esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Array Processing
### SLL Reduction Applied to ULA and NULA  
In this case, weight vectors are calculated for minimizing Equation (12), and a comparative study of the ULAs and NULAs composed of five and eleven commercial antennas is presented. The objective is to evaluate the impact of this beamforming technique on radiation pattern parameters in both array architectures. The iteration method proposed in [31] was applied with the constraints of SLL and GLL to be lower than 15 dB. Tables 6 and 7 summarize the results for \(N=5\) and \(N=11\), respectively. The SLL improvement with respect to uniform conventional beamforming is clear, with a very small reduction in directivity. For \(N=5\), the SLL (dB) of the NULA is slightly higher than the SLL (dB) of the ULA with \(d=270\) mm, and the directivity is slightly lower, but GLL (dB) is lower than the SLL (dB) for the ULA with \(d=270\) mm for steering angles equal to \(\pm 30^{\circ}\) and does not fulfill the defined GLL constraint. The gain and SLL (dB) are similar for NULA and the ULA with \(d=210\) mm, but the BW in the azimuth plane is \(2^{\circ}\) narrower in the case of NULA. For \(N=11\), the improvement of NULA compared to ULAs is significantly higher. The SLL (dB) of the NULA is higher, especially for steering angles close to the broadside; the directivity is lower than the directivity of the ULA with \(d=270\) mm, but this ULA does not fulfill GLL constraint for steering angles equal to \(\pm 30^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pointing Angle** & **Array** & **BW** & **SLL (dB)** & **GLL (dB)** & **D (dBi)** \\ \hline \multirow{3}{*}{\(-30^{\circ}\)} & ULA (\(d=210\) mm) & \(9^{\circ}\) & 10.98 & x & 15.93 \\ \cline{2-5}  & ULA (\(d=270\) mm) & \(7^{\circ}\) & 9.69 & 12.26 & 16.60 \\ \cline{2-5}  & NULA & \(9^{\circ}\) & 14.34 & x & 16.06 \\ \hline \multirow{3}{*}{\(-15^{\circ}\)} & ULA (\(d=210\) mm) & \(9^{\circ}\) & 12.25 & x & 16.07 \\ \cline{2-5}  & ULA (\(d=270\) mm) & \(7^{\circ}\) & 12.23 & x & 17.09 \\ \cline{2-5}  & NULA & \(8^{\circ}\) & 13.22 & x & 16.48 \\ \hline \multirow{3}{*}{\(0^{\circ}\)} & ULA (\(d=210\) mm) & \(8^{\circ}\) & 13.62 & x & 16.15 \\ \cline{2-5}  & ULA (\(d=270\) mm) & \(6^{\circ}\) & 13.39 & x & 17.18 \\ \cline{2-5}  & NULA & \(8^{\circ}\) & 14.68 & x & 16.63 \\ \hline \multirow{3}{*}{\(15^{\circ}\)} & ULA (\(d=210\) mm) & \(9^{\circ}\) & 12.33 & x & 16.09 \\ \cline{2-5}  & ULA (\(d=270\) mm) & \(7^{\circ}\) & 12.29 & x & 17.11 \\ \cline{2-5}  & NULA & \(8^{\circ}\) & 13.30 & x & 16.5 \\ \hline \multirow{3}{*}{\(30^{\circ}\)} & ULA (\(d=210\) mm) & \(9^{\circ}\) & 11.09 & x & 15.92 \\ \cline{2-5}  & ULA (\(d=270\) mm) & \(7^{\circ}\) & 9.78 & 13.10 & 16.58 \\ \cline{2-5}  & NULA & \(9^{\circ}\) & 14.44 & x & 16.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main characteristics of the azimuth plane radiation patterns generated with the maximum directivity weighting vector of the ULAs with inter-element spacings of 210 mm and 270 mm and the NULA designed in Section 3.2, for \(N=11\) commercial antennas. The considered steering angles are \(\phi=[-30,-15,0,15,30]^{\circ}\).
# Delay Analysis and the Control of Electro-Hydrostatic Actuators
###### Abstract  
The electro-hydrostatic actuator (EHA) has a high energy density and a compact structure so it is widely used in all/more electric aircraft. To gain energy-saving capability, the hydraulic lock can be applied in the EHA system, which can overcome the change of load after the position control is complete, reducing the motor's need to work continuously. Due to the use of a hydraulic lock in the EHA system, there is a more significant position output delay compared with the EHA system. The delay would lead to a dynamic response error. Therefore, an electro-hydrostatic actuator with a bidirectional hydraulic lock (BHL-EHA) model is established that can reflect the position output delay and it can be used to analyze its influence factors. According to the analysis, the factors affecting the delay are pump speed, external load, hydraulic lock cracking pressure, friction load, pump displacement, fluid volume of the system, and effective bulk modulus; thus, an equation that can be used to calculate the delay is proposed. At the same time, the experimental system of the BHL-EHA system is established, and the parameters of the experimental facility are completely consistent with the simulation model. Finally, a delay compensation control method is proposed, and the control time parameter \(t_{0}\) is calculated based on the equation proposed above. The results of the simulation and the experiment show that the method can compensate for the delay caused by the hydraulic lock. Combined with a feedforward control, the method can dismiss the dynamic response error of 0.6 Hz sinusoidal tracking characteristics.  
