esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Quantitative Evaluation
#### 3.2.1 Subjective Visual Comparison
GF performs moderately but shows slight halo effects, while DWT and LP suffer from blurring or checkerboard artifacts. CVT, DTCWT, and NST exhibit significant blurring and distortions, making them unsuitable for hardware inspection. CNN enhances local contrast but introduces artificial textures. Overall, DSIFT and the proposed method are optimal for circuit board fusion, balancing detail preservation and noise suppression, whereas wavelet-based and pure deep learning approaches need refinement to avoid over-smoothing or artificial enhancements. Figure 21 shows the fusion images of the battery holder circuit sample dataset shown in Figure 10 under different algorithms. DSIFT and the proposed algorithm demonstrate optimal performance, clearly presenting circuit trace details and solder joint morphology without noticeable artifacts. GF and LP algorithms perform second best, preserving main features but exhibiting slight halo effects or edge aliasing. DWT, CVT, DTCWT, and NST algorithms show significant blurring due to limitations in frequency-domain transformation. While the CNN algorithm enhances local contrast, it introduces unrealistic texture details. Overall, the proposed algorithm achieves the best balance between retaining critical circuit features and suppressing artifacts, making it particularly suitable for the inspection requirements of precision electronic components such as battery holders. Figure 22 shows the fusion images of the sensor circuit dataset shown in Figure 11 under different algorithms. DSIFT and the proposed algorithm demonstrate the best performance, clearly preserving the fine traces and pad details of the golden circuit structures without introducing noticeable artifacts. Although the GF and LP algorithms maintain the overall structural integrity, they suffer from slight blurring and jagged edges, respectively. Other traditional transform-based methods exhibit significant blurring due to the limitations of frequency-domain processing--most notably, DWT introduces checkerboard artifacts, while NST excessively smooths component labels. While the CNN method enhances contrast, it also introduces false textures. Overall, the proposed algorithm achieves the optimal balance among detail preservation, artifact suppression, and computational efficiency, significantly outperforming the other methods. It is particularly well-suited for sensor circuit inspection scenarios that require micron-level precision.
## 3 Numerical Investigation of the Actuator
The displacement amplitude in the Z direction is 47.53 \(\upmu\)m, while those in the X and Y directions are 6.24 \(\upmu\)m and 3.1 \(\upmu\)m, respectively. The ratio between displacement amplitudes in the X and Y directions is 2.01, while the ratio between displacement amplitudes in the Z and X or Y directions is approximately 7.61. The velocity-frequency characteristics of the contact point motion were calculated as well. It was found that peaks of the velocity amplitudes are at the same frequencies as those in the displacement-frequency characteristics. The velocity amplitudes of the contact point are 15.3 m/s, 0.98 m/s, and 2.01 m/s in the Z, X, and Y directions, respectively. The ratio between velocity amplitudes in the X and Y directions is 2.05, while the ratio between the Z and X or Y directions is approximately 7.61. The contact point motion trajectories in the XY, YZ, and XZ planes were calculated. A time domain study was used for this purpose. A time range equal to one period of the resonance frequency was analyzed while a time step of 0.25 \(\upmu\)s was used. The same excitation scheme of the actuator as that in the frequency domain study was used. The results are shown in Figure 6.  
It can be seen that the contact point trajectories are close to an elliptical shape and have different lengths of the major axis and rotation angle to the horizontal axis. The contact point motion trajectory in the XY plane shows that the displacement projection in the Y axis is 1.94 times larger compared to displacement in the X axis. This trajectory mainly represents displacement generated by the second bending vibration mode. The contact point trajectory in the XZ plane has notably higher displacement compared to that in the XY plane (Figure 6a). The length of trajectory projection in the X axis is 13.2 \(\upmu\)m, while that in the Z axis is 92.5 \(\upmu\)m. The ratio between displacements is around 7. It can be concluded that the superposition of the first longitudinal and second bending vibration modes ensures higher displacement compared to that generated by a single second bending vibration mode. The contact point trajectory in the YZ plane has the highest major axis length compared to the trajectory projections of the XY and XZ planes. The length of trajectory projection in the Y axis is 6.45  
Figure 5: Amplitude–frequency characteristics of the contact point. \(\upmu\)m, and the projection in the Z axis is 93.25 \(\upmu\)m. The ratio between displacement projections in the Y and Z axes is 14.45. These results show that displacement fields generated only by the second bending mode in the XY plane are notably lower compared to displacement fields generated in the YZ plane during actuator operation in the superposition mode. Based on the results of the numerical study, it can be concluded that traveling wave displacements generated by the second bending vibration mode are modified by displacements of the first longitudinal vibration mode and can be used to rotate a rotor.
