esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology
### Unsupervised Learning  
In this work, we employ graph contrastive learning to facilitate unsupervised learning for EA tasks. Graph contrastive learning has been demonstrated to enhance the performance of graph-based models by perturbing the input graph to achieve data augmentation through techniques such as masking, denoising, and dropping. In our study, we specifically adopt the masking method for augmenting KGs.  
**Momentum update.** First, two distinct perturbation ratios, \(\gamma^{q}\) and \(\gamma^{k}\), are utilized to mask the multi-hop neighborhood feature \(\mathbf{E}\) (the input of the Attention-based Transformer encoder), resulting two outputs \(\mathbf{H}^{q}\) and \(\mathbf{H}^{k}\). We then construct two Graph Transformer encoders: a query encoder (with parameters \(\theta^{q}\)) and a key encoder (with parameters \(\theta^{k}\)). The key encoder is structurally consistent with the query encoder. The outputs of these encoders are denoted as \(\mathbf{\tilde{H}}^{q}\) and \(\mathbf{\tilde{H}}^{k}\), respectively. Following the approach outlined in [51], we update the parameters of the query encoder, \(\theta^{q}\), using gradient back-propagation at each training iteration. Conversely, the parameters of the key encoder, \(\theta^{k}\), are not updated via back-propagation, but rather through the following mechanism:  
\[\theta^{k}\gets m\cdot\theta^{k}+(1-m)\cdot\theta^{q}, \tag{14}\]  
where \(m\in[0,1)\) represents the momentum coefficient. In our experiments, a relatively large momentum (for instance, \(m=0.999\), which is the default value) often yields better performance than a smaller value (such as \(m=0.9\)). This approach enables \(\theta^{k}\) to evolve more smoothly compared to \(\theta^{q}\).  
**Contrastive learning.** The fundamental principle of contrastive learning is to learn how to distinguish positive samples from negative ones across two different distributions. Following the general paradigm of graph contrastive learning [45; 52], _MPGT-Align_ aims to maximize the consistency between the outputs of two encoders. We employ InfoNCE [45] as a loss function that enforces consistency among entity embeddings produced by both encoders, while ensuring they are distinct from embeddings derived from other entities,  
\[\mathcal{L}_{infoNCE}=-\sum_{e_{i}\in E}log\frac{s(\mathbf{e}_{i}^{q},\mathbf{ e}_{i}^{k})}{s(\mathbf{e}_{i}^{q},\mathbf{e}_{i}^{k})+\sum_{j\
eq i}s( \mathbf{e}_{i}^{q},\mathbf{e}_{j}^{k})+\sum_{j\
eq i}s(\mathbf{e}_{i}^{q}, \mathbf{e}_{j}^{q})}, \tag{15}\]  
where \(\mathbf{e}^{q}\in\mathbf{\tilde{H}}^{q}\), \(\mathbf{e}^{k}\in\mathbf{\tilde{H}}^{k}\), \(s(\mathbf{e}_{i}^{q},\mathbf{e}_{i}^{k})=e^{d(\mathbf{e}_{i}^{q},\mathbf{e}_{ i}^{k})/\tau}\), \(d()\) denotes the dot product operation, and \(\tau\) represents a temperature hyperparameter. Guided by this loss function, _MPGT-Align_ is optimized through backpropagation to learn entity embeddings.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Static Sink-Based Frameworks  
Static sink-based solutions are the most popular WSN data-gathering solutions among the investigated categories. In these frameworks, a static sink is assumed to be either inside or outside the ROI, depending on the application scenario. The static sinks are assumed to have unlimited power sources and are connected to the control center.
## 2 Experimental Investigation
### Test Rig Description  
The test rig used in this paper consists of a rotating disk submerged and confined in water (see Figure 2). The disk is made of stainless steel and it rotates at \(f_{t}\) = 4.1 Hz by means of a transmission belt driven by a motor (Mavilor MLV-072) (Mavilor Motors S.A., Santa Perpetua de Mogoda, Spain), which rotates five times faster than the disk (20.5 Hz). The rotating speed is controlled and stabilized with a computer. The excitation and response signals are transmitted from the rotating to the stationary frame through a Michigan S10 slip ring. This system is mounted at the tip of the shaft. This test rig has been used in previous studies to analyze the dynamic behavior of the submerged disk-like structures [26; 28; 29; 33; 34; 35].
# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 8 Various Optimization Problems
### Cost Optimization  
First of all, in [39] we can see the optimization of the cost of creating the cloud data center. The problem consists of creating a project and a model of the mentioned cloud data center, taking into account things such as infrastructure, fault possibility etc. Data center main parts are: power, cooling, and IT. Each of these systems are described in detail. Then the project was divided into subsystems, which consist of simple blocks. Thanks to that it is possible to calculate the availability and to calculate the cost of the projected architecture. Then, the optimization is needed in order to find the best cost solution while taking into account the constraints. The authors used algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithms (GA) to achieve this goal [39].  
Creating such a model, with many places, transitions, and tokens turns out to be extremely complex to compute. To reduce the complexity, and get rid of the heavy computing problem, the authors decided to use a surrogate model with the use of GP. Contrary to other proposed solutions such as neural networks, GP does not need a huge training dataset to be useful. They used a kind of GP called design and analysis of computer experiments (DACE), often referred to as kriging. In the original mean,kriging is a statistical interpolation method based on GP to interpolate complex functions. Moreover, kriging is used to represent the input/output mapping of expensive computational models. It is now also connected with problems such as regression and identification [40]. A sampling strategy of Latin Hypercube Sampling (LHS) was adopted in this case. The authors evaluated the surrogate to ensure a low error in comparison to the original model. Thy created different scenarios with:  
* exponential covariance, optimization via genetic algorithms with a maximum of 1000 generations;
* with similar configuration, only changing the covariance to Gaussian;
* with Gaussian covariance and Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method with 1000 start points.  
The results of creating the surrogate model were satisfying, with low RMSE. The whole problem was also tested, but these results focus mostly on choosing which one from three presented optimizing algorithm performs best [39].
## 5 Experimental Validation and Benchmarking
### Experimental Validation of Design 2  
Design 2 is fabricated and tested using the same measurement setup. Figure 20a,b shows a photograph of the prototype with the FSS wall inserted between the two monopole antennas. Figure 20c shows the reflection and transmission coefficients of Design 2 along with the corresponding measurement data. It can be seen that both simulated and measured results are in reasonable agreement. The reflection coefficient is below \(-10\) dB from \(24\) GHz to \(27\) GHz. The mutual coupling is \(-28\) dB at \(24\) GHz and below \(-35\) dB in the entire target frequency range. In this case, the discrepancies between the simulation and measurements are smaller, and their origin is of the same nature as explained in the previous section. Figure 21 provides a comparison of the simulated and measured radiation patterns.  
Figure 19: Design 1: simulated (gray) and measured (black) radiation patterns at \(25.5\) GHz: (**a**) H-plane, (**b**) E-plane.  
Figure 20: Fabricated prototype of Design 2, (**a**) perspective view, (**b**) back view, (**c**) simulated and measured S-parameters.
## 3 Proposed Algorithm
The process begins with each agent observing the current state (\(s_{t}\)) and selecting an action (\(a_{t}\)) based on the Actor network's policy. The environment then provides a reward (\(r_{t}\)) and the next state (\(s_{t}^{\prime}\)), which are stored as transitions (\(s_{t},\ a_{t},\ r_{t},\ s_{t}^{\prime}\)) in the replay buffer. During the learning process, a mini-batch of transitions is sampled to update the Critic networks using the TD3 loss function. Finally, the Actor network is updated based on the deterministic policy gradient. Each agent learns by sampling \(B\) transitions randomly from a replay buffer. These transitions are used to update the agent's policy and value networks. The reward is generated based on the actions taken by the agents, which are determined from the information observed in the environment. The proposed environment is dynamically changing at every time slot, and it has the characteristics of generating large-scale tasks due to user density. By utilizing the characteristics of TD3 described above, this study aims to effectively solve the problem of determining task partitioning ratio in an HSR environment. In particular, the approach ensures stable and efficient task partitioning ratio decisions, even in environments characterized by dynamic MECS loads, varying numbers of HSTs, and high task generation rates caused by dense user traffic. Figure 3: Task partition ratio decision algorithm flowchart using TD3.