electro-hydraulic actuator; hydraulic lock; modeling; delay; delay compensation control
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 2 Experimental Approach
### Tested Bearing and Cage Design  
The angular contact ball bearing of 7013 AC was chosen in this paper for experimental investigation. The main geometrical parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Ball diameter & 11.1 & mm \\ Bearing outside diameter & 100 & mm \\ Inner ring diameter & 65 & mm \\ Initial contact angle & 25 & deg \\ Inner curvature factor & 0.515 & - \\ Outer curvature factor & 0.51 & - \\ Pitch diameter & 82.525 & mm \\ Cage outside diameter & 86.5 & mm \\ Cage width & 26 & mm \\ Number of balls & 18 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of an angular contact ball bearing 7013 AC.  
Figure 1: Structural diagram of cage clearances.  
Figure 2: Bearing test rig. (**a**) Photo of test rig, (**b**) internal structure of bearing box.  
In order to investigate the cage motion affected by the cage guiding clearance, three cages with constant pocket clearance but different guiding clearances were designed with the cage inner diameter \(D_{ci}\) of 75.5 mm, 75.7 mm, and 76.1 mm as shown in Figure 3. Correspondingly, \(C_{g}\) was 0.1 mm, 0.2 mm, and 0.4 mm, respectively, and the constant pocket clearance was set as 0.1 mm. For the influence studies about of the pocket clearance, three cages with diverse pocket clearances (0.1 mm, 0.25 mm, and 0.4 mm) and constant guiding clearance of 0.45 mm are designed, in which pocket diameter \(D_{p}\) was 11.3 mm, 11.6 mm, and 11.9 mm, respectively, as shown in Figure 4. In addition, the material of the cage is 45# steel.  
Oil-injection lubrication with flow speed of 0.1 L/min was adopted for the tested bearing. The axial load and radial load applied on the tested bearing could be from 50 N to 2000 N, and operating speed could range from 600 to 6000 rpm. A variety of experiments with different loads and operating speeds were carried out for further study.
# An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans
###### Abstract  
This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm's search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems.  
multi-objective optimization; high-quality population initialization; associative learning; axial cooling fan; aerodynamic performance
## Appendix C
### Stress-Strain Displacement Relations  
The relations between stress and strain displacement are given in the \((\xi,\vartheta,\eta)\) coordinates as follows:  
\[\begin{split}&\sigma_{\xi\xi f}=c_{11}\Big{(}\frac{\partial u_{\xi\xi f }}{\partial\xi}\Big{)}+c_{12}\Big{(}\frac{u_{\xi f}}{\xi}+\frac{1}{\xi}\frac{ \partial u_{\theta\theta f}}{\partial\vartheta}\Big{)}+c_{13}\Big{(}\frac{ \partial u_{\theta\theta f}}{\partial\eta}\Big{)}\\ &=P_{f}\Big{(}a_{11f}A_{mf}+a_{12}B_{mf}+a_{13}C_{mf}+b_{1f} \Big{)}e^{-i\omega t}\\ &=0,\end{split} \tag{100}\]  
\[\begin{split}&\sigma_{\xi\eta f}=c_{44}\Big{(}\frac{\partial u_{ \theta\xi f}}{\partial\xi}+\frac{\partial u_{\theta\xi f}}{\partial\eta}\Big{)} \\ &=P_{f}\Big{(}a_{31f}A_{mf}+a_{32f}B_{mf}+a_{33}C_{mf}+b_{3f} \Big{)}e^{-i\omega t}\\ &=0,\end{split} \tag{101}\]  
where \(f=z\) or \(\theta\). For \(m=0\), \(B_{0f}=0\). For \(f=z\), substituting Equations (36) and (38) into Equations (100) and (101), respectively, and for \(f=\theta\), substituting Equations (39) and (41) into Equations (100) and (101), respectively, we obtain the following two linear algebraic equations:  
\[\begin{split}\begin{bmatrix}a_{11f}&a_{13f}\\ a_{31f}&a_{33f}\end{bmatrix}\begin{bmatrix}A_{0f}\\ C_{0f}\end{bmatrix}=\begin{bmatrix}b_{1f}\\ b_{3f}\end{bmatrix}\end{split} \tag{102}\]
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 2 Background and Motivation  
Like many early and formative internet protocols, security unfortunately became an afterthought in the design of NDP: presumably sacrificed based on the need for protocol optimizations that matched the performance of the more limited hardware constraints of the era. The widespread adoption of NDP occurred before its threat models were formally cataloged in RFC 3756 [9] and before the specification of Secure Neighbor Discovery in RFC 3971 [5] with its complementary Cryptographically Generated Addresses in RFC 3972 [6]. [PERSON] et al. in 2002 [10] first detailed the various vulnerabilities of Neighbor Discovery: neighbor spoofing, Router Advertisement spoofing, bogus-prefix denial-of-service attacks, Duplicate Address Detection attacks, and attacks based on falsely advertised configurations. Many of these remain trivial to execute in normative networks as shown by [PERSON] et al. as recently as 2016 [11], almost a decade after the final revision of the NDP specification. Unfortunately, these problems continue to linger in modern IPv6 deployments.  