## 3 Results
### Absolute Biases of Jason-3  
In April 2016 and then in July 2017, we measured the vertical profile of the lake along the Jason-3 track, #131 during cycles 5 and 52. The results are shown in Figure 7 for cycle 52, with the along-track vertical's height of the lake, based on the _Ocean_ and _Ice-1_ retrackers, and compared to the  
Figure 5: Ellipsoidal height of the lake surface measured by Sentinel-3A with the _Ocean_ retracker for cycle 9 (October 2016), for the two tracks (666 and 707) and with the boat instruments.  
Figure 6: Height above the ellipsoid of the lake measured by Sentinel-3A, with the _Ice-1_ retracker for the cycle 9 (October 2016), for the two tracks (666 and 707) and with the shipborne instruments.  
GPS profile. Absolute bias of Jason-3 (with _Ocean_ retracker) measured through the two campaigns was \(-28\pm 40\) mm StD. With _Ice-1_ the bias was 206 \(\pm\) 30 mm StD. This was in very good agreement with other estimation done over the ocean C/V sites [34; 35], oral presentations at the last OSTST 2017, in Miami. We could also remark that the along-track stability of the Jason-3 data (characterized by the StD of the comparison with GPS data: 30 mm with _Ice-1_ and 40 mm with _Ocean_) was poorer than with the Sentinel-3A (20 mm for both retracking algorithms).
## 4 Two-Particle Velocity Correlation Function
[29] for \(R_{c}\) are also depicted in Figure 12 as crosses, showing a good agreement with those obtained in the present simulations. However, in Reference [29], the volume fraction values were different for the four particle types analysed and ranged from 0.3% to 2%. Another interesting fact that can be observed in the \(R_{c}\) correlation functions in Figure 12 is that, for a fixed particle Stokes number, their values increase with increasing solid volume fraction. The counterpart, seen in Figure 13, is that the radial relative velocity between colliding particles decreases with increasing volume fraction. This fact was already shown by the authors of Reference [38], who also observed that the mean angle formed by the velocities of colliding particles was increasing with particle inertia. These authors ascribed such behaviour to a kind of collective motion of particles in clusters. Such phenomenon has also been found in the present work and it was illustrated in Figure 8a. However, in the present study, such trend is not as evident as in that research because they employed higher values of \(\alpha_{p}\) than in this work. In summary, it has been found that the colliding particle pair velocity correlation is smaller than that computed by the Eulerian particle pair velocity correlation as particle separation tends to zero. The practical consequence for the stochastic collision models in the Euler-Lagrange frame is that the velocity correlation between the real and fictitious particles cannot be drawn out from the Eulerian two-particle velocity correlation given by the model of [PERSON] and [PERSON], but instead should be built from the colliding particle-pair correlation function, which depends on both particle inertia and volume fraction. As an example (which does not pretend to be universally valid), functional forms for such correlation can be devised from the presented DNS results and Table 3 shows the fitted coefficients for the \(R_{c}\) curves to Equation (8) for the volume fraction cases studied in this work. The corresponding curves of such fits are also plotted in Figure 12 for comparison, which could be used for building the fluctuating component of fictitious collision partners in stochastic models of inter-particle collisions, e.g., Equation (23) in Reference [31]. Nevertheless, it would be necessary to study the dependence of such correlations on turbulent Reynolds number and in a larger range of particle volume fractions. Figure 12.0 Modulus of mean radial relative velocity (RRV) between particle pairs, \(w_{r}=w\cdot r/|r|\), as a function of the particle Stokes number normalised by the Kolmogorov velocity, \(u_{K}\). Half-closed symbols represent colliding particles while open symbols denote the average of close particle pairs at a distance lower than \(r=3d_{p}/2\) based on Eulerian evaluations of the radial distribution.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation
### Evaluation Criteria
#### 4.1.1 Mae  
MAE (mean absolute error) is a statistical indicator used to measure the difference between predicted values and actual values. It calculates the average of the absolute values of the differences between predicted values and actual values. The formula is presented as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\widetilde{y_{i}}| \tag{7}\]  
where \(n\) is the total number of test samples, and \(y_{i}\) and \(y_{i}\) are the true and predicted remaining useful life (RUL) values for the \(i\)th test sample data, respectively.
