esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This work investigated the influence of an oxygen-free atmosphere on an adhesive bond between aluminum specimens. The following conclusions can be drawn.  
* The ambient atmosphere influences the tensile strength of aluminum-aluminum joints bonded with 2-component adhesives. Depending on the adhesive used, the oxygen-free atmosphere increases (adhesives based on epoxy and polyurethane) or decreases (adhesives based on acrylate) in tensile strength. Increases in tensile strength of up to 58% compared to the normal atmosphere are achieved.
* Bonding deoxidized substrate surfaces lead to an increase in the strength of the bonded joint compared to bonding oxidized substrate surfaces under constant ambient conditions. A pure consideration of the difference between oxidized and deoxidized is only carried out for the argon/silane atmosphere, since it has already been shown that the atmosphere also has an influence on the strength. Here, increases in tensile strength of between 7% and 38% were achieved for the three adhesives investigated.  
For the future, interfacial investigations on the exact bonding mechanism of the adhesive to the substrate surface are planned in order to be able to make a statement and, in the best case, a prediction about the strength to be achieved. Furthermore, XPS measurements are carried out to prove that the oxide layer can be completely removed by grinding. Since these measurements take place in UHV, an oxygen-free sample transport must first be developed so that an oxide layer does not already form during sample transport.  
Subsequently, based on the results of this work, as far as the strengths of the bonded joints are concerned, investigations into a possible improvement of the functional properties are to be carried out. Here, the thermal and electrical conductivity without an intermediate oxide layer will be investigated.  
**Author Contributions:** Conceptualization, S.G.; methodology, S.G.; formal analysis, S.G.; investigation, S.G.; data curation, S.G.; visualization, S.G.; writing--original draft preparation, S.G.; writing--review and editing, A.R.; supervision, A.R.; project administration, A.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--Project-ID 394563137--SFB 1368.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data is stored in the CKAN repository of the SFB 1368 and is available upon reasonable request.  
**Acknowledgments:** The authors would like to thank the colleagues of subproject B04 of the SFB 1368 for the productive discussions about the experimental environment. Likewise, the authors would like to thank the colleagues of subproject S01 of SFB 1368 for developing and providing the oxygen measurement.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
For the perceptual consistency, we use the outputs of several layers of the pre-trained VGG16 model to extract the abstract semantic information of the image and utilize the pyramid feature aggregation method to aggregate high-order and low-order features. Model training is supervised by minimizing the perceptual consistency loss between the generated image and the original image. For the data augmentation consistency, we compare the unprocessed areas of two disparity maps, which are obtained by feeding the original image and the image after data augmentation on some areas into the network. Figure 1: Illustration of the _color constancy hypothesis_ problem in previous self-supervised depth estimation task, where \(l^{l}\) and \(l^{r}\) represent the left and right view, \(d^{r}\) represents the disparity map of the right view, _ij_ represents the coordinates on an image, and RGB represents the RGB value on each pixel. Table 1 shows the comparison between the above approaches and PDANet in terms of data acquisition, generalization ability, limitations, and prediction effect. An example result from our algorithm is illustrated in Figure 2. Specifically, our contributions are as follows:  
1. Based on the baseline monocular depth estimation network, we integrate data augmentation consistency and perceptual consistency as supervised signals to overcome the color constancy hypothesis and image gradient disappearance in low-texture regions for previous work. 2. We propose a new unsupervised loss based on perceptual consistency to excavate the deep semantic similarity before and after image reconstruction so that the model still obtains genuine image differences in low texture and color fluctuation regions. 3. We also innovatively propose a heavy data augmentation consistency to solve the color fluctuation problem and greatly enhance the generalization of the model.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 3 Model Test Results
### Pile-Subsoil Relative Displacement  
In essence, the pile-subsoil differential settlement is what causes the soil arching in embankments. Figure 8 displays the monitoring findings of the pile-subsoil differential settlement in model experiments. Figure 8 demonstrates:  
1. The pile-subsoil differential settlement continues to grow during the embankment filling process, and the rate of growth gradually decreases. The final values of pile-subsoil differential settlement at the mid-span between four piles are 4.89 mm, 4.69 mm, and 4.58 mm, respectively, and the final values of pile-subsoil differential settlement at the mid-span between two piles are 3.85 mm, 3.69 mm, and 3.55 mm respectively. The different settlement at the mid-span between four piles is more extensive than between two piles. The pile spacings are always 500 mm. However, there is a difference between the spacing of diagonal adjacent piles and horizontal adjacent piles. As a result, the subsoil between four piles needs to bear a greater load.
