esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
###### Abstract  
In recent years, scholars have paid increasing attention to the joint entity and relation extraction. However, the most difficult aspect of joint extraction is extracting overlapping triples. To address this problem, we propose a joint extraction model based on Soft Pruning and GlobalPointer, short for SGNet. In the first place, the BERT pretraining model is used to obtain the text word vector representation with contextual information, and then the local and non-local information of the word vector is obtained through graph operations. Specifically, to address the lack of information caused by the rule-based pruning strategies, we utilize the Gaussian Graph Generator and the attention-guiding layer to construct a fully connected graph. This process is called soft pruning for short. Then, to achieve node message passing and information integration, we employ GCNs and a thick connection layer. Next, we use the GlobalPointer decoder to convert triple extraction into quintuple extraction to tackle the problem of problematic overlapping triples extraction. The GlobalPointer decoder, unlike the typical feedforward neural network (FNN), can perform joint decoding. In the end, to evaluate the model performance, the experiment was carried out on two public datasets: the NYT and WebNLG. The experiments show that SGNet performs substantially better on overlapping extraction and achieves good results on two publicly available datasets.  
joint extraction; overlapping entity; overlapping relation extraction; soft pruning; GlobalPointer
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
###### Abstract  
Industrial parts are increasingly being designed to be more lightweight in modern manufacturing for energy saving and material cost reduction. However, the high-speed motion of flexible systems tends to excite severe residual vibrations that result in positioning accuracy degradation and loss of productivity. This study proposes a closed-form trajectory optimization method for vibration suppression based on trapezoidal velocity profiles, which are most widely used in industrial robots and machines. First, the formulation and minimum time solution under actuator limits of the motion profile are defined. Then, the relationship between the trajectory parameters and the vibration response is investigated. It is shown that residual vibration can be eliminated by properly tuning the acceleration/deceleration switching times according to the natural frequency. Based on the derived vibration suppression conditions, a tuning procedure for time parameters compliant with actuator limits is established to generate fast and precise movement. A main advantage of the proposed method is easy implementation for general machines without requiring extra computational resources or modification to the control system. The effectiveness and practicality of the proposed approach are verified through experiments conducted on a robot. The experimental results show that the optimized trajectory achieves superior residual vibration reduction performance.  
Keywords:trajectory optimization; residual vibration; motion profile; motion control; trapezoidal velocity profile
## 4 Results and Discussions
### Effect of Falling Angle under Different Submergence Conditions
#### 4.2.1 Cases of Subaerial Landslides  
The falling angles (\(\theta\)) refer to the angle between the sliding direction of the slider along the slope and the positive x direction and are set as 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\), 60\({}^{\circ}\) and 70\({}^{\circ}\), respectively, and the submergence conditions of the landslides are subaerial landslide (the falling height is 0 m). When the falling angles are 30\({}^{\circ}\), 50\({}^{\circ}\) and 70\({}^{\circ}\), the temporal evolution of water elevation in the G\({}_{1}\) and G\({}_{2}\) point are shown in Figure 11. The most obvious wave propagation feature is that the oscillation at the back of the wave train intensifies as the falling angle increases at G\({}_{1}\). Specifically, there has been a significant attenuation of the fluctuation between 4 and 5 s at 30\({}^{\circ}\) at the back of the wave list, which diminishes at 50\({}^{\circ}\), and almost disappears at 70\({}^{\circ}\) (each wave has almost the same large amplitude in the entire wave train at 70\({}^{\circ}\)). This continuous large amplitude wave train caused by the sliding block falling with a large angle has serious perniciousness. Compared with G\({}_{1}\), the most obvious propagation feature at G\({}_{2}\) is attenuation, as the initial falling height of the slider is 0 m, which can also be reflected in Figure 9. It is worth noting that the maximum wave heights at G\({}_{1}\) and G\({}_{2}\) do not simply vary linearly with the falling angle. The maximum wave heights of 30\({}^{\circ}\) and 50\({}^{\circ}\) are basically the same and the maximum wave height of 70\({}^{\circ}\) is greater than either of them, which indicated that there is a complex pattern in the effect of falling angle on the generation and propagation of tsunami waves. This pattern will be discussed later in conjunction with the change of maximum falling angles with falling height.  
Figure 11: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with the subaerial landslide: (**a**) \(\theta=30^{\circ}\); (**b**) \(\theta=50^{\circ}\); and (**c**) \(\theta=70^{\circ}\).  
