esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 1 Introduction
We discover that current unsupervised methods [10; 11; 12; 13; 14] all consider the similarity of the one-hop neighborhood of the aligned entities. Through analysis of the relevant data, we find that, on the one hand, a little difference between the aligned entities in the one-hop neighborhood will affect the recognition effect of EA model and, on the other hand, that the aligned entities exhibit certain similarities in the multi-hop neighborhood. As depicted in Figure 1, there is frequently some interference in the one-hop neighborhood of the entity \"Jackie Chan\", while there is a considerable amount of similarity in the multi-hop neighborhood. Therefore, we propose a novel unsupervised EA method based on multi-hop pruning, called _MPGT-Align_, which implements efficient modeling of multi-hop neighborhoods of aligned entities. Specifically, we first model the textual information of entities by leveraging both their names and context-dependent information within KGs. Then, we enhance a technique called Pruning-hop2 Token to extract multi-hop neighborhood information from KGs. This approach allows for complex multi-hop operations to be executed offline prior to model training, thereby avoiding an increase in the training load of _MPGT-Align_. Furthermore, we develop an attention-based Transformer encoder [15] for advanced feature learning of the previously extracted multi-hop neighborhood features. In line with established practices in related work, we employ a contrastive learning mechanism to optimize this Transformer encoder. Finally, we introduce a consistency-similarity function aimed at enhancing recognition accuracy for aligned entities. In summary, our contributions are outlined as follows:  
* We propose an unsupervised EA method, _MPGT-Align_, based on multi-hop pruning Graph Transformer. _MPGT-Align_ features a streamlined structure and supports training with small batch sizes, enabling its application to EA tasks within large-scale KGs. * We enhance a feature aggregation technique for multi-hop neighborhoods, termed Pruning-hop2 Token, which is optimized from the original hop2 Token approach. This allows each entity in complex KGs to be represented as a sequence of tags. * We introduce an attention-based Transformer encoder designed to augment the features of entities within KGs. On the one hand, this encoder aggregates neighborhood features into entity representations, resulting in sequences of tag embedding that preserve neighborhood information across different hops. On the other hand, it adaptively learns the significance of neighborhoods at various hops to further enhance the performance of the EA model. * Extensive experiments conducted on two well-established benchmark datasets demonstrate that _MPGT-Align_ not only significantly outperforms 26 state-of-the-art models, but also exhibits remarkable scalability and robustness.
## 3 Results and Discussion  
### Laboratory Results  
Figure 5 shows the effect of pipe diameter and burial depth on the peak uplift resistance of pipes buried in non-reinforced sand. The results show that the peak uplift resistance increased with increasing pipe diameter. For 100 mm burial depths, the peak uplift resistance increased from 36.48 to 49.8 N as the pipe diameter increased from 25 mm to 55 mm. Similar trends were observed for 150 mm burial depths with the peak ultimate resistance increasing from 49.92 to 76.8 N as the pipe diameter increased from 25 to 55 mm. For both burial depths, the peak uplift resistance values are related to the pipe with a diameter of 55 mm. It is clear from this figure that pipe diameter and burial depth have a great influence on the results of the peak uplift resistance. Actually, there is a direct connection between the peak uplift resistance values of pipes and their diameters. Specifically, it was found that there is a big difference in the peak uplift resistance values from D = 25 mm to D = 35 mm, while the mentioned variance is decreased from D = 45 mm to D = 55 m significantly. In this regard, [PERSON] [70] mentioned that the difference in the peak uplift resistance values turn out to be lesser in the case of greater pipe diameters.  
Figure 6 shows the effect of the geogrid layout on the peak uplift resistance of sand. The geogrid layout was varied by including different geogrid lengths and numbers of layers. The results in Figure 6 show that increasing the geogrid length and number of layers significantly increased the peak ultimate resistance of the buried pipe. Inclusion of geogrid reinforcement changed the uplift resistance to the displacement response of the sand. In the non-reinforced sand, the uplift resistance increased with the increasing strain up to a peak value, followed by strain softening, whereas in the reinforced soil the uplift resistance increased monotonically with the increasing strain. The results in Figures 5 and 6 show the influence of pipe diameter, burial depth, geo-grid length and geo-grid number oflayers on the uplift resistance of pipes and justifies them as appropriate input parameters for the artificial neural network modeling for the prediction of the peak ultimate resistance.  
