esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
## 2 Antenna Design and Analysis
### Working Mechanism
Figure 4: Evolution toward to the proposed DBDCP DRA. Figure 3: Simulated results of (**a**) \(|S_{11}|\) and (**b**) \(|S_{22}|\) of the dual-band DRA design. To clearly understand how the proposed antenna realizes dual-CP operation, the \(E\)-field distributions inside the main DR are analyzed. For a clear observation, the diagonal intersecting planes are introduced consisting of \(x\)'\(z\)'- and \(y\)'\(z\)'-plane since the main DR is arranged diagonally relative to \(x\)- and \(y\)-axes. The \(x\)'\(y\)'-plane is located at the top surface of the main DR. Figure 6 depicts the simulated results of the \(E\)-field distributions for two different angular times at 4.0 GHz with excitation from P1. Two orthogonal modes, namely TE\({}_{111}^{y^{\prime}}\) and TE\({}_{111}^{y^{\prime}}\) modes, are excited simultaneously at 4.0 GHz. Owing to the diagonal arrangement of the main DR, the excitation from the P1 through the cross-slot-coupled aperture effectively excites the fundamental TE\({}_{111}\) mode of the main DR and splits it into near-degenerate orthogonal modes. Furthermore, the \(Esum\)-field (vector sum of the main \(E\)-field distributions) in the \(x\)'\(y\)'-plane at \(\omega t=0^{\circ}\) is orthogonal to that at \(\omega t=90^{\circ}\) and rotates counter-clockwise as the time \(t\) increases. Thus, RHCP is achieved at 4.0 GHz for the LB with P1 excited. Figure 7 depicts the simulated results of the \(E\)-field distributions for two different angular times at 5.5 GHz with excitation from P2. It is also evident that, owing to the diagonal arrangement of the main DR, the excitation from the P2 through the cross-slot-coupled aperture effectively excites the higher-order TE\({}_{113}\) mode of the main DR and also splits it into near-degenerate orthogonal TE\({}_{113}^{x^{\prime}}\) and TE\({}_{113}^{y^{\prime}}\) modes. Additionally, the \(Esum\)-fields in the \(x\)'\(y\)'-plane at two orthogonal phases (\(\omega t=0^{\circ}\) and \(\omega t=90^{\circ}\)) are orthogonal to each other, and they rotate \(90^{\circ}\) clockwise as the time \(t\) increases. Thus, LHCP is achieved at 5.5 GHz for the UB with P2 excited. The above analysis demonstrates that the proposed two-port DRA realizes the dual-band operation with dual-CP performance; RHCP at the LB and LHCP at the UB. Figure 5: Simulated results of (**a**) \(|S_{11}|\) and AR when P1 excited and (**b**) \(|S_{22}|\) and AR when P2 excited of the proposed DBDCP DRA.
## 4 Results and Discussion
### Static Field Test
Furthermore, it is worth mentioning that the frequency difference between input and local carrier is shown in Figure 8. Although the GPS tracking channel generates a local carrier with a high fluctuation from the input carrier, it can be interpreted as resulting from the design principle that \(\Delta\theta_{k+1}^{carr}\) must return to 0 in the shortest amount of time. The analysis of Figure 4 indicates the precision of Doppler shift tracking is not reduced, that is, pre-filter 1 is insensitive to the wide fluctuation of local carrier frequency. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMS of Position Error (m)**} \\ \cline{2-4}  & \(X\) & \(Y\) & \(Z\) \\ \hline Pre-filter-S & 0.359 & 0.585 & 0.519 \\ Pre-filter-D & 0.171 & 0.367 & 0.315 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMS of position estimation errors. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMS of Velocity Error (m/s)**} \\ \cline{2-4}  & \(X\) & \(Y\) & \(Z\) \\ \hline Pre-filter-S & 0.044 & 0.085 & 0.072 \\ Pre-filter-D & 0.026 & 0.036 & 0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMS of velocity estimation errors. Figure 7: Velocity estimation errors with field data.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 5 Conclusions  
A combined anomaly detection and trend detection system for the condition monitoring of industrial robot gears has been presented. To select suitable models for these tasks, a method in which models are evaluated based on synthetic and accelerated wear test data was formulated. The synthetic data consists of time series with noise, cyclic behaviour, trends and anomalies based on realistic assumptions that were gathered from industry data. The accelerated wear test data was collected during two experiments with six-axis industrial robots, which provoked multiple gear faults and exhibited both trends and anomalies. By applying the presented method, it was found that the Cox-Stuart test is most suitable for trend detection and the local outlier factor algorithm or the long short-term neural network are capable of detecting the anomalies in the accelerated wear test data. For future research, we believe that the considerations in Section 4 regarding the extensions of CATS with functionalities to detect reasons for false alarms such as robot program changes or the change of the robot tool and the automatic reconfiguration of models in case of too many false alarms are the most important topics for enabling the automated condition monitoring of industrial robot gears in industry.  