Figure 10: Setting diagram of the cases of falling angle under different submergence conditions.
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 1 Introduction  
It is widely agreed that electric vehicles (EVs) are a promising solution to relieve environmental issues. In fact, EV sales in the United States (U.S.) have increased yearly. Charging infrastructure is critical to the continued growth of EVs and its upstream industries. A lack of convenient and ubiquitous charging infrastructure is one of the key factors that impedes EV adoption [1]. The U.S. federal government moved swiftly to address this. An example is the EV Charging Action Plan [2; 3] that provides USD 7.5 billion to develop 500,000 public chargers nationwide by the Year 2030. The private sector has also responded positively to the government's leading effort. As an example, Tesla announced a commitment to open thousands of its \"Superchargers\" to EVs made by other manufacturers [4]. Currently, Tesla provides 28,000 charging ports at Supercharger stations in the U.S., which have been accessible primarily to drivers of the company's own cars until now. Nonetheless, the reality still looks quite far-fetched. The U.S. government aims for the average availability of EV charging stations to be every 50 miles [5] (compared to gas stations existent every 3.5 miles [6]), while low-income residents are more dependent on automobiles and must travel further to access jobs and essential services [7]. To this end, this paper lays out a theoretical framework of optimal EV charger deployment.  
We emphasize that our EV charger location optimization problem will very highly likely take a wide diversity of variables (including economic, societal, human behavioral, etc.) into account. As such, we set this paper to contribute to the following:  
* Building a _comprehensive mathematical framework_ accommodating the particular complexity,
* Demonstrating our numerical computational framework for solving the facility location problem (FLP) representing the optimal location;* Laying out an extensive comparative study among the optimization _solving techniques_ as an effort to find the most efficient solver;
* Applying the findings to two real-world _case studies_ representing an average and high density of EVs.
## 2 Materials and Methods
### Selection of Candidate Reference Genes and qPCR Primer Design  
Eight candidate reference genes previously reported to be suitable to transcript normalization in other organisms under different experimental conditions were selected. The sequences of candidate reference genes were obtained from the transcriptome _de novo_ assembly sequences of \(B\). _xylophilus_[38]. Primers were designed using Primer Premier 6 software (Premier Biosoft International, Palo Alto, CA, USA) according to the SYBR Green Master Mix (Tiangen, Beijing, China) manufacturer's instructions.
## 3 Semantic Expression of Relational Data Provenance
### Representation of Provenance Transformation  
In a closed relational database management system that supports data provenance, the process of generating relational data is called provenance transformation. Provenance transformation emerges at different levels of granularity, such as tuples, fields, and values. For the relational data of a given granularity, their provenance transformations form a transformation expression, explaining data creation. To accurately illustrate data provenance, this paper introduces provenance explaining transformation (PET), a method based on transformation expressions. Notably, PET can describe provenance transformation in queries and is exclusive to the provenance transformation of tuples and values.
## 2 Theory Background  
### The Theory of ACLHC  
Huffman coding is a variable-length source coding method which was proposed by [PERSON] in 1951 Huffman coding has been widely used in computer, data encryption, and communication fields because of its high efficiency. Huffman coding is a prefix code, which minimizes the average coding length Huffman coding (1951). The symbols with high probability are represented by short code words, while the symbols with low probability are represented by long code words. So, Huffman coding depends on the occurring probability of symbols. The closer the probability of each symbol appears, the higher the average coding length of Huffman coding is. For a symbol sequence, the ACLHC can be obtained as follows ([PERSON] and [PERSON], 1998; [PERSON] and [PERSON], 1998).  