Figure 5: The effects of burial depth and pipe diameter on the peak uplift resistance of pipes buried in sand. (**a**) pipe diameter; (**b**) burial depth.
# Angle-Regulating Rule of Guide Vanes of Variable Geometry Turbine Adjusting Mechanism
## 1 Introduction
### Calculation Settings  
The regulating rule of the deflection angle of guide vanes with driver displacement was solved. First, the heat-fluid-solid coupling calculation was carried out for the calculation model through FLUENT. It obtained the thermal deformations of the two adjusting mechanisms. Second, the flexible bodies of components were obtained through ANSYS. It established a rigid-flexible coupling model of multiflexible bodies with two adjusting mechanisms. Finally, the kinematic and dynamic analysis of the model was carried out  
Figure 4: Three-dimensional model of three drivers’ adjusting mechanism with push–pull rod. 1—Inner casing, 2—Linkage ring, 3—Outer casing, 4—Hydraulic cylinder, 5—Pull rod, 6—Push rod, 7—Linear bearing, 8—Connecting rod, 9—Inner support ring, 10—Guide vane, 11—Arm II, 12—Arm I, 13—Pin.  
Figure 3: Three-dimensional model of three drivers’ adjusting mechanism with paddle rod. 1—Base, 2—Hydraulic cylinder, 3—Piston rod, 4—Linkage ring, 5—Pull rod, 6—Paddle, 7—Bearing block, 8—Shaft, 9—Guide rod, 10—Pin, 11—Arm I, 12—Arm II, 13—Guide vane, 14—Outer casing, 15—Inner casing, 16—Inner support ring.  
through Adams. It is a three-dimensional rigid-flexible coupling calculation model, including thermal deformations of components and multiflexible bodies. The experimental data of the institute provide the boundary conditions. The gas inlet temperature was 1273.15K. The inlet temperatures of cold air were 473.15K, 573.15K, and 673.15K, respectively. Table 1 shows the pressure and temperature boundary conditions at gas and cold air inlet and outlet. Figure 5 shows the inlet and outlet of gas and cold air. The cold air enters the outer channel from the cold air inlet (Inletcold1). Cold air enters the lower chamber through the inner holes of the guide vanes and positioning sleeves. At the same time, another part of cold air enters the main channel through the small holes on the guide vane body. The cold air and hot gas are mixed in the inner channel. The positioning sleeve is fixed at the lower end of the guide vane. The cylindrical surface of the positioning sleeve is designed as a spherical surface to reduce friction. The temperature of the inner channel is very high. The positioning sleeve is prone to jamming due to thermal expansion. Table 2 shows the computation method and model conditions. The computational domain is the entire fluid domain and the solid domain. The fluid type is compressible mixed gas. The turbulence model is k-\(\epsilon\) Model. The two cascaded sides are periodic symmetric boundaries. The other walls are nonslip adiabatic walls.
## 2 The Proposed Methods
### Discrete Wavelet Transform  
Wavelet transform is used to discuss the global and local characteristics of signals according to the expansion and contraction of the wavelet function. In the process of DWT, the signal can be decomposed into multi-level analytic decomposition by scaling function \(\varphi(x)\) and wavelet function \(\psi(x)\), as shown in Equation (1) and Equation (2), respectively:  
\[\varphi_{j,k}[t] =2^{j/2}\sum\
olimits_{k}c_{j,k}\varphi\left[2^{j}t-k\right] \tag{1}\] \[\psi_{j,k}[t] =2^{j/2}\sum\
olimits_{k}d_{j,k}\psi\left[2^{j}t-k\right] \tag{2}\]  
where,  
\(j\): is the analytic hierarchy  
\(k\): is the operational index  
\(c_{k}\): s the scaling coefficient  
\(d_{k}\): is the wavelet coefficient  
While \(d_{k}=(-1)^{k}c_{-k+1}\), using different wavelet families will result in different coefficients.  
