 91.2\% & 12.6\% \\
3 \(\times\) 3 (Double corrosion) & 88.7\% & 15.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-shot imaging accuracy and error of different models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model 1 (2 \(\times\) 3)**} & \multicolumn{2}{c}{**Model 2 (3 \(\times\) 3)**} \\ \hline  & \(S\) & \(P\) & \(S\) & \(P\) \\ \hline CNN & 0.8146 & 16.4524 & 0.8065 & 16.6654 \\ U-Net & 0.8476 & 18.1468 & 0.8654 & 18.4514 \\ U-Net-CBAM & 0.8975 & 20.4465 & 0.9044 & 19.6332 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation index table of different algorithms.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 3 Terminal-Side Mixing Technology
The conference-initiating terminal thus takes over the mixing task from the server, which is then only responsible for forwarding the voice data, achieving a truly end-to-end encrypted conference. Compared with homomorphic encryption, terminal-side mixing technology enables the use of different voice encoding methods based on the network environment. In high-bandwidth networks, the G.711 or Opus encoding formats can be used, while in low-bandwidth environments such as satellite networks, the Lyra encoding is used as it requires extremely low bandwidth. This approach enables calls even without mobile networks and Wi-Fi, reducing the CPU load on mobile devices. Furthermore, since each mobile terminal and the conference-initiating terminal have different keys, only the new member needs to perform the key exchange when joining a meeting in progress. This effectively handles and improves the issues associated with homomorphic encryption in group calls, creating a superior end-to-end group calling environment.