Conceptualization, C.N.; methodology, C.N.; software, C.N.; validation, C.N.; formal analysis, C.N.; investigation, C.N.; resources, G.R.; data curation, C.N.; writing--original draft preparation, C.N.; writing--review and editing, G.R.; visualization, C.N.; supervision, G.R.; project administration, C.N.; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript.  
We express our gratitude to the Bavarian Ministry of Economic Affairs, Regional Development, and Energy for the funding of our research. The formulated outlook will be investigated as part of the research project \"KIVI\" (grant number IUK-1809-0008 IUK597/003) and will be further developed and implemented.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality reasons.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
We express our gratitude to our project partners BMW, Fluke and KUKA, who participate in the KIVI project for the fruitful discussions and creative ideas.  
Figure 15: Selection of model parameters based on a maintenance score.
# Active Tendon Control of Stay Cable by a Giant Magnetotrstive Actuator Considering Time-Delay
## 5 Giant Magnetostrictive Actuator and Its Dynamic Model  
The active control force of the cable is provided by a (giant magnetostrictive actuator) GMA. The magnetostrictive rod employed in the GMA is made of Tb0.3 Dy0.7 Fe1.95 alloy, with a length of 120 mm and a diameter of 20 mm, as shown in Figure 5a. The specific structure of the designed GMA is shown in Figure 5b. The length and inner diameter of the excitation coil frame are 150 mm and 24 mm, respectively. Moreover, the number of excitation coil turns is selected as 1100 to cover the range of the control force. In addition, the manufactured GMA is illustrated in Figure 5c and the bias magnetic field is generated by a bias coil whose turns are 1300 and frame diameter is 52 mm. A six MPa preload is applied to the magnetostrictive rod by a preload spring.  
Figure 4: Phase difference between control force and response.  
Employing the cable active control model in Section 6, the relationships of the control force vs. the input voltage of GMA are obtained as follows [24]: ascending stage  
\[U=-0.0114V_{\text{in}}^{3}-0.0078V_{\text{in}}^{2}+2.5955V_{\text{in}}+0.3784 \tag{33}\]  
descending stage  
\[U=0.0058V_{\text{in}}^{3}-0.0993V_{\text{in}}^{2}+1.2425V_{\text{in}}+8.3992 \tag{34}\]  
where \(U\) is the control force produced by the GMA in the axial direction of the cable, \(V_{\text{in}}\) is the input voltage.
###### Abstract  
This study investigates the noise impact on reconstructed images in computer-generated holography (CGH) through theoretical analysis and Matlab 2015b simulations. By quantitatively injecting noise to mimic practical interference environments, we systematically analyze the degradation mechanisms of four CGH types: detour-phase, modified off-axis beam reference, kinoform, and interference type. A dual-metric evaluation framework combining peak signal-to-noise ratio (PSNR) and the Structural Similarity Index Measure (SSIM) is proposed. Results demonstrate that increasing noise intensity induces progressive declines in reconstruction quality, manifested as PSNR reduction and SSIM-based structural fidelity loss. The findings provide theoretical guidance for noise suppression, parameter optimization, and algorithm selection in CGH systems, advancing its applications in optical encryption and high-precision imaging.  