# Numerical Calculation of Slosh Dissipation
## 5 Results
### SPH-Flow Solver Results
#### 5.1.5 Three-Dimensional SPH Oil Results  
In the oil 3D test-case \(E_{diss}\) is evaluated with four resolutions and--as for the 2D simulations--remains fairly close to each other. From Table 1, the oil 3D results overestimate the experimental data by approximately 7%, while 2D simulations undervalue the dissipation by approximately 13% and this is linked mainly to the missing friction effects on the lateral walls (for more details, see also [42]).  
It is important to point out that the experimental uncertainty and its effect on \(E_{diss}\) are unknown. The experimental value of \(E_{diss}\) is calculated using Equation (12), where just small shifting errors between the forces and the tank velocity measures may imply non-negligible errors on the dissipation. For the above reasons, an in-depth investigation on the experimental side is still ongoing [11].  
Figure 14 depicts the time histories of different energy components, including real, turbulent and numerical dissipation obtained for the maximum spatial resolution \(N=75\). Analogous to the 2D results, in the water case, the turbulent energy dissipation is the largest, while in the oil case, the dissipation due to the real viscosity is dominant.  
Figure 14: Energy dissipation due to real, turbulent and numerical viscosity for 3D simulations at the maximum resolution \(N=75\). (**Left**) water test-case. (**Right**) oil test-case.  
Figure 13: Test with water: free-surface at the first roof impact. Three-dimensional simulation. (**Top**) Three-dimensional view, (**Bottom**) lateral and from above views.  
In Section 3, the role of center of mass motion of the fluid is highlighted in the dissipated energy. The top plot of Figure 15 depicts the time histories of the vertical position \(y_{G}(t)\) of the fluid center of mass for the water test-case evaluated by the SPH-Flow solver for both 2D and 3D framework. Remarkably, the two time histories are very close, up to \(t=10\)T, while large differences appear in the rest of the evolution. However, in the first ten periods, about 85% of slosh dissipation takes place. In the bottom plot of Figure 15, the \(y_{G}(t)\) time histories are reported for the oil test case. As already commented above, the differences in the flow behavior between 2D and 3D are larger here. In particular, the plots of Figure 15 show that the fluid center of mass in 3D is subjected to a larger motion, which is related to the higher slosh dissipation reported in Table 1.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 2 Previous Use of InSAR over Australia  
The spatial and temporal coverage of InSAR data over mainland Australia has been variable, and overall, the density of satellite acquisitions has been less than that over other continents. Situated in a relatively stable intraplate setting, mainland Australia has an absence of active volcanoes (as recognised by the Smithsonian Global Volcanism Program: [[http://volcano.si.edu](http://volcano.si.edu)]([http://volcano.si.edu](http://volcano.si.edu))) and low-to-moderate seismicity compared to the world average [25], both of which have been used to inform past and present satellite acquisition strategies [26]. However, large-magnitude (M\({}_{w}\) > 6) earthquakes do occur [27], and intraplate seismicity tends to be unusually shallow [28], therefore being more likely to cause ground displacements and/or surface rupture [29]. Coseismic deformation has been observed with InSAR in the South West Seismic Zone (Figure 1), including the \(M_{w}\) 4.4 2005 Kalannie earthquake sequence, one of the smallest magnitude earthquakes linked to ground deformation detected using InSAR [30], and the 2007 Katanning \(M_{w}\) 4.7 earthquake, which caused up to 320 mm of displacement in the satellite line of sight [30; 31].  
Other peer-reviewed InSAR studies of Australia document ground deformation induced by anthropogenic activities, predominantly underground longwall coal mining in New South Wales,which is linked to horizontal and vertical displacements of 10 s of mm/y and 100 s of mm/y, respectively [32; 33; 34; 35; 36] (Figure 1). Elsewhere, regional-scale InSAR surveys have been implemented, including in the Gippsland Basin, Victoria. Here, ALOS data spanning 14,000 km\({}^{2}\) were used to measure subsidence arising from open-cut mining and groundwater extraction and subsequently to assess the susceptibility of nearby coastal regions to sea-level rise [37]. Similarly, in the Surat Basin, Queensland (Figure 1), regional-scale InSAR reveals more than 20 mm/y of subsidence due to the extraction of coal bed methane and associated groundwater [38]. Monitoring of the Surat Basin includes a network of 40 corner reflectors installed by Geoscience Australia to tie relative ground displacements recorded by InSAR to GNSS [39] and also act as a calibration site for Sentinel-1 [26].  