Step 1: Count the occurring probability \(\{p_{1},p_{2},\cdots,p_{m}\}\) of each symbol in the symbol sequence. Construct the binary tree set \(F=\{T_{1},T_{2},\cdots,T_{m}\}\) based on the statistical probability \(\{p_{1},p_{2},\cdots,p_{m}\}\). In each binary tree \(T_{i}\), there is only one root node with the probability \(p_{i}\). The left and right subtrees of the binary tree are empty;  
Step 2: The two trees with minimum root node probability in the binary tree set \(F\) are selected as the left and right subtrees to construct a new binary tree. The probability of the root node of the new binary tree is the sum of the probabilities of the left and right root nodes of the subtree;  
Step 3: Delete the two trees with the minimum probability in the binary tree set \(F\) and add the new binary tree to \(F\);  
Step 4: Repeat Step 2 and Step 3 until there is only one tree in the binary tree set \(F\);  
Step 5: After the Huffman tree is constructed, the left child nodes of each father node are coded as 1 (0) and the right child nodes of each father node are coded as 0 (1). Search forward from the last binary tree, the Huffman coding of each symbol \(\{H_{1},H_{2},\cdots,H_{m}\}\) is the set of all codes on the corresponding path;  
Step 6: The average coding length of Huffman coding is obtained according to the length of the Huffman coding of each symbol and the corresponding probability, as follows:  
\[ACL=\sum_{i=1}^{m}p_{i}\cdot length(H_{i}), \tag{1}\]  
where \(ACL\) is the average coding length of Huffman coding, \(length(H_{i})\) is the number of 0 or 1 contained in \(H_{i}\).  
To better illustrate the calculation process of ACLHC, suppose a probability sequence {0.35, 0.30, 0.15, 0.10, 0.10}, the Huffman tree and corresponding Huffman coding is shown in Figure 1.  
The average coding length of the probability sequence is calculated by (1). Therefore, the average coding length of the probability sequence is 2.2:  
\[ACL=0.35\cdot 1+0.30\cdot 2+0.15\cdot 3+0.10\cdot 4+0.10\cdot 4=2.2.\]
## 4 Results and Discussion on the Survey
### Highlight from the Survey  
This survey offers valuable insights into the sustainability practices of Italian textile companies; however, several limitations must be taken into consideration. First, the sample primarily consisted of micro and small enterprises, which restricts the generalizability of the findings to larger companies that possess different resources and organizational structures. Additionally, there was a geographical bias, as most of the participating companies were in northern and central Italy. This regional concentration might not adequately represent the practices prevalent in southern Italy, where industrial infrastructure and conditions differ significantly.  
Another notable limitation arises from the reliance on self-reported data in the survey. This method introduces the potential for response bias, including the possibility of overreporting sustainability practices. Furthermore, the scope of the study was primarily focused on environmental sustainability, offering only limited consideration of social and economic dimensions of sustainability. Lastly, the cross-sectional nature of the research presents another constraint, as it provides a snapshot of sustainability practices at a single point in time, thus limits the ability to analyze long-term trends in the sector.  
It is important to keep these limitations in mind when interpreting the study's results.  
However, despite the limitations mentioned above, the results from the survey provide an important foundation for developing more targeted and effective strategies to promote sustainability in the Italian textile sector. To address some of the identified challenges, such as the need for continuous monitoring and a more comprehensive view of the data, the introduction of an operational model based on a real-time sustainability monitoring dashboard is proposed. This tool, using real-time data, would allow companies to have a more accurate and dynamic view of their progress towards sustainability goals, reducing the risk of errors from self-reported data and enabling more strategic resource management.
# Embedded One-Dimensional Orifice Elements for Slosh Load Calculations in Volume-Of-Fluid CFD
## 3 Results
### VOF Advection Test Case  
The first test-case assesses if the liquid-gas interface is propagated sharply in 1D orifice elements via the CICSAM scheme (Section 2.3.4). For this purpose, a liquid packet (Fluid 1) is propagated in a channel at a constant velocity of 1 m/s, as shown in Figure 6. The flow is inviscid with slip boundary conditions at the top and bottom boundaries, while the channel contains embedded 1D elements, as depicted in Figure 7. The results in this figure show the fluid interface before and after passing through the one-dimensional orifice elements, both with and without the proposed CICSAM modification. In the case where the centroid locations are not corrected, it can be seen that the interface smears considerably while passing through the one-dimensional orifice elements. However, with the modification applied, the fluid interface remains sharp and matches the expected position (white dotted lines), thus validating the proposed method.  
Figure 5: Nodes in orifice region (\(x_{1}\) to \(x_{2}\)) with their cell volumes, where the \(\times\)’s indicate the actual centroid of the cell volume.  
Figure 6: Sharp fluid interface test case.  
Figure 7: Fluid interface plot before Fluid 1 (red) traveled through the one-dimensional orifice elements (**top**). Fluid interface plot after Fluid 1 (red) traveled through the one-dimensional orifice elements without (**middle**) and with (**bottom**) the changes to CICSAM.