Keywords:computer-generated holography (CGH); noise robustness; image quality assessment; Matlab simulation; structural similarity index measure (SSIM) +
Footnote †: journal: Applied Sciences
## 3 Results
### e-Archeo 3D  
e-Archeo 3D [43] is an interactive, browser-based application implemented on the open-source web platform ATON, developed by the CNR ISPC. It is a web app that visualises the eight archaeological areas through 360\({}^{\circ}\) scenes that can be interrogated and populated with various multimedia content: videos, pictures and 3D models of individual finds. The exploration is designed to be carried out in situ, to have valid knowledge support during a live visit of the archaeological sites, or online, to virtually reach the archaeological site from home or school. It is possible to select one of the eight sites using an initial map or a list on the right in the layout (Figure 10).  
Within each site, the map proposes some points of interest corresponding to the location of the monuments reconstructed in 3D (Figure 11b). By clicking on the pin of the monument or selecting it from the menu, it is possible to display the 360\({}^{\circ}\) panorama with the reconstructive hypothesis (the 'Then' viewing level) and navigate the pictures by rotation and zooming (Figure 11a). The yellow-pulsing elements in the pictures give access to interaction with insights of various kinds: descriptive texts, pictures, videos and 3D objects. The 'Then' display levels can be more than one in cases where several phases of the monument or environment have been reconstructed. It is possible to read or listen to the monument's description and to change the display level from 'Then' to 'Now'. The latter visualisation level shows a 360\({}^{\circ}\) photo of the current site from the same viewpoint as the previous reconstruction (Figure 11b). A further visualisation level, called 'Sources', allows the user to view the colour mapping of reliability levels and access information on sources and interpretation processes by clicking on the coloured areas (Figure 11c). It is possible to move to multiple viewpoints within the reconstructed space through a button at the top of the layout or from the small side map. Integrating more popular information with scientific information makes the web app suitable for the general public and specialist audiences. In addition, it is possible to modify specific settings related to language (Italian  
Figure 9: Outputs and logos of the e-Archeo project.  
or English), text size, the visually impaired mode and automatic audio playback to allow a broader audience to enjoy it. e-Archeo 3D is also available in a more spectacular and site-specific version at the Mengarelli Hall of the Banditaccia Necropolis in Cerveteri.  
Figure 10: e-Archeo 3D: (**a**) interface with the initial choice of site; (**b**) points of interest at the archaeological site of Nora (by 3D Research).
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Ablation Experiment
#### 4.4.1 Attention Mechanism Selection  
Firstly, we compared the performance of our background depth prediction model with/without using the different attention mechanisms (AM, SE, and CBAM) for feature extraction. Table 1 shows that the background depth prediction model achieved the best performance when using the SE attention mechanism because the SE attention mechanism could extract global features that could enhance the background depth prediction of wide areas, especially in outdoor scenes. Figure 5 shows the depth prediction results from our background model using the different attention mechanisms.  
We then evaluated our proposed foreground depth prediction model with/without using the different attention mechanisms for feature extraction. Table 2 shows that the foreground depth prediction model achieved the best performance when using the AM attention mechanism and indicates that the foreground depth prediction model needed some clues to enhance the foreground region depth prediction, especially using the spatial-wise attention mechanism to extract spatial features, which could reflect the 3D details of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attention** & \(\delta\) **\textless{} 1.25** & \(\delta\) **\textless{} 1.25\({}^{2}\)** & \(\delta\) **\textless{} 1.25\({}^{3}\)** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE log** \\ \hline No Attention & & & & & & & \\ Mechanism & 0.949 & 0.991 & **0.998** & 0.063 & 0.289 & 2.986 & 0.102 \\ \hline AM & 0.948 & 0.991 & **0.998** & **0.062** & 0.291 & 3.102 & 0.102 \\ \hline SE & **0.951** & **0.992** & **0.998** & **0.062** & **0.278** & **2.977** & **0.101** \\ \hline CBAM & 0.922 & 0.984 & 0.996 & 0.073 & 0.368 & 3.434 & 0.122 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The evaluation of our background depth prediction model using the different attention mechanisms.  