Assume that the measured signal after sampling is \(v(t)=(v_{0},v_{1},\ldots,v_{N-1})\) and the number of sampling points is \(\mathrm{N}=2^{j}\); the DWT of \(v_{j}[t]\) decomposes from analytic level \(j\) to analytic level \(j-1\), as follows:  
\[DWT\left(v_{j}[t]\right)=2^{(j-1)/2}(\sum\
olimits_{n=0}cA_{j-1,n}\varphi \left[2^{j-1}t-n\right]+\sum\
olimits_{n=0}cD_{j-1,n}\psi\left[2^{j-1}t-n \right]),0\leq k\leq\frac{N}{2^{j}}-1 \tag{3}\]  
where,  
\[cA_{j-1,n}=\sum\
olimits_{k}c_{j,k}v_{j,k+2n},0\leq k\leq\frac{N}{2^{j}}-1 \tag{4}\]  
Figure 1: Flow chart of the proposed system.  
\[cD_{j-1,n}=\sum_{k}d_{jk}v_{jk+2n},0\leq k\leq\frac{N}{2j}-1 \tag{5}\]  
\(cA\) is the approximated coefficients and signals that high resolution has decreased to low resolution; \(cD\) is the detailed coefficients and solution signals between high and low resolution; \(n\) is the operation modulation parameter, as shown in Equation (6):  
\[v_{j}=v_{j-1}\oplus w_{j-1} \tag{6}\]  
where, \(v_{j-1}\) is the approximate decomposition signal of the original signal and \(w_{j-1}\) is the decomposition signal of the detail change of the original signal.  
Through Equations (1)-(6), the original function of \(v_{j}\) is transformed from the base of \(\varphi_{jk}(x)\) to the base of \(v_{j-1}\oplus w_{j-1}\). \(v_{j-1}\), which can be further decomposed into \(v_{j-2}\) and \(w_{j-2}\). Hence, the decomposition signals of approximation are decomposed continuously to obtain a series of \(w\) detail vectors and \(v\) approximation vectors of the lowest resolution space, as shown in Equation (7), i.e., multi-level analysis of the wavelet transformation, as shown in Figure 2:  
\[v_{j}=v_{0}\oplus w_{0}\oplus w_{1}\oplus w_{2}\oplus\ldots\oplus w_{j-1} \tag{7}\]
## 3 Materials and Methods  
### Proposed Method  
#### 3.1.1 Dataset Description  
To explicitly integrate concurrent purchase information, we represent order histories as natural language sentences. We then formulate the problem of predicting the next purchase item by optimizing BERT's Next Sentence Prediction objective, enabling the model to capture the nuances of consumer behavior and improve the accuracy of e-commerce recommendation systems.  
Table 1 shows an example of _[PERSON]'s_ raw data. As it is an e-commerce service based in Korea, both the original Korean text and its English translation are provided side by side to facilitate understanding for readers of this paper. Each row represents a purchased product. order_id is the order identifier, product_name is the name of the product, category_name is the name of the category, attribute_values are extended attributes, and initial_paid_at is the purchase timestamp. It is important to note that the second and third rows with order_id 2 (*) indicate that these products are purchased concurrently within the same order.  
We also considered using other purchase data; however, datasets containing only IDs do not utilize BERT's ability to understand item attributes, so they were not used. NBR (next basket recommendation) data were also not used because the problem of concurrent purchases is distinctly different from NBR. The Online Retail dataset [29] was also considered because it includes purchase data with natural language information about items. The Online Retail dataset comprises 22,034 orders, each characterized by 8 distinct features, including \"InvoiceNo\" which serves as an order identifier, and was collected from 10 January 2011, to 9 September 2011. However, this dataset represents wholesale transactions, which differ fundamentally from typical e-commerce data (i.e., _[PERSON]'s_ dataset). Therefore, we decided not to use this dataset.  