## 2 Literature Review
### Research Ideas  
Surrounding revenue management theory, scholars have explored and tested the core methods of revenue management in many fields, such as dynamic pricing and seat control strategies in air and railway transport. As the research progresses, the application of revenue management in railway transport mainly focuses on dynamic pricing and ticket allocation. However, within the existing literature, the research on the characteristics of China's HSR transport is not comprehensive enough, and the shortcomings can be summarized as follows.  
1. The existing dynamic pricing studies mainly focused on the characteristics of uneven distribution of passenger flow throughout a given year, with differential pricing for high and low seasons or differential pricing for the same Origin-Destination (OD) parallel train service index, ignoring the law of the distribution of passenger demand during the pre-sale period. In addition, because the intensity of passenger demand for tickets varies in different periods of the pre-sale period, the existing studies only considered the overall situation of passenger demand, and seldom considered the characteristics of dynamic passenger demand for tickets in different periods of the pre-sale period.
2. The existing ticket pricing and allocation joint optimization studies rarely considered the feedback relationship between the two. The dynamic adjustment of the ticket price will directly affect the passenger demand and, consequently, affect the ticket allocation scheme. In addition, the ticket pre-allocation scheme is also the basis for the implementation of dynamic pricing.  
To summarize, this paper analyzes the characteristics of dynamic passenger demand by combining the past ticket-purchasing data of passengers in each OD section during the pre-sale period. Aiming at maximizing train revenue and considering the full utilization of trains' seat capacity, a joint optimization method of ticket pricing and allocation for a single train was innovatively proposed.
## 4 Primary Findings
### Holistic Analysis of Findings
#### 4.1.4 Demanded I4.0 Enablers  
This category identifies the most demanded technologies and tools of I4.0 in Kazakhstan. Overall, interviewees mentioned 19 enablers. Most popular were the IoT (six mentions), big data (6), and enterprise resource planning (ERP) (5) (Figure 5). 1C systems, widespread in CIS countries, received three points [69]. For comparison, 3D printing, digital twin, artificial intelligence (AI), and robot process automation (RPA) had only two mentions. Unexpectedly, respondents mentioned cloud systems, virtual reality (VR), customer relationship management (CRM), and lean tools only once.  
According to interviews, one of the most popular elements of I4.0 in Kazakhstan is big data. One interviewee commented on big data as critical to improve processes (Q18). However, big data algorithms require raw data input in large amounts. This factor leads to the popularity of another I4.0 technology--the IoT, capable of production and collection of raw data (Q1). IoT, combined with big data, can significantly simplify the companies' work. Furthermore, enterprises consider ERP as a universal system to optimize many processes (Q19). However, some companies might overestimate their need for such complex systems and be unprepared to work with them properly (Q4).  
Many factors affect the failure risk of the I4.0 implementation projects. These include the low qualification of the workforce (Q20) and the cultural mentality. Some companies digitalize for the sake of digitalization, not knowing the benefits of I4.0. Moreover, some enterprises implement costly technologies not because of necessity but because of modern trends.  
We have two kinds of technology trends in our country: fashionable and necessary. The first one is part of the mentality, and it does not bypass the IT sector. If something is fashionable, then all companies start to use it right away. (Intervie-wee 05)  
In such cases, the competency of the top management is questionable. Therefore, issues may arise in both the implementation strategy and right timing to introduce new technologies.
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
###### Abstract  
This article addresses the issue of energy waste resulting from frequent braking of underground mine cars and proposes an optimization design to address this. The proposed solution involves the installation of a regenerative braking device within the mine cars to capture and reuse the energy wasted during braking. This implementation improves the endurance capabilities of the underground mine cars. The article begins by analyzing the working characteristics of underground mine cars and proposing a design optimization method based on regenerative braking energy. Subsequently, a regenerative braking device specifically designed for underground mine cars is introduced. Finally, through physical modeling, a comparison is made between the energy consumption of the underground mine cars before and after the installation of the energy recovery system, allowing for an estimation of the actual benefits of energy recovery. The results demonstrate that the regenerative braking system successfully recovers approximately 60% of the braking energy during operation, resulting in an improvement of around 20% in the endurance capabilities of the underground mine cars. This significant enhancement contributes to the improved energy utilization efficiency of coal mine electric cars, reducing system energy consumption and lowering CO\({}_{2}\) emissions.  
underground mine car; brake energy recovery; design optimization; mine car locomotive; physical modelling; range capacity +
Footnote †: journal: Applied