object surfaces. Figure 6 shows depth prediction results from our foreground model using the different attention mechanisms.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results
### Performance of the Group-Based DCTF Identification Scheme
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **Train Preamble** & **Test Preamble** & **Group** & **Classification Accuracy** \\ \hline \multirow{4}{*}{Adjacent indexes} & P1 & P2 & [P1,P2,P3,P4] & 92.08\% \\  & P13 & P14 & [P13,P14,P15,P16] & 91.54\% \\  & P41 & P42 & [P41,P42,P43,P44] & 89.80\% \\  & P53 & P54 & [P53,P54,P55,P56] & 93.07\% \\ \hline \multirow{4}{*}{Indexes with a difference of 2} & P1 & P3 & [P1,P2,P3,P4] & 89.84\% \\  & P13 & P15 & [P13,P14,P15,P16] & 89.09\% \\ \cline{1-1}  & P41 & P43 & [P41,P42,P43,P44] & 86.44\% \\ \cline{1-1}  & P53 & P55 & [P53,P54,P55,P56] & 90.14\% \\ \hline \multirow{4}{*}{Indexes with a difference of 3} & P1 & P4 & [P1,P2,P3,P4] & 87.39\% \\ \cline{1-1}  & P13 & P16 & [P13,P14,P15,P16] & 87.56\% \\ \cline{1-1}  & P41 & P44 & [P41,P42,P43,P44] & 84.16\% \\ \cline{1-1}  & P53 & P56 & [P53,P54,P55,P56] & 86.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results with preamble sequences within the same _group_. Figure 15: Performance of _group_-based DCTF identification scheme using a single-channel CNN.
## 2 Methodology
### Investigation of the Feasibility of Proposed Approach on Simply Supported Beam  
A simply supported beam was designed to investigate the feasibility of the proposed approach on small structures with a limited data set, as the proposed method in this study was utilized on the tunnel structures, which are considered to be massive structures with a large dataset. ABAQUS was incorporated to do a concrete modeling analysis on a beam that was only held up by two points. C40 concrete was considered a solid element for the modeling. The element type was C3D8R. The concrete's material properties and dimension parameters were 3.2 m long, 0.25 m wide, and 14 mm in diameter, respectively. The beam was distributed into 32 Euler-Bernoulli elements; the elasticity modulus was E = 1.96 \(\times\) 105 MPa. A sensor was positioned at the mid-span of the proposed simply supported beam to measure the vibration response, and the impact load was implemented to acquire the acceleration signals from the beam. The acceleration data of the beam model was obtained using a history output.  
The impact load (hammering) was applied to the proposed simply supported beam model. The beam vibration signals under varying values of impact loads were acquired in the mid-span of the beam via the acceleration sensor to perform a dynamic analysis on the beam. The impact load was adopted six times under relevant conditions, respectively,to collect the vibration signal data from the mid-span of the beam. The beam model was divided into 32 elements, and the displacement and rotation were at the locations of the corresponding supports of the beam.  
The entire finite element model with a cross-section of the beam structure is depicted in Figure 3. To identify the damage using acceleration data acquired from a simply supported beam model, the proposed (DAE) approach was utilized. In contrast, two other methods, LSTM and SVM, were also used for the damage identification of beams. The average testing accuracy and computation time of the three different methods are shown in Table 2. The proposed method's average tested accuracy was 95.3%, which was far higher than the other two methods, with respective accuracy rates of 82.65 percent and 55.75 percent. The proposed DAE system standard deviation was 1.
## 3 Methods
### Multi-Scale Saliency Attention (MSA)  
In order to fully utilize the spatial prior information of the CAM salient graph, an innovative dual-attention fusion mechanism is designed in this module (Figure 5). Specifically, a mask is obtained through pre-training to acquire a CAM with rich multi-scale features, and the generated CAMs are extracted by Conv-BN-ReLU features and then go through a linear layer to obtain the query vectors \(Q_{c}\) and the key vectors \(K_{c}\), respectively, and the attention scores \(M\) of the saliency maps are computed. At the same time, the traditional query Q and key \(F\) are computed for the original feature layer to obtain the attention score of the feature layer. \(F\) is the input feature matrix, and \(W_{Q}\) and \(W_{K}\) are trainable projection weights for query and key, respectively. The core innovation of the mask attention mechanism is the weighted fusion of the saliency maps' attention score \(M\) with the feature layer's attention score through dynamically learnable weight \(\theta\), as shown in Equation (14), in which \(M\) is added as an additional attention augmentation term into the traditional scaled dot product attention computation.  