2. As filling cohesiveness increases, the pile-subsoil differential settlement reduces. The different settlement at the mid-span between four piles in Test 2 and Test 3 decreased by 4.09% and 6.34%, respectively, and the different settlement at the mid-span between two piles in T2 and T3 decreased by 4.11% and 7.79%, respectively, compared with  
Figure 7: Effect of filling cohesion on Vertical stress in embankments.  
T1. It confirms that the pile-subsoil differential settlement, which is also caused by the variable spacings of diagonal adjacent piles and horizontal adjacent piles, is more susceptible to cohesiveness change at the mid-span between four piles than it is at the mid-span between two piles.
## 4 Discussion
### Mechanism of Pore Elimination Using Beam Oscillation  
In general, there are two main reasons for the formation of pores in deposited components: (1) hydrogen pores are formed by the absorption of hydrogen by the melt pool; (2) process pores are generated by the collapse of the laser keyhole. The pore morphology in this experiment is large in size, excluding hydrogen pores visible only at the microscopic level. Due to the low viscosity, high fluidity, and fast solidification of liquid magnesium alloy, it is extremely easy for the laser keyhole to collapse and form pores without oscillation of the beam. Process pores are more likely to form in deposited magnesium alloy components.  
However, it is obvious from the experimental results that the beam oscillation can suppress the pores. There are two main reasons. First, pores formed by the collapse of the keyhole tip are reduced because the high-speed movement of the laser beam helps to stabilize the laser keyhole [31]. According to Equations (4) and (5), the beam scanning speed \(V_{scan}\) can be obtained as a function of the oscillating frequency \(f\). As shown in Figure 14, the curve after the linear fitting of the function shows that the laser beam scanning speed increases with the increase in frequency. The calculation results of this paper show that the components are detected without porosity with the X-ray when the scanning speed of the laser beam is increased to 0.57 m/s(300 Hz). It is similar to the results (0.33 m/s) found by [PERSON] et al. in high-speed laser welding experiments on aluminum alloys [32]. Second, the scanning effect of the oscillating laser beam, melting the solidified part at the tail of the melt pool, not only releases the solidified porosity [33] but also helps to expand the volume and slow down the solidification rate of the melt pool, so that the bubbles have enough time to escape and reduce the porosity accordingly.  
Figure 13: Schematic diagram of hump formation during deposition.  
Furthermore, the stirred laminar flow generated with the beam oscillation can promote the escape of bubbles. When the beam is not oscillating, the bubbles formed by the collapse of the laser keyhole are very easily carried into the solidification front due to the temperature gradient melt flow. It follows that the pores in the component are easily affected by the state of the melt flow in the melt pool. According to the calculation of the Reynolds number in Section 4.1, the flow state in the melt pool is dominated by the high-speed stirred laminar flow. Therefore, as the oscillation frequency increases, the bubbles accelerate with the stirred laminar flow and escape from the melt pool faster. Meanwhile, the shearing effect of the stirred laminar flow will break the bubbles, thus reducing the size of the pores.  
