esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 2 Preliminaries and Problem Formulation
### Problem Description  
The dynamics of each motor is governed by [39]:  
\[\left\{\begin{array}{l}\dot{\theta}_{i}=\omega_{i}\\ \dot{\omega}_{i}=-\frac{K_{ii}K_{ii}}{j_{i}R_{i}}\omega_{i}+\frac{K_{ii}}{j_{i} R_{i}}u_{i}-\frac{1}{j_{i}}T_{Li}\end{array}\right.\ i=1,2,\ldots,n, \tag{1}\]  
where \(\theta_{i}\) is the angular position of motor \(i\), \(\omega_{i}\) is the angular velocity of motor \(i\), \(u_{i}\) is the control input, \(R_{i}\) is the total arm resistance, \(K_{ei}\) is the voltage feedback coefficient, \(J_{i}\) is the moment of inertia, \(K_{ii}\) is the electromagnetic torque coefficient, and \(T_{Li}\) is the load torque of motor \(i\).  
We consider that the parameters vary with time during the actual operation of the motor. These parameters such as the resistance, inductance can change with different temperature. Therefore, we set \(k_{i}=-(K_{ii}K_{ei}/j_{i}R_{i})\), \(b_{i}=K_{ii}/j_{i}R_{i}\) and \(c_{i}=-1/j_{i}\), where \(k_{i}=\bar{k}_{i}+\Delta k_{i}\), \(b_{i}=\bar{b}_{i}+\Delta b_{i}\), and \(c_{i}=\bar{c}_{i}+\Delta c_{i}\). \(k_{i}\), \(\bar{b}_{i}\), \(\bar{c}_{i}\) are the nominal parts of system parameters \(k_{i},b_{i},c_{i}\). \(\Delta k_{i},\Delta b_{i},\Delta c_{i}\) are the perturbed values. The parameter perturbation and load torque are unified as unknown composite disturbances, i.e., \(d_{i}=\Delta k_{i}\omega_{i}+\Delta b_{i}u_{i}+c_{i}T_{Li}\). Thus, Equation (2) can be written as follows:  
\[\left\{\begin{array}{l}\dot{\theta}_{i}=\omega_{i}\\ \dot{\omega}_{i}=\bar{k}_{i}\omega_{i}+\bar{b}_{i}u_{i}+d_{i}\end{array}\right. i=1,2,\ldots,n. \tag{2}\]  
Assuming that motor 0 is a virtual leader, the kinetic equation of the virtual leader is:  
\[\left\{\begin{array}{l}\dot{\theta}_{0}=\omega_{0}\\ \dot{\omega}_{0}=\bar{k}_{0}\omega_{0}+\bar{b}_{0}u_{0}\end{array}\right.. \tag{3}\]  
The objective of this study is to design a finite-time observer to estimate the speed and unknown composite disturbance in the absence of speed information, and the estimated value is fed back to the controller. The finite-time consensus protocol based on the output feedback is designed to make the state of each follower consistent with the state of the virtual leader, i.e.,  
\[\underset{t\to T}{\lim}\|\theta_{i}-\theta_{0}\|=0,\underset{t\to T}{\lim}\| \omega_{i}-\omega_{0}\|=0. \tag{4}\]
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
###### Abstract  
Using large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to those metrics. Orbits are defined by a set of Keplerian elements, and the relationship between those elements and the spatial and temporal coverage metrics are examined in order to maximize global and zonal (latitude-dependent) coverage. Additional optimization is performed by dividing a constellation into multiple orbit planes. An iterative method can be applied to this design process to compare the performance of current and previous designs. The main objective of this work is the design of optimized configurations of satellites in low Earth orbiting constellations to maximize the spatial and temporal sampling and coverage provided by its sensors. The key innovations developed are a new cost function which measures the temporal sampling properties of a satellite constellation, and the use of it together with existing cost functions for spatial sampling to design satellite constellations that optimize performance with respect to both performance metrics.  
constellation design; CYGNSS; GNSS reflectometry; SpOCK
## 2 Literature Review and Hypotheses
### Entrepreneurial Orientation and Firm Performance  
Entrepreneurial Orientation (EO) is an organizational attribute that exists when a company is entrepreneurial and has various interpretations and implications depending on the school of thought. This diversity in interpretations has led to different conceptual frameworks, which have fragmented the EO literature [37; 38]. Two main schools of thought are identified in the EO literature. On one hand, [PERSON] [39] and [PERSON] and [PERSON] [40] conceptualize EO as a unitary construct that reflects common qualities among entrepreneurial firms manifested through risk-taking, innovation, and proactivity. On the other hand, [PERSON] and [PERSON] [12] conceptualize EO as a multidimensional construct focusing on unique qualities that distinguish some entrepreneurial firms from others [37]. [PERSON] and [PERSON] expanded the original three dimensions by adding autonomy and competitive aggressiveness. It is worth noting that these two conceptualizations are not contradictory or incompatible [37] and that there is a consensus on viewing EO as a behavioral phenomenon at the company or strategic business unit level [10; 38].  
We adopted the approach proposed by [PERSON] [39] and [PERSON] and [PERSON] [40] due to its wider adoption in the literature [38; 41]. In the literature, the most common way to study EO is by analyzing its relationship with FP [10], with vast empirical evidence of this relationship using the three dimensions from [PERSON]/[PERSON] and [PERSON]. An example is the empirical study conducted by [PERSON] et al. [42], who evidenced that EO positively affected the performance of Indonesian firms. In the Chilean context, [PERSON] and [PERSON] [43] supported the positive influence of EO on the performance of businesses in a post-disaster scenario.  
Given that EO, understood as a dynamic capability, is highly contextual and immersed in organizational routines, it seems crucial to avoid a universalist approach [20]. Expanding the scope of companies examined in the literature to include Chilean industrial companies characterized by innovative culture, diversity, and dynamism would significantly enrich the understanding of how EO can influence performance. Research in similar contexts is very scarce. Considering the strong empirical support from prior studies on the link between EO and firm performance, the following hypothesis is proposed for the companies that will be analyzed:  
**Hypothesis 1 (H