esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance
## 5 Error Compensation
### Variable Parameter Error Compensation
To solve for the parametric error \(\Delta\mathbf{x}\), we bring in the Levenberg-Marquardt (LM) nonlinear damped least-squares method for the identification of the parametric error. The LM algorithm is optimized with respect to the Gaussian-Newton method in terms of iteration steps, and the damping factor \(\lambda\) is introduced to avoid problems such as the singularity caused by the generalized inverse matrix. The iterative equation based on the LM algorithm is  
Figure 12.0  
By iterative calculation, a set of compensation parameters \([\Delta\alpha,\Delta\beta,\cdots,\Delta\delta_{6}]\) can be obtained, which are brought into the inverse kinematic solution of the HKM trajectory planning to complete the position error compensation. Fourth, we judged the compensation effect. The maximum value, mean value, and standard deviation of the compensated residual error \(\Delta\mathbf{P}_{i}\) were calculated to judge whether the accuracy requirement is satisfied. If the accuracy requirement is not satisfied, the grid density was locally increased to identify the parameters again, and if the requirement is satisfied, it will continue to calculate the compensation parameters for the next grid. At the end of the cycle, the parameter error compensation values \([\Delta\mathbf{\alpha},\Delta\mathbf{\beta},\cdots,\Delta\delta_{6}]\) of the whole  
Figure 13a,b show the residual position error of the end-effector during the HKM lifting the outboard and inboard BBs, respectively. After analyzing the results, we can obtain that the end position error is significantly reduced after compensation, but there are still large position errors at the moments of 20 s, 40 s, and 100 s. The main reason is that the grid density of these positions is small. When the HKM end-effector position error varies a large amount on a certain time series, using a set of parameters for error compensation will lead to poor compensation at the start and end moments. The use of local grid encryption can reduce the amount of position error variation within a single grid, which can effectively improve the compensation effect. Figure 14a,b and Table 3 show the compensation effect of the process of the HKM lifting the left outboard and inboard BBs with three grid densities. The results show that the maximum error, mean error, and standard deviation decrease significantly when the grid density increases. The higher the grid density, the higher the accuracy of the HKM end position, but with it, the computational amount also becomes larger. Reasonably limiting the accuracy index of the HKM end-effector and using a local grid encryption method will maximally solve the problems of accuracy and computational amount.
# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
## 2 Improved Ant Colony Mapping Algorithm
### Initialization  
Heuristic information is defined as follows:  
\[\eta_{i,j}=\frac{importance(i)}{center(j)} \tag{7}\]  
The heuristic information of neuron \(v_{i}\) mapping to node \(r_{j}\) is determined by the importance of neurons and the centers of the nodes. The number of neuron connections represents its importance, \(importance(i)\).  
\[importance(i)=Fanin(i)+Fanout(i) \tag{8}\]  
The degree of the center of the node is determined by its Manhattan distance from the remaining nodes.  
\[center(j)=\sum\
olimits_{n=1}^{N}ManhattanDistance(n,\ j) \tag{9}\]  
Figure 1: Ant colony algorithm. (**a**) Ants follow a path between points A and E. (**b**) An obstacle is interposed. (**c**) On the shorter path more pheromone is laid down.  
The center degree of the node is the sum of its Manhattan distance from other nodes. This value reflects the communication ability of the node; the smaller the Manhattan distance, the higher the center degree of the node. The more neurons the output of the neuron is connected to, the greater the communication pressure of the neuron, so it should be placed on a node with a high degree of centrality. Therefore, the heuristic information should be proportional to the importance of neurons and inversely proportional to their centrality, so that neurons with high communication pressure are more likely to be deployed on nodes with strong communication capabilities.
# Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM
## 1 Introduction  
With the vigorous rise of a new round of industrial reform, the manufacturing industry has become the focus of global economic development. Major developed countries in the world have taken a series of significant measures to promote the transformation and upgrading of the intelligent manufacturing industry. In 2009, the framework of the \"Reinstabilization\" plan of the United States changed from revitalizing the manufacturing industry to vigorously developing the advanced manufacturing industry, actively seizing the strategic springboard of the world's high-end manufacturing industry and promoting the development of the intelligent manufacturing industry [1]. In 2013, the German government officially launched the implementation proposal of the German industry 4.0 strategic plan, put forward the concept of industry 4.0, and analyzed the vision, strategy, demand, limited action and other intelligent manufacturing fields of industry 4.0 [2]. France, Japan, South Korea, and other countries have also launched manufacturing revitalization plans. With the digital transformation, networked collaboration and intelligent transformation of manufacturing enterprises, intelligent manufacturing system solutions (IMSS for short) suppliers have become an innovative subject, with the deep integration of manufacturingtechnology and information and communication technology, and IMSS suppliers are the key force to promote the development of intelligent manufacturing. Since the Chinese government officially issued the made in \"China 2025\" document in 2015, China's intelligent manufacturing plan has been continuously implemented and achieved remarkable results, driving the vigorous development of intelligent manufacturing system solution suppliers. Traditional industrial system solution suppliers are actively transforming into IMSS suppliers. More than 12,000 suppliers of intelligent manufacturing equipment, industrial automation and industrial software have participated in various intelligent manufacturing projects [3]. However, the supplier quality and service level are uneven, which cannot effectively solve the pain points in the insufficient overall planning of intelligent factories and low integration of digital production line in the intelligent transformation of manufacturing enterprises, especially the insufficient substantive supply capacity of intelligent manufacturing system solutions, such as quality, cost, efficiency and delivery time [3], which poses hidden risks to the development of intelligent manufacturing formats. Therefore, the scientific prediction and evaluation of IMSS suppliers risks and helping manufacturing enterprises select appropriate IMSS suppliers have become important research issues in the field of intelligent manufacturing enterprises' selection and evaluation.
## 3 Results and Discussion
### Thermal Analysis  
Thermogravimetric analysis of the polymers carried out under ambient atmosphere revealed an onset of thermal decomposition at about 380 and 400 \({}^{\circ}\)C respectively for PPM containing 13.4% mol/mol and 6.1% mol/mol of repeat units with alkoxy side chains, respectively, while the maximum decomposition rates were observed at 482 \({}^{\circ}\)C (for 13.4%) and 508 \({}^{\circ}\)C for (for 6.1%), compared to 510-515 \({}^{\circ}\)C for PPM itself [5; 18].  
Differential scanning calorimetry (DSC) performed with the copolymers revealed in each case only a single glass transition temperature (Table 2). The glass transition temperature (T\({}_{\mathrm{g}}\)) of the copolymers decreases as the fraction of the octyloxy side chains along the polymeric backbone increases. The pure PPM of \(M_{\mathrm{n}}\) = 2400 g mol\({}^{-1}\) features T\({}_{\mathrm{g}}\)\(\approx\) 55 \({}^{\circ}\)C [2] while the PPM derivative containing 6.1% and 13.4% mol/mol of octyloxy side-chains exhibited T\({}_{\mathrm{g}}\) values of 48 and 31 \({}^{\circ}\)C, respectively (Table 2). This is likely due to the relative difference in mobility of the polymer chains, whose motion is progressively enhanced with the increase of the concentration of octyloxy substituents, leading to the corresponding decrease of T\({}_{\mathrm{g}}\)[20]. Moreover, the lack of any thermal transition beyond the glass transition temperature indicates that the copolymers are amorphous like PPM itself [6].  
Figure 3: \({}^{1}\)H NMR spectra. From the bottom to top: 4-octyloxybenzyl chloride (blue line), copolymer with 6.1% mol/mol (red line), and 13.4% mol/mol 4-octyloxybenzyl-containing constitutional repeat units (green line).
## 1 Introduction
However, similar to any other machinery, the characteristics of an articulated manipulator, such as its motion, vibration, rigidity, and force transmissibility, depend on its kinematic structure. For example, an articulated manipulator with an open-loop KC has a simple structure and a large workspace regarding its size. Conversely, those with a closed-loop KC are more rigid, precise, and stronger against payload [24]. The characteristics also vary depending on connectivity between links and the number of binary, ternary, and quaternary links. Hence, it is crucial to find the most suitable kinematic structure for the desired task when developing a new articulated manipulator. However, most studies have been limited to only traditional structures with simple open KCs [25], although they have various kinematic structures. There are only a few studies on the structural synthesis of articulated manipulators with complex shapes [26]. However, no research used graph theory to systematically enumerate all the KCs of articulated manipulators, including non-FKCs and FKCs. Therefore, the purpose of this research is to propose methods for generating non-isomorphic graphs of articulated manipulators that consist of a fixed base, an end-effector, and an intermediate KC that connects the two. Since a graph has a one-to-one correspondence with a KC and has several advantages in a systematic approach, graphs are generated instead of KCs. By taking a graph theory-based approach, the proposed method can systematically enumerate all the articulated manipulator kinematic structures. In addition, since utilizing the symmetry group theory can avoid the problem of generating duplicate graphs, the complicated and time-consuming process of isomorphism test can be skipped entirely. The synthesis results include all cases where the intermediate KCs are non-FKCs and FKCs. The paper is arranged as follows: Section 2 introduces the basic concepts used in this paper; Section 3 proposes systematic methods for generating graphs of articulated manipulators according to the types of 2-DOF intermediate graphs; Section 4 presents the results with the numbers of graphs enumerated and a few graphs with their conceptually designed kinematic structures; finally, Section 5 concludes with a research summary and future work.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 4 Conclusions  
This paper proposes and validates a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs) to address large-scale peak shaving in power grids. The core innovation lies in coordinating discrete BESS units to achieve equivalent continuous adjustable power, significantly enhancing flexibility and utilization. The specific conclusions obtained are as follows.  