\[V_{scan}(t)=\sqrt{\left[V_{d}+2\pi fros(2\pi ft)\right]^{2}+\left[2\pi frsin(2 \pi ft)\right]^{2}} \tag{4}\]  
\[V_{scan}(f)=\frac{\int_{0}^{T}V(t)dt}{T} \tag{5}\]
## 4 Discussion
### Influences on Hearing Preservation  
#### 4.4.1 Slow Electrode Insertion and Hearing Preservation  
According to previous research, slow insertion of the electrodes should reduce the insertion force [30] which should reduce the trauma from the insertion itself and reduce intracochlear fluid pressure, which also has a traumatic potential [31,32]. [PERSON] et al. [33] demonstrated that the standard continuous forward velocity surgeons achieve is 52.3 mm/min with a standard deviation of 19 mm/min. Studies definitively show that slower insertion speeds are associated with improved audiological outcomes [33]. We inserted the electrode in 2 min. As the active length of the slim straight electrode is 20 mm, we divided the active length into quarters and inserted one quarter of the electrode per half a minute.  
The slow insertion of the electrodes is said to be particularly important for preserving residual hearing, which is supposed to improve PSA [34]. In our research, we compared the results of tone audiometry, i.e., the preservation of residual hearing, in patients operated with and without the slow insertion of a slim straight electrode. We found that there was no significant change due to slow insertion. There is a possibility that atraumatic insertion was carried out even prior to using the slow insertion technique.  
In a study by [PERSON] et al. [35] an even lower degree of hearing preservation was achieved when using slow electrode insertion. The authors suggest that auditory insertion speed might be directly related to the consistency of insertion [34].  
On the other hand, the research by [PERSON] et al. [36] found that the speed of electrode insertion indeed influenced either complete or partial preservation of residual hearing. With complete preservation, the speed was 33.03 \(\pm\) 16.66 mm/min, and with partial preservation, it was 43.68 \(\pm\) 18.27 mm/min. However, it should be emphasized that in their group, in addition to the slow insertion of electrodes, other evidence-based practices were applied to preserve residual hearing, such as electrode selection (slim straight electrode), surgical technique (complete opening of the round window), etc. [36].
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 3 Sample Results
### TC-011: Accuracy of SOG/COG (Constant Speed)  
TC-011 evaluates the performance of the receiver in terms of SOG and COG errors at three different speeds. The SOG and COG error time evolution is shown in the left part of Figure 7. The DUT fulfills these test criteria for the three cases.  
Figure 5: Example results for dynamic accuracy test B (TC-003).  
Figure 6: Example results for the broadband interference test (TC-008).
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
###### Abstract  
Recently discovered pits on the surface of the Moon and Mars are theorized to be remnants of lava tubes, and their interior may be in pristine condition. Current landers and rovers are unable to access these areas of high interest. However, multiple small, low-cost robots that can utilize unconventional mobility through ballistic hopping can work as a team to explore these environments. In this work, we propose strategies for exploring these newly discovered Lunar and Martian pits with the help of a mother-daughter architecture for exploration. In this architecture, a highly capable rover or lander would tactically deploy several spherical robots (SphereX) that would hop into the rugged pit environments without risking the rover or lander. The SphereX robots would operate autonomously and perform science tasks, such as getting inside the pit entrance, obtaining high-resolution images, and generating 3D maps of the environment. The SphereX robot utilizes the rover or lander's resources, including the power to recharge and a long-distance communication link to Earth. Multiple SphereX robots would be placed along the theorized caves/lava tube to maintain a direct line-of-sight connection link from the rover/lander to the team of robots inside. This direct line-of-sight connection link can be used for multi-hop communication and wireless power transfer to sustain the exploration mission for longer durations and even lay a foundation for future high-risk missions.  
sensor network; cave exploration; mapping; navigation +
Footnote †: journal: Sensors
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### Experiment Results and Analysis
#### 4.4.3 Ensemble Learning (EL)  
The performance of the final modified system is obtained using ensemble learning (EL). The EL model performance is illustrated in Figures 7 and 8. Training and validation loss and accuracy for the game ship classification dataset are shown in Figure 7. The highest accuracy achieved in the game ship classification dataset is marked by the blue dot (98.38%).  