# Double Trapezoidal Wave Transmitting System with Controllable Turn-Off Edge
## 4 Experiment with Wire Loop  
### Setup of Experiment  
In geophysics, the resistance of the earth is usually equivalent by placing a closed, accurately laid out, and surveyed multi-turn, insulated ground wire loop of known inductance L and resistance R [21]. To verify the effect of the transmitting system on the polarized earth, we designed a polarized wire loop by imitating the normal wire loop. The polarized wire loop was designed according to the filament model and the multi-turn coil with known inductance was equivalent to the inductance L in the model. Next, the circuit built by selected resistance, R0, R1, and capacitance, C was connected to the two ends of multi-turn coil to form a loop. This wire loop had a resistance R0 of 400 \(\Omega\), resistance R1 of 40 \(\Omega\), capacitance (C) of 50 \(\mu\)F, and an inductance (L) of 100 mH. The equivalent circuit of the polarized wire loop is shown in Figure 7a and the physical figure is shown in Figure 7b.  
During the experiment, the transmitting system injected a double trapezoidal wave emission current into the \(100\times 100\) m square transmitting coil with a resistance of 12 \(\Omega\) and an inductance of 1.05 mH, which was powered by 12 V battery. The clamping voltage of the two TVS diodes in the turning-off edge control circuit of the transmitting system was 68 and 8.2 V. We used a homemade HT SQUID for the receiver and placed it in the center of the transmitting coil. Compared with the coil receiver system, the accuracy and bandwidth of SQUID were more conducive to observing the induced polarization effect [9]. The experimental layout is shown in Figure 8. The polarized wire loop was placed coaxially and aligns the SQUID sensor to simulate the polarized earth.  
Figure 8: The layout of experiment.  
Figure 7: The polarized wire loop: (**a**) equivalent circuit and (**b**) photograph.
## 5 Selective Bio-Inspired Algorithms with Hybrid Optimization
### Hybrid GA-PSO Algorithm  
The fundamental goal of hybrid GA-PSO is to enhance cluster head selection and routing between deployed nodes and the base station. There are two steps to the proposed method. In the first phase, the PSO algorithm holds passed population and fittest individuals. In the second phase, these fittest individuals are operated by the GA operators who are selection, cross-over, and mutation. Hybridization of GA and PSO combines the merits of both algorithms, which provide us better convergence rate and avoid the problem of local optima [51; 52]. The major contribution of both algorithms has been shown in Table 2.  
This method combines the benefits of both algorithms, such as PSO's rapid convergence rate and GA's problem of trapping in local optima. The primary purpose of this PSO-GA strategy is to steadily raise the number of decent people across generations.  
The design procedure for the GA-PSO algorithm is:  
* Initialization;
* Generation of the initial population;
* Selection;
* Cross-over;
* Mutation;
* Growth;
* Generation of a new population;
* Repeat until no. of generation (Ng) evaluates.  
The hierarchical sensor network model is used in the PSO, GA, and PSO-GA approaches for small and large size networks. In a hierarchical WSN paradigm, each cluster contains one base station and one relay node. In this WSN configuration, the relay node serves as the cluster head. The most significant assumption is that base stations offer routing pathways and that each relay node's average data volume is known. Each relay node's leftover energy is replenished at the end of each generation, and current energy is utilized to determine the next routing path. When comparing the shortest path method, PSO approach, GA approach, and hybrid PSO-GA approach for large-scale networks, we observed that the hybrid PSO-GA strategy has the best network lifespan and packet delivery ratio.