1. This strategy addresses the peak-shaving challenge by coordinating the start-stop sequence of multiple constant power BESS units. This coordination produces equivalent continuously adjustable charging and discharging power at the grid level, enabling fine-grained aggregation and control of discrete storage devices. This paper illustrates the basic design principles of this coordinated variable-power strategy and develops an auxiliary optimization model that incorporates load forecasting, security constraints, and evaluation metrics.
2. The effectiveness of the proposed strategy is rigorously demonstrated through a comparative case study. The validation clearly demonstrates the superior overall peak-shaving performance compared to the constant power strategy of a single BESS.
3. Quantitative analysis confirms the significant advantages: improved energy storage utilization through phased collaborative scheduling, reduced equipment risk and flexibility constraints inherent in single BESS operation, and reduced system dynamic response requirements through the \"cluster-based stand-alone replacement\" model. All in all, this provides a reliable solution for large-scale energy storage participation in high-precision peaking applications.  
Conceptualization, P.C. and W.C.; methodology, P.C.; software, W.C.; validation, P.C., W.C. and J.S.; formal analysis, J.S.; investigation, B.X.; resources, J.S.; data curation, B.X.; writing--original draft preparation, D.L.; writing--review and editing, D.L.; visualization, D.L.; supervision, C.L.; project administration, C.L.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Project of State Grid Tianjin Electric Power Company (R&D2024-27).  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by Electric Power Research Institute, State Grid Tianjin Electric Power Company. Author [PERSON] was employed by State Grid Tianjin Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
BESS Battery Energy Storage Station  
SOC State of Charge
## 1 Introduction
### Waste to Carbon Concept in Waste Management  
The Waste to Carbon concept is the conversion of organic waste into valuable materials, including fuel with high carbon concentration. Low-temperature thermal-treatment technologies, such as torrefaction, were adapted for organic waste [10; 11; 12]. To date, torrefaction was mainly used for the improvement of biomass properties [13]. One of the proposed pathways for the waste-to-carbon process is the torrefaction of municipal solid waste (MSW), which turns it into carbonized refuse-derived fuel (CRDF) with a calorific value between 24 and 26 MJ\(\cdot\)kg\({}^{-1}\)[14]. In our previous study, significant increase of lower heating value (LHV) during torrefaction was found. [PERSON] et al. [14] showed an increase of LHV from 19.6 MJ\(\cdot\)kg\({}^{-1}\) in raw RDF to 25.3 MJ\(\cdot\)kg\({}^{-1}\) in CRDF. [PERSON] et al. [9] also showed the feasibility of CRDF pelletization. In this case, the lower calorific value increased from 21.0 [15] to 25.9 MJ\(\cdot\)kg\({}^{-1}\). [PERSON] et al. [16] also indicated an increase of Refuse-Derived Fuel (RDF) LHV from 19.7 to 21.2 MJ\(\cdot\)kg\({}^{-1}\) due to 90 min torrefaction at 220 \({}^{\circ}\)C. The evidence is growing that CRDF can be a replacement for bitumen and lignite coal or woody biomass [14]. CRDF solves the problem of MSW management, and particularly MSW of organic origin. Waste can be transformed into CRDF, thus not being landfilled and making it useable as fuel.
# Research Progress on the Mechanisms and Control Methods of Rockbursts under Water-Rock Interactions
## 4 Consideration of Water's Impact on Rockburst Prediction and Prevention
### Vision for the Future  
Additionally, future research could investigate the rock burst characteristics of different rock types under various water conditions and focus on developing new prevention and control technologies. Details are as follows:  
1. Rock explosion characteristics of different rock types in the water environment. Study the mechanical performance of different rocks in various water environments, to explore the water content, permeability and other factors on the rock strength, brittleness and stress concentration changes. This type of research can reveal the mechanism of water on different rocks in a particular environment, to help predict and control the conditions for the occurrence of rock bursts, especially in the wet or water-saturated underground engineering.
2. Development of new prevention and control technologies. For the risk of rockbursts in the water environment, new prevention and control technologies can be developed in the future, such as the use of nanomaterials or intelligent water control technology to change the mechanical properties of the rock, thereby reducing the occurrence of rockbursts. In addition, technologies that can actively regulate the water content of the rock body can be developed to control water infiltration and diffusion in order to enhance rock stability.