To indicate the accuracy of different class ship classifications, the confusion matrix for the game ship classification dataset is shown in Figure 8. The accuracy values demonstrate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No\_Fcn** & **Fcn1\_Out\_Features** & **Fcn1\_Out\_Features** & **Fcn1\_Out\_Features** & **Accuracy (\%)** \\ \hline
1 & 5 & - & - & 97.67 \\
3 & 1408 & 1408 & 5 & 97.56 \\
3 & 720 & 720 & 5 & 97.56 \\
3 & 256 & 256 & 5 & 97.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EfficientNetB2 Different feature vectors performance.  
Figure 8: Confusion matrix for game ship classification dataset.  
Figure 7: Training and validation loss and Accuracy for game ship classification dataset.  
the integration of the modified model with the optimization of hyper-parameters, then the ensemble learning for the best three models with confidence averaging, which gives higher accuracy than concatenation ensemble learning.  
The EfficientNetB2 precision performance matrices may be seen in Table 5. The accuracy of the classification system reached a total of 98.34% overall. The cargo and tanker classes scored the lowest precision, coming in at 97.86% and 95.75%, respectively, in their results. The military, aircraft carriers, and cruise ships were correctly classified at a rate of more than 98%. The results indicate that it performed well in terms of specificity and that it worked well overall.  
In Table 6, the results for the final modified model accuracy after optimization, concatenation and confidence averaging ensemble learning (EL) are presented. The three best performance models are combined using the two ensemble learning methods. Confidence averaging shows better performance.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 3 Proposed Method
### Detailed Design
#### 3.2.1 Data Division  
During the data preparation phase, FSM-BC-BSP employs a strategy to divide the graph data G into multiple partitions. A more intuitive partitioning strategy involves segmenting the large graph into different partitions based on the number of edges. This strategy is particularly effective on datasets where most vertices have a greater number of edges. However, it is important to note that such a partitioning strategy might face challenges with datasets exhibiting extremely unevenly distributed edges. In cases where the edges are distributed unevenly, this strategy could result in an unbalanced load distribution, potentially leading to nodes experiencing an overflow or saturation due to excessive computational loads.  
In the context of the FSM-BC-BSP algorithm, the number of partitions is a crucial parameter. Experimental evaluations are conducted to assess the algorithm's performance under different numbers of partitions. The objective is to strike a balance between load balancing and efficient parallel processing, considering the characteristics of the specific dataset being analyzed. Experimentation with varying numbers of partitions allows for an empirical understanding of how the algorithm performs under different configurations, guiding the selection of an optimal number of partitions for a given dataset.
###### Abstract
[37] developed an ANN model for a CO\({}_{2}\) chemical absorption capture plant and [PERSON] et al. [38] constructed an ANN-GCS model to predict the effectiveness of trap mechanisms for CO\({}_{2}\) sequestration in a saline aquifer. [PERSON] et al. [39] developed a neural network model to predict CO\({}_{2}\) plume migration in heterogeneous reservoirs from an injection well. Despite its strength and applicability, ANNs have not been implemented for optimal relief well design during CO\({}_{2}\) storage. Therefore, in this study, we propose an ANN model for optimal relief well design, targeting the Pohang Basin in South Korea, to determine optimum well location and operating conditions. An ANN model for relief well optimization was developed to identify the relationship between selected parameters, including geological properties, operating conditions, total storage capacity, and storage efficiency, and to predict the optimal location of the relief well. In order to generate the training and testing datasets, the optimization process for various injection scenarios using a relief well was performed in an actual field model. The training and testing procedures were executed using a model fitted in Python with Keras, based on the calculated coefficient of determination (\(R^{2}\)) and root mean squared error (RMSE) values. In addition, the validity of the developed ANN model was investigated with the results of a certain injection scenario, which were not included in the training datasets.