## 2 Theory and Methodology
### Northern Goshawk Optimization
#### 2.3.2 Prey Identification  
The Northern Goshawk randomly selects a prey to achieve a global search. The main process is represented by Equations (13)-(15).  
\[P_{i}=X_{k},i=1,2,\ldots,N,k=1,2,\ldots,i-1,i+1,\ldots,N \tag{13}\]  
\[x_{i,j}^{new,P1}=\left\{\begin{array}{c}x_{i,j}+r(P_{i,j}-1x_{i,j}),F_{P_{i} }<F_{i}\\ x_{i,j}+r(x_{i,j}-p_{i,j}),F_{P_{i}}\geq F_{i}\end{array}\right. \tag{14}\]  
\[X_{i}=\left\{\begin{array}{c}X_{i}^{new,P1},\;\;\;F_{i}^{new,P1}<F_{i}\\ X_{i},\;\;\;\;\;\;\;\;F_{i}^{new,P1}\geq F_{i}\end{array}\right. \tag{15}\]  
where \(P_{i}\) represents the prey position for the \(i\)th Northern Goshawk, \(F_{P_{i}}\) denotes its objective function value, \(k\) is a random natural number, \(X_{i}^{new,P1}\) signifies the new status for the \(i\)th proposed solution, \(x_{i,j}^{new,P1}\) refers to its \(j\)th dimension, \(F_{i}^{new,P1}\) indicates its objective function value based on the first phase of NGO, \(r\) is a random number within the interval [0, 1], and \(I\) is a random number that can take values 1 or 2. Parameters \(r\) and \(I\) are utilized to introduce a random behavior in the search and update processes of NGO.
# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
## 3 Methods
### Entity Relational Storage of Neo4j  
Neo4j is a commercially supported open-source graph database. It stores data in a graph, the most generic of data structures, capable of elegantly representing any kind of data in a highly accessible way. For entity relation representation, Neo4j records entities in nodes which have two properties: entity name and entity frequency. These nodes are organized by relation type which has the property of co-occurrence weight (\(X_{\text{ij}}\) in the co-occurrence matrix). Figure 2 shows the graph structure that we use for entity relation representation.  
The cosine similarity between entity vectors is one of the most common methods for distributional entity relation representation. However, Neo4j is suitable for storing relational types instead of real numbers. In order to classify the relational types from distributional entity relation representation between entities, this paper presents an unsupervised method based on entity vectors. Firstly, we choose 24 relation sub-types in the task of ACE relation extraction as our relational types. Then we use the traditional global vector model to train the relational type vectors in a large-scale corpus of Sina News. Finally, we choose the most probable entity relationship type by calculating the cosine similarity between entity vectors and relationship type vector.  
Neo4j can store hundreds of millions of nodes and relations. Querying from huge data needs a powerful query language. The declarative graph query language of cypher is designed to allow for expressive and efficient querying. Cypher is a humane query language which is similar with SQL  
Figure 2: The graph structure for visual representation.  
There are four common distinct clauses for querying: START (starting pointing in the graph), MATCH (the graph pattern to match), WHERE (filtering criteria) and RETURN (what to return).
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 4 Experiment Implementation
### Data Argumentation  
For data augmentation, we adjusted the size of input images to accommodate various sizes and cropped out background areas. We also applied techniques such as horizontal flipping, as images of the left eye need to mirror those of the right eye. Previous studies on glaucoma detection utilized additional data augmentation methods, such as image rotation and adding Gaussian noise. However, considering the importance of the optic cup-to-disc ratio as a critical indicator for diagnosing glaucoma, we refrained from using image-resizing methods that could alter this ratio. Instead, we chose to reduce the size of the images while preserving the original proportions.  
Additionally, since retina fundus images are not typically captured in rotated positions, we did not use image rotation. We believe that altering medical data can impact the characteristics of the disease. Thus, we avoided using data augmentation methods that introduce transformations, such as adding noise. We systematically reduced the size of the images from their original dimensions down to 500 \(\times\) 500, carefully cropping 10% from all four sides in stages to ensure that the optic nerve within the retina fundus images was not cut off. Horizontal flipping was applied to ensure symmetry between the corresponding left and right-eye images.  