The diversity of the above studies reflects the wide range of applications of InSAR data in Australia, but the small number of peer-reviewed studies shows that the uptake of this technique has been relatively slow. This is likely related to the sparsity of data, particularly in the north and west of the continent (e.g., Figure 1), although this is subject to change with the launch of new satellite missions. At the time of writing, the second half of the Sentinel constellation, Sentinel-1B, has begun to acquire data Australia-wide at 12-day repeat intervals. Such increases in data coverage will aid in enabling wider application of InSAR to measure ground deformation driven by anthropogenic and geological/hydrogeological factors.
## 5 Conclusions  
This paper presents a Golay3 sparse-aperture system for practical engineering applications. The design scheme of the beam steering and adjustment structure is described in detail and the actual adjustment capability is simulated. Wave aberration detection is carried out on the three light paths of the beam steering and adjustment structure. The RMS of the three light paths reaches 0.08\(\lambda\), 0.10\(\lambda\), and 0.08\(\lambda\), respectively. After the overall system assembly is completed, the infinite point target is simulated with a large-diameter collimation, and the CCD image surface imaging diagram is used as the detection object. Through rough adjustment, point target imaging is achieved, and the converged beams of the three subapertures interfere with each other on the CCD image surface. With the USAF1951 resolution test panel display 70 m away from the system as the surface target and the image of subaperture 1 on the CCD as the reference, the _PZT_s of subaperture 2 and subaperture 3 are adjusted, respectively, to obtain the synthetic image after the correction of the cophasing error. The beam steering and adjustment structure proposed in this paper is the first time it is applied to the sparse aperture system, and it can effectively control the piston error within 55 nm and the tilt error within 0.25 \(\upmu\)rad. Compared with other alternatives, it has the advantages of simple structure, convenient operation, and high precision. Furthermore, this design can be applied to other sparse-aperture systems.  
Conceptualization, J.Q. and Y.T.; methodology, Y.T.; software, X.H.; validation, X.W. and H.L.; formal analysis, X.H.; investigation, J.Q.; resources, X.W.; data curation, H.L.; writing--original draft preparation, J.Q.; writing--review and editing, R.Z.; visualization, X.H.; supervision, R.Z.; project administration, X.W.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Major Science and Technology Projects in Sichuan Province (2019 ZDZX0038).  
Not applicable.  
The data that support the findings of this study are available from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Evaluation and Comparison of Pear Flower Aroma Characteristics of Seven Cultivars
## 4 Discussion
These results further demonstrated that pear flowers mainly presented a sweet-orange or citrus-like flavor; these aroma descriptions were also consistent with the actual sensory characteristics. To the best of our knowledge, this was the first paper focused on preferred aromatic series and their components. PCA is a multivariate technique used to analyze data in which observations were described by several inter-correlated quantitative dependent variables and samples [41]. HCA is suitable for classifying samples into different classes or clusters. Due to the significant similarities or differences in the quantities of the identified volatiles, Anli flower was discriminated from the others and existed as an independent category, indicating that the green leaf flavor was the preferred aromatic property; Bayuesu was similar to Xizilu flowers, discriminated from the others indicated that the citrus flavor was the preferred aroma correlated with the typical odorants of 1-octanal, 1-nonanal, 1-decanal with aldehyde odor [24; 51]; in contrast, Bayuesu showed the highest diversity to KorlaXiangli flower because of the significant differences in the identified volatiles. It can be concluded that cluster analysis based on aroma components performed well in the discrimination of the flowers of different pear cultivars. To some extent, we also found the cluster results were consistent with the species difference (Table 1) and genetic relationship between cultivars. According to the obtained results, the PCA and HCA of volatile compounds can be successfully applied for the aroma evaluation of pear flowers, and the cultivars' differences were critical for the overall pattern. As reported, several key volatile chemical compositions had previously been found to be bio-activator to plant species [52; 53]. Here, we showed that the pear flower contained phenolic compounds such as hydroquinone, and its derivatives were investigated for their most important antioxidant-active and skin depigment [3]; linalool can repel agricultural pests such as aphid _Myzus persicae_; the C6 and C9 aldehydes and alcohols such as 3-hexenol, 2-hexenol, 1-hexanol were produced by plants in response to the physical injury and played an important role in plants defense strategies and pest resistance;11 furthermore, eugenol exhibits an excellent bactericidal activity against a wide range of organisms [54]. In addition, volatiles of 1-octanal, 1-nonanal, 1-dodecanal, 1-undecanal, 1-decanal, (\(E\))-\(\beta\)-damascone, \(\alpha\)-farnesene, benzaldehyde, 6-methyl-5-hepten-2-one, 2-hexenal were commonly found in diverse plants, and it had been recognized that the biological behavior responded by the blend of volatiles rather than individual compound [55].