Among its list of insecurities, NDP harbors a dangerous capability for neighbor spoofing threats creating an opportunity for easy-to-execute on-path attacks. By successfully advertising a spoofed link-layer address binding with a victim IP address, an attacker can redirect frames from neighbors to itself, allowing it to inspect and capture private packet data on the forwarding path before then forwarding it to its original unicast target. This initial spoof and redirection process is sometimes termed \"cache poisoning\" to express that each misled neighbor falsely caches a binding (i.e., association) between a neighbor IP address and a malicious link-layer address rather than a target's genuine link-layer address. If an attacker successfully spoofs link-layer address bindings for both target IP addresses in a local exchange, then it can insert itself on-path between the two communicating neighbors and transparently observe passing network traffic. Any upper-layer protocol without encryption then becomes susceptible to this attack, having atrocious privacy and security implications at the local network scope where traffic has the highest chance to be unencrypted and insecure.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 4 Discussion  
The results of our pipeline indicate that it effectively meets the requirements for rescue scenarios. Our methodology was able to improve image detail, enhance object detection, and provide fast processing.  
As described in the details recovery Section 3.1, we used several metrics to evaluate the image characteristics of the pipeline's outputs compared to the original images. The results showed that the pipeline was able to improve the complexity of the image's texture, increase the information content, and augment the number of objects detected. Reference-based image quality analysis also demonstrated that the pipeline was able to produce images with higher quality compared to the original images, as evidenced by the reduced MSE and increased SSIM. Visual inspection further supported these results, providing a clear side-by-side comparison of the original, reference, and corrected images.  
One of the main objectives of this model was to improve the light environment to boost the efficiency of other vision DL models, and this goal has been achieved as detailed in the object detection improvement Section 3.2. The section highlighted how the pipeline was able to improve the performance of the YOLOv7 object detector in the detection of people. The results indicated that the corrected images allowed the model to identify individuals with higher Precision, Recall, F1-score, and Average Precision compared to the original images. The Precision-Recall and F1-Confidence curves, as well as the visual comparison of the predicted bounding boxes, provided clear evidence of the improvement in people detection.  
The Efficiency Section 3.3, indicates that the pipeline was able to process and provide information quickly enough to meet real-time requirements, which is essential in rescue scenarios. The results showed that the pipeline was able to reach the IF module (1 st stage) and complete the fusion stage in a reasonable time even for images with different shapes.
## 2 Methodology  
### Interpolation Model  
The interpolation accuracy of the proposed DSM was compared with that of the commonly used DIM as well as USM which is based on the longitude and latitude of the network center and IPP. The LSM can consider various forms, such as elevation, to adapt to different application environments, so it was chosen as an interpolation function of tropospheric delay that is sensitive to elevation. The interpolation model that was used is described below.
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis
#### 3.3.3 Experimental C  
The surface residual current distributions of the surrounding before and after the coastline change in Area 3 are shown in Figure 11a,b, whereas the changes in flood and ebb currents are shown in Figure 11c,d. The residual current distribution of Experiment C was similar to that of the control experiment, whereas the tidal current changes were not as significant as in Area 1 and Area 2. The variations in velocities of peak flood and ebb tidal currents were not significant, and they primarily showed a decreasing trend. This study took Station C (29.53\({}^{\circ}\) N, 121.60\({}^{\circ}\) E) to compare the changes in flood and ebb current velocities before and after the coast changes.  
Figure 11: Current changes in Area 3. (**a**) Residual currents distribution before the change in coastline; (**b**) Residual currents distribution after the change in coastline; (**c**) Changes in the velocity of the peak flood tidal currents; (**d**) Changes in the velocity of the peak ebb tidal currents.  