3. Intelligent monitoring and early warning system. Combined with advanced sensing technology, develop a system for real-time monitoring of the water content status and stress changes in the rock body, and use data-driven intelligent algorithms for rock explosion risk prediction and early warning. Such a system can provide accurate rock burst prevention information by monitoring the dynamic changes in water and rock, helping to take timely countermeasures during engineering construction.
###### Abstract
They carried out unsteady numerical calculation on the mixed-flow pump. On this basis, unsteady pressure pulsation on the blade surface was taken as the sound source, and the indirect boundary element method was used to numerically calculate the external noise caused by the rotating dipole source of the blade. [PERSON] et al. [11] concluded that the flow path spraying technology can effectively improve the anti-wear ability of the flow parts of the pump and reduce hydraulic loss, but its influence on the pressure pulsation characteristics is not clear. [PERSON] et al. [12] used dynamic grid technology to carry out a three-dimensional numerical simulation of the load rejection transition process of a model pump-turbine and analyzed the pressure pulsation of the turbine and the change characteristics of the runner force and the internal flow mechanism of the evolution. [PERSON] et al. [13] conducted non-steady constant value calculations for four operating points of the pump-turbine operating at low head and obtained four values at the inlet of the volute, the front of the runner after the guide vane, the upstream of the taper tube of the draft tube, and the inner side of the elbow tube. The pressure signal of the monitoring point was compared with the test results, and the pressure pulsation of the pump-turbine when the water head was abnormally low was studied. [PERSON] [14] measured the pressure pulsation at different positions of the vertically placed axial flow pump by experiment, and measured the pulsation under the working conditions of forward operation and reverse operation, and the study showed that under different operating conditions, the axial force of the impeller also changed as the speed at the inlet changed. The internal flow field of the pump is complex, and pressure pulsation testing has the disadvantages of difficult measurement, a long period, and high cost. The above research on the pressure pulsation and internal flow characteristics of pumps mainly focused on centrifugal pumps [1,3,4,11], axial flow pumps [6, 7, 8, 14, 15, 16, 17, 18], pump turbine [19, 20] and mixed flow pumps [21, 22, 10, 23]. Some research has also focused on turbines [24]. There are relatively few studies on the shaft-extension tubular pump [5,9], especially research on the pressure pulsation of the internal flow field of the shaft-extension tubular pump under multiple working conditions. In this paper, the axial flow pump of a pumping station in Nanjing, Jiangsu Province, is used for experimental research, and the three-dimensional turbulent flow numerical simulation and the actual machine test are used to study the pressure pulsation and flow characteristics of the axial flow pump under multiple working conditions. The internal pressure pulsation changes of the pump are analyzed, and the internal pressure pulsation characteristics of the pump are analyzed to reveal the real pressure pulsation distribution characteristics inside the axial flow pump to explain the influence of the pressure pulsation changes on the operation of the unit, so as to provide a reference for the stable operation and safety protection of the unit.
## 3 Cross-Layer Adaptive Feedback Scheduling
### Adaptive Feedback Scheduling Algorithm  
From the above analysis, the basic idea of feedback scheduling of WCSs can be stated as follows: with the goal of maximizing QoC, dynamically adjust the sampling periods of control loops to maintain the DMR at a desired level. From the control perspective, lower DMRs are always better. Therefore, large sampling periods should be used to avoid deadline misses.  
However, it is not easy to completely avoid deadline misses in a typical wireless environment. As also shown in Fig. 3, in order to reduce the DMR to a near-zero level, quite a large sampling period has to be assigned to each control loop. Unfortunately, according to sampled-data control theory, such a large increase in sampling period could degrade the QoC remarkably. In this context, the resulting QoC of the system may be adversely deteriorated, regardless of the decrease in the DMR. Therefore, in WCSs it is favourable to maintain the DMR at an appropriate non-zero level [10,21].  
Within the framework of feedback scheduling, we use a simple proportional control algorithm to adjust the sampling period:  
\[\Delta h(j)=K\cdot e(j) \tag{2}\]  
where \(K\) is the proportional coefficient, \(e(j)\)=\(\rho(j)\)-\(\rho_{r}\) is the difference between actual DMR and its desired value, and \(j\) is the index of the invocation instant of the feedback scheduler. Taking into account the constraint on the maximum allowable sampling period \(h_{max}\) of the control loops, the sampling period at \(j\)-th instant is then calculated by:\[h(j)=\min\left\{h(j-1)+K\cdot e(j),\ h_{\max}\right\} \tag{3}\]  
In the above algorithm, the proportional coefficient \(K\) and the DMR setpoint \(\rho_{r}\) are two key design parameters. Related design considerations are described below.