## 4 Results and Discussion
### Maximum Proposed Systems' Productivity  
In light of previous discussions, the evaluation of the currently proposed systems will be based on the number of needed productivities (electrical power, fresh water, and cooling/heating capacity). Figure 9a-c shows comparisons of the two proposed systems (MGS-I and MGS-II) at the maximum systems' productivity under the same operating conditions. The maximum \(\dot{W}_{\it{net}}\), \(\dot{m}_{\it{fresh}}\), \(\dot{Q}_{cooling}\) within the ranges of all studied parameters are 102.3 kW (MGS-I and MGS-II), 141.5 kg/h (MGS-II), and 20.77 kW (MGS-I and MGS-II), respectively.  
Figure 9: Comparisons of maximum proposed systems’ productivity and performance: (**a**) \(\dot{W}_{\it{net}}\); (**b**) \(\dot{m}_{\it{fresh}}\); (**c**) \(\dot{Q}_{cooling}\).  
Correspondingly, systems evaluation can also be measured by space-supplied air conditions (ta4 and wa4) to maintain human comfort conditions inside the conditioned space. Figure 10 illustrates all available air-conditioned space supply conditions on the psychrometric charts for all studied parameter ranges relative to the space condition (25 \({}^{\circ}\)C and 12 gv/kga). The accepted conditions of space-supplied air are shown in the figures to be ta4 = 15.5-18.2 \({}^{\circ}\)C and wa4 = 9.2-12.00 gv/kga for systems MGS-I and MGS-II. Otherwise, the conditions outside are not desirable.  
Figure 11a shows the limits of supplied conditions (ta4 and wa4) of conditioned space for two proposed systems (MGS-I and MGS-II) at any \(\dot{W}_{net}\) for all studied parameter ranges. Moreover, Figure 11b displays the values and limitations of \(\dot{W}_{net}\)_and_\(\dot{Q}_{cooling}\) at any \(\dot{m}_{fresh}\) for two systems and all studied parameter ranges. Figure 11 can help researchers, solar multi-generation developers, power plant designers, and investors relate the proposed systems' productivities (electricity, space cooling, freshwater) in the design stage to select the suitable multi-generation system based on the needed application.  
Figure 10: Accepted supply conditions to air-conditioned space (systems evaluation) for MGS-I and MGS-II.
# Parameter Estimation of a Class of Neural Systems with Limit Cycles
## 1 Introduction
### Contributions  
Inspired by these works, we propose four parameter estimation algorithms for the FHN neuron system with limit cycle and external disturbance. The contributions of this paper include the following:  
* We formulate the FHN neuron system as an identification model based on the explicit forward Euler method.
* We propose a recursive least-squares algorithm and a stochastic gradient algorithm to estimate the unknown parameters of the model.
* We extend the innovation concept in [24], and explore the multiinnovation recursive least-squares algorithm and multiinnovation stochastic gradient algorithm for parameter estimation of the FHN neuron system.
* We show that a faster convergence rate and better accuracy can be achieved using the innovation and repeated available data.