Before the coastline change, the maximum current velocities for flood tide and ebb tide at Station C were 0.62 m/s and 0.63 m/s, with the average velocities being 0.34 m/s and 0.27 m/s, respectively. After the coastline change, the maximum current velocities for flood tide and ebb tide were 0.67 m/s and 0.52 m/s, with the average velocities being 0.35 m/s and 0.26 m/s, respectively. Compared to currents before the coastline change, the maximum flood current velocity increased by 0.05 m/s, whereas the maximum ebb current velocity decreased by 0.09 m/s. The average flood current velocity during flood time increased by 0.01 m/s, and the average ebb current velocity during ebb time decreased by 0.01 m/s at this station. Overall, the flow velocity changes at Station C were not significant, with slightly increased flood current velocity and slightly decreased ebb current velocity, which may have been related to the shortened duration of the flood tide and lengthened the duration of the ebb tide at this station after the coastal boundary changes.
## 2 Materials and Methods  
### Data Presentation  
_Seismic data:_ post-stack time migrated full-stack seismic data with an angle range of 5-40 degrees were used in this study. The seismic data have a dominant frequency of 30 Hz at the reservoir interval and a bandwidth of 80 Hz. Its sample interval is 2 ms with an approximate tuning thickness of 32 m at the reservoir interval. Figure 4 shows the base map of the seismic data, with a dip line range of 3040 to 3840 and strike line range of 4215 to 5615.  
_Well logs:_ Eight wells were drilled in the field, but only four out of them have the required log suites for this analysis. These wells are B-1, B-2, B-7, and B-8 (Figure 4). All the required composite logs, such as compressional sonic, shear sonic, density, gamma-ray, etc., were available and adequately covered the interval of interest. The check shot data, the vertical seismic profiling data, and interval velocity data were provided and carefully integrated to perform a robust well-seismic tie. The seismic-well tie process was used to validate the accuracy of the input wavelet.  
_Seismic wavelets:_ This technique requires an intensive wavelet analysis. First, we used an analytical wavelet for the initial analysis, followed by a statistical wavelet from seismic data, and finally, a deterministic wavelet was extracted by combining the well and the seismic data.  
Figure 3: Schematic geologic cross-section, showing the sequence stratigraphic Groups and geologic structures of Malay Basin (redrawn by [39], after [40]).
## 4 Experiment
### Comparison Models  
To fully estimate our model, it is compared to a couple of advanced baseline models.  
* IAN [27] (Interactive Attention Network) generate aspects and sentence representation through two LSTMs and interactive attention mechanisms for interactive learning in context and target.
* AOA [28] (attentional-over attentive) jointly utilizes aspects and sentences for modeling and pays automatic attention to significant parts of a sentence.
* ASGCN [33] uses multi-layer GCNs to encode and integrate sentence dependency trees to gain aspect-oriented features and solve the multi-word dependency problem in sentiment classification.
* CDT [7] learns representations of sentence features via BiLSTM, performs convolutionally enhanced embedding using dependency parse trees of sentences, and learns aspectual representations with syntactic information.
* R-GAT [10] defines aspect-oriented dependency structures by recasting and trimming the common dependency parse tree. The new tree is then encoded using R-GAT.
* R-GAT + BERT [10] is the R-GAT model using pre-trained BERT instead of BiLSTM as the encoder.
* DualGCN [16] integrates syntactic and semantic information for sentiment categorization and utilizes orthogonal and differential regularizers to process semantically related terms.
* DualGCN + BERT [16] is the DualGCN model using the pre-trained BERT instead of BiLSTM as the encoder.
* dotGCN [29] regards aspect-to-context attention scores as syntactic distances, constructs aspect-specific tree models, and simplifies complex structures.
* RAG-TCGCN [38] fuses syntactic, semantic, and public information through a three-channel GCN, optimizes fusion using a multi-head attention mechanism, and reduces information loss through a residual attention gating mechanism.  
Figure 6: The number of GCN layers.  
* SSEGCN [18] generates sentence attention score matrices through aspect-aware attention mechanisms and self-attention mechanisms, combines them with syntactic mask matrices, and utilizes GCNs to enhance node representations, fusing syntactic and semantic information for ALSC tasks.
* SSEMGAT [17] fuses syntactic and semantic features using a multilayer GAT by introducing component trees and aspect-aware attention mechanisms.
* GMF-SKIA [40] combines affective knowledge and inter-aspect dependencies to model interactions using GCNs and multi-head self-attention mechanisms, and fuses features through gating mechanisms.
* LSOIT [41] integrates semantic knowledge, phrase structure, and dependencies via reinforcement learning and attention and then applies GNNs for analysis.