When comparing the purchase statistics of users with multiple purchases, _[PERSON]'s_ data show an average of approximately 3.77 purchases, a median of 3, and a maximum of 47. In contrast, the Online Retail dataset exhibits a significantly higher average of approximately 21.51 purchases, a median of 15, and a maximum of 543. Additionally, there is a marked difference in the number of users with concurrent purchases between the datasets. In _[PERSON]'s_ data, 6002 out of 16,840 users, or about 36%, have made concurrent purchases. Meanwhile, the Online Retail dataset shows that 4373 out of 4194 users, or approximately 96%, have made concurrent purchases. Even when matching the timeframes to align with _[PERSON]'s_ data (from April 18 to July 17), the Online Retail dataset still has 1864 out of 1984 users, or about 94%, making concurrent purchases.
## 4 Ultrastrong Coupling: Results and Discussion
### The Case of Small Bloch-Frequency
The spectrum defined by Equation (29) corresponds to the physical picture of RBO as a superposition of three partial motions: (i) a single quantum transition from the ground to excited state (or vice versa); (ii) the series of periodic quantum transitions between different states with frequency \(\overline{\Omega}\) (RO); (iii) the spatial oscillations as a comprehensive whole, with frequency \(\Omega_{B}\)(BO). Of course, the validity of this picture is essentially strongly limited, in view of the fact that the travelling waves are used as a model of excitation. But if we go to the wavepacket with strongly defined momentum, this picture will be adequate too, and is supported by the numerical calculations presented below. The similar general scenario of the complex dynamics manifests itself in a lot of other physical systems. In particular, as one more example, the coherent control of charge fluxes in unbiased molecular junctions may be considered, and is discussed in [65]. Such control is induced by resonances between the Rabi-oscillations driven by a pumping laser field, and the bridge mediated tunneling oscillations between the lowest unoccupied molecular orbitals of the donor and acceptor. One can see that the resonance condition [65] corresponds to Equation (29) for \(m=0\). The frequency given by Equation (30) is associated with the Rabi frequency in [64]. The frequency of intra-molecular oscillations driven by the donor-acceptor tunneling in [65] may be identified as a Bloch frequency in Equation (29). We will comment only on the analysis and physical interpretation of the dominative lines, which will keep their resolvability in real experimental conditions. The spectra in Figures 5 and 6 are clearly separated into low-frequency and high-frequency regions. The low-frequency part consists of the single line \(\omega\approx\Omega_{B}\), while the high-frequency part consists of the different sub-harmonics (29). The low-frequency part corresponds to the ordinary BO; the high frequency spectrum is governed by the RO. The dipole moment and inversion densities are presented in Figures 7 and 8, and corresponding spectra in Figures 9 and 10, respectively. Figure 6: Frequency spectrum of the density of the tunneling current with RO and BO in the separated frequency ranges. \(t_{a}=t_{b}=0.04\). All other parameters are identical to Figure 5.  
Figure 8: Space–time distribution of the inversion density with RO and BO in the separated frequency ranges. All parameters are identical to Figure 3.  
Figure 7: Space–time distribution of the dipole moment density with RO and BO in the separated frequency ranges. All parameters are identical to Figure 3.  
As one can see, the dipole current and inversion densities are modulated by the Bloch frequency. The reason is the mutual influence of BO and RO. The low frequency component \(\omega\approx\Omega_{B}\) corresponds to the mutual influence of RO and BO in the so-called RBO regime.