\[CamAtten(Q,K,V,M)=Softmax\left(\frac{QK^{T}}{\sqrt{d_{k}}}+\theta M\right)V \tag{14}\]where the standard query \(Q=FW_{Q}\), and key \(K=FW_{K}\) matrices are derived through linear transformations of the input features \(F\). The multi-scale CAM-conditioned query and key are generated via a convolutional pathway defined as \(Q_{c}=K_{c}=Linear(ReLU\big{(}BN(Conv(CAM))\big{)}\big{)}\). The attention map \(M=\frac{\Omega_{c}K_{c}^{T}}{\sqrt{d_{k}}}\) scaled by the square root of the key dimension \(d_{k}\) encodes spatial dependencies guided by the multi-scale CAM and enhancing region-specific feature aggregation.  
The fundamental innovation of our mask attention mechanism lies in its dynamic fusion strategy, where saliency map attention scores \(M\) are combined with feature layer attention scores through learnable weighting parameter \(\theta\) (Equation (14)). This integration introduces \(M\) as an auxiliary attention augmentation term within the traditional scaled dot-product attention computation framework.  
This sophisticated design achieves significant advancements by effectively utilizing the spatial priors from CAM saliency maps to guide precise attention allocation in cloud regions. It establishes a synergistic relationship between global semantic comprehension and local feature perception. Furthermore, it demonstrates substantial performance gains in cloud image segmentation through two complementary mechanisms. The spatial guidance from saliency maps suppresses background noise interference while enhancing boundary recognition accuracy for complex cloud formations. Meanwhile, the dynamic weighting scheme enables context-aware attention strategy adaptation, maintaining segmentation precision across diverse cloud types (including thin cirrus, thick cumulonimbus, and multi-layered systems) and challenging conditions (variable morphology, indistinct boundaries, and complex illumination).
## 5 Comparison
### Testing and Results  
Five indicators were used to evaluate the training results, including landing errors LR10, LR20 and LR50, crash rate, and average landing errors. LR10, LR20, and LR50 represent the percentage of episodes that the distance between the final landing point of the parafoil, and the target point is less than 10 m, 20 m and 50 m after 100 tests, respectively. Crash rate is the percentage of times the parafoil hits the no-fly zone or exceeds the flying zone after 100 tests. The average landing error is the sum of the total errors of successful landing divided by the number of successful landings. The lower the crash rate is, the higher the success rate of the model is. The lower the average landing error is, the higher the landing accuracy of the model is. In the simulation environment 1 shown in Figure 6, the test results of DDPG, TD3, and improved TD3 after testing 100 episodes are shown in Table 3. In the simulation environment 2 shown in Figure 7, the test results of DDPG, TD3, and improved TD3 after testing 100 episodes are shown in Table 4.  
Different from simulation environment 1, the horizontal velocity of parafoil will change in different directions due to the influence of wind velocity. If the horizontal velocity decreases, the parafoil may not have redundant height. The control input needs to ensure that the parafoil flies directly to the target point as far as possible without consuming height. This is the reason why the control input in simulation environment 2 fluctuates frequently, but the improved TD3 algorithm can still constrain the size of the control input to make the control input as small as possible.  
The experimental results of the three algorithms under random initial points are also compared. Figures 18-20 show the initial positions of parafoil under simulation environment 1 as [439; 299], [72; 312], and [339; 452], DDPG, TD3, and improved TD3 were used for testing, respectively. Figures 21 and 22 show the initial positions of parafoil in simulation environment 2 as [432; 325] and [165; 99]. The landing errors of the three algorithms are shown in Table 5. It can be seen from Figure 19c that, because the learning ability of the DDPG is poor compared with the other two algorithms, it can not learn a better input control strategy, resulting in a high input control oscillation frequency of the DDPG. The oscillations in Figure 19d are due to the fact that 360\({}^{\circ}\) and 0\({}^{\circ}\) are considered equal in this work. When the initial positions are [439; 299] and [72; 312], the landing accuracy of improved TD3 is obviously better than DDPG and TD3. When the initial position is [339; 452], although the landing accuracy of the improved TD3 is slightly worse than that of TD3, it can be seen from Figure 20 that the trajectory of the improved TD3 algorithm is more consistent with the setting of the reward function and can effectively consume altitude, and the control input value is smaller, thus proving that the training effect of the improved TD3 is better.