# Differential Privacy in Federated Learning: An Evolutionary Game Analysis
## 5 Numerical Results
### Utilities
#### 5.1.1 Two-Strategy Scenario  
We first consider the model under a two-strategy scenario, with simulations examining the impact of varying Gaussian noise powers on the utilities. The parameters are set as follows: \(a=10\), \(b_{1}=0.02\), and \(b_{2}=0.02\). The basic incentive for training is set as \(A=5\), with a fixed population share of \([0.5,0.5]\). Additionally, \(\sigma_{1}\) is held constant at \(0.1\), while the initial value of \(\sigma_{2}\) is set at \(0.1\) and incremented by \(0.02\) up to \(2.5\). The values of \(U_{1}\) and \(U_{2}\) are observed in the left figure of Figure 4.  
As shown in the left figure of Figure 4, the utilities of the two parties are initially identical. However, as \(\sigma_{2}\) increases, the utilities of both parties begin to decrease, with a more pronounced impact on \(U_{2}\). This decline occurs because the continuous addition of Gaussian noise to \(S_{2}\) affects the accuracy of model training, which, in turn, influences the size of the total incentive pool, ultimately leading to a reduction in the utilities of both parties.
## 3 Waveguide Design: Cut-Off Analyses  
The array period was computed in the previous section to satisfy the grating lobe-free condition. With reference to Figure 1, waveguides should be smaller than the array period (\(a<p\)) to provide a minimum metal thickness of 1 mm for manufacturability [21]. Therefore, the maximum waveguide sizes \(a\) are 6.8 mm and 8 mm for square and triangular lattices, respectively. Such sizes do not allow fundamental degenerate mode propagation in the operative band if square or circular/hexagonal waveguides are considered (cut-off frequencies would be about 22 GHz, preventing signal propagation from input port to the aperture in the operative frequency band), e.g., \(f_{c}=c_{light}/2a\) in the square waveguide. Dielectric filling has been proposed in [7] to lower the cut-off frequencies of such waveguides. Similarly to [5], in this work we instead propose the use of ridges to achieve fundamental mode propagation in the frequency band together with low losses (all-metal solutions) and easier manufacturing. When ridges are loaded into the waveguide, they effectively change the boundary conditions and the distribution of the electromagnetic fields. As can be seen in [22; 23] this significantly reduces the cut-off frequency of the fundamental modes (\(f_{c}<c_{light}/2a\)) without significant variation of the higher-order modes, leading to an overall increase in bandwidth.  
All-metal antennas are also generally preferred for space applications [24]. New manufacturing techniques such as Additive Manufacturing help not only to manufacture complex metal structures in one piece, but also to reduce the amount of material where it is not needed. In this way, the overall mass can be reduced without affecting performance. Metal structures provide better thermal performance in terms of higher dissipation and lower deformation. The absence of dielectrics also leads to better radiation efficiency (no dielectric losses).  
The design of the ridged waveguides in terms of cut-off frequencies are reported in the next subsections. Such non-canonical waveguides [22; 23] require a numerical approach to be evaluated. We adopted the mode solver of the CST Microwave Studio mode solver [25] in the present study.  
All the waveguides reported in the next sections will support two orthogonal degenerate modes (two modes having the same cut-off frequency and hence the same propagation constant). This feature is needed to avoid deterioration of circularly polarized signals. Circular polarization in a waveguide is indeed a combination of two orthogonal linearly  
Figure 2: Lattice geometry.  
polarized modes whose phase difference of 90\({}^{\circ}\) (as excited by the polarizer [26]) should remain constant during propagation in the waveguide.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### ELMO Model for Contextual Word Embeddings (CWE)
A number of authors [96; 97; 98] suggested many simplified ways to use ELMO contextual embeddings with domain-specific tasks. Some researchers used the final ELMO layer to predict the score, while others fused vectors, and many other researchers assessed the average of all layers.