In order to address the issue of data imbalance, we conducted data augmentation to achieve a 1:1 ratio between normal and glaucoma cases. This approach maintains the integrity of medical imaging data while effectively increasing the dataset for balanced training.
## 3 Proposed Fault Detection and Classification Algorithm
### Ground Fault Index  
A ground fault index (GFI) is introduced to discriminate the LL fault with respect to the LL-G fault. This is evaluated in a similar way as the W-index and depends on the signal of the zero sequence current. The WDF of the signal with the current (zero sequence) is evaluated for a duration of a quarter cycle with a moving window over one sample with a frequency of sampling of 3.84 kHz. The SAVs of the quarter cycle are calculated, and the maximum values of these quantities over a quarter cycle window are recorded. The maximum values of the WDF over the quarter cycle moving with one sample are designated as the ground fault index (GFI). The threshold value (GFTH) of \(4\times 10^{10}\) is selected for the GFI to identify the involvement of the ground during the LL faults. A higher value of the GFI compared to the GFTH indicates that the ground is not involved in the LL fault, whereas a lower value of the GFI compared to the GFTH indicates that the ground is involved. Further, the GFTH is selected by testing the algorithm on the 20 datasets of the LL and LL-G faults obtained by changing the parameters such as the location of the fault on the transmission line, the fault incidence angle, the fault impedance, the reverse power flow, the presence of noise, the hybrid combination of the underground (UG) cable and overhead line, etc. Hence, the value of \(4\times 10^{10}\) for the GFTH is effective at identifying the involvement of the ground during the two phase faults.
## 3 Methodology
### Rock Physics Workflow
#### 3.1.2 Rock Physics Diagnostics  
[PERSON]'s contact theory and the modified Hashin-Strikman lower bound are used to derive theoretical estimations of the effective elastic constants of unconsolidated sand [29]. First, the aggregate's shear and effective bulk moduli are calculated, assuming that all grains are spherical and organized in a random pack with a porosity of 0.36. Next, the modified Hashin-Strikman lower bound is used to find the effective elastic moduli at a decreased porosity. Suspension-like rocks are best described with this bound. The Ref. [30] equation is used to introduce the impact of pore fluid. The stiff sand model estimates bulk modulus (K) and shear modulus (u) from the porosity and fluid characteristics using the HMHS \"contact cement\" sand model of [5]. Using elasticity equations, \(V_{p}\) and \(V_{s}\) are calculated from K and u [31]. The HM unconsolidated sphere pack model determines the high porosity velocity limit. At the maximum porosity level, a higher coordination equals a stiffer pack and, as a result, a higher velocity. In ref. [32], the authors devised a method for predicting \(V_{s}\) in porous rocks that is now widely used.  
Figure 3: Petrophysical inversion workflow.  
The rock physics models (RPM) that best fit the measured data were identified. These models establish a deterministic relationship between reservoir parameters (such as minerals, fluids, porosity, and geometry) and elastic properties (i.e., P-wave velocity \(V_{p}\), S-wave velocity \(V_{s}\), and density). We use these models to adjust the \(V_{p}\), \(V_{s}\), and density when they are missing or have inaccurate measurements. More importantly, we modify the elastic response for changes in porosity caused by compaction.  
We have utilized granular models to predict the P-wave velocity and response due to the elastic nature of the environment. Figures 4 and 5 illustrate that the stiff sand model better predicts the response of sand-rich sediments, while the soft sediment model better predicts the response of shales [33]. The modified [34] relationship is most suitable for \(V_{s}\) prediction, with a correlation coefficient as high as 0.9.  
Figure 4: Well logs showing one of the identified reservoir zones. From the left: Caliper, GR, deep resistivity, clay volume, porosity, saturation, depth tracks, density, \(V_{p}\), \(V_{s}\), AI, \(V_{p}/V_{s}\), and [PERSON]’s ratio (PR). The rock physics model curves are shown in blue, the original logs are shown in red, and the final edited curves are shown in black.