# Study of Cyclic Plasticity and Creep Ratchet Behavior of PTFE
###### Abstract  
Due to its superior corrosion resistance and low coefficient of friction, polytetrafluoroethylene (PTFE) is extensively used in the aerospace, machinery, chemical, and pharmaceutical industries. However, PTFE components encounter complex alternating stresses, resulting in ratchet and creep, which will affect the component's reliability. It is therefore necessary to clarify the PTFE's resistance to ratchet and creep. In this paper, uniaxial ratchet and tensile creep experiments were conducted at five temperatures on a PTFE dog-bone tensile specimen. At various temperatures and stress levels, the effects of average stress and stress amplitude on the cyclic plastic behavior of PTFE were investigated. It is demonstrated that the ratchet strains and strain rates at 23 \({}^{\circ}\)C are greater than those at 50 \({}^{\circ}\)C. The reason for this is that the PTFE material exhibits different crystal states at these two temperatures. At temperatures above 50 \({}^{\circ}\)C, the ratchet strain and ratchet strain rate increase with temperature. At temperatures above 100 \({}^{\circ}\)C, the ratchet strain and ratchet strain rate of PTFE materials increase more rapidly due to the glass transition. By analyzing the creep strain and ratchet strain of specimens subjected to varying levels of average and amplitude stress, it was discovered that the creep strain and ratchet strain caused by the average stress under the same stress increment were greater than those caused by the amplitude stress.  
PTFE; cyclic plasticity; ratchet; tensile creep +
Footnote †: journal: Applied Sciences
## 4 Result and Discussion
According to the accuracy performance comparison of machine learning methods on test data, the prediction model utilizing the random forest algorithm has yielded the best result with an accuracy rate of 87%. When the prediction models using other algorithms are ranked in terms of success, decision trees have an accuracy rate of 81%, KNN has 79%, SVM has 67%, and linear regression has 64% accuracy rate.
## 5 Results
### Radio Map of the Area  
Figure 10 illustrates the generated radio map of the experimental area and the test movement routes. Each layer of the map corresponds to the received RSSI signals from each receiver. With four receivers utilized, the radio map comprises four distinct layers. Each point is associated with coordinates obtained from the odometer, and the color of the point reflects the RSSI value: red indicates higher values, while blue indicates lower ones. As observed from the radio map, the signal strength decreases as the distance from the corresponding receiver increases.  
Figure 9: Three scenarios of robot movement: (**a**) first scenario (**b**) second scenario (**c**) third scenario.  
However, it is important to note that some points deviate significantly from the general trend of RSSI variation. This deviation may be caused by signal reflections from walls and interference.
### Objectives  
The aim of this research is to develop advanced tomato-image-preprocessing techniques in the context of gravimetric analysis applied to robotic harvesting. The study is unique as it aims to comprehensively analyze the impact of image-preprocessing techniques on the performance of two-dimensional (2D) correlation matrices, especially in CNNs, which has not been previously addressed in such a context. We propose new techniques and hypotheses that significantly improve the accuracy and efficiency of automated tomato harvesting.  
1. The development of improved preprocessing techniques: This research introduces an improved tomato-image-preprocessing technique for robotic harvesting systems, involving the integration of part affinity fields (PAFs) and confidence maps. This provides improved accuracy for tomato-center-of-gravity localization, extending the capabilities of previous approaches focused mainly on fruit detection and localization.
2. Quantitative improvement and practical applicability: In our study, a series of structured experiments were conducted to evaluate the improvements made by these methods. We evaluate the practical applicability of these methods in different real-world agricultural scenarios, providing a detailed understanding of the relationship between pretreatment and CNN performance.  
Our study aims to address the following gaps:  
1. The lack of attention to preprocessing in CNNs: Our study fills this gap by focusing on how image preprocessing affects the accuracy of CNN models, especially in precision agriculture.
2. Improved robustness and adaptability: The data augmentation and multispectral image-enhancement techniques used in this study improve the robustness and adaptability of the proposed model, given the variability in environmental conditions and tomato varieties.
3. Innovative approach to key point localization: This study focuses on the localization of key points, particularly the center of gravity, offering a new perspective and achieving high accuracy in this area.  
The initial hypotheses of this study suggest that advanced image-preprocessing techniques integrated with CNNs can significantly improve the accuracy of tomato detection and localization, improving the efficiency of automated harvesting in real-world environments. This research represents a significant advancement in agricultural automation, affecting crop management and harvesting. It lays the foundation for future advances in agricultural technology.  
Thus, the aim of the study is not only to present a new image-preprocessing technique, but also to demonstrate its effectiveness in practical applications, filling a critical gap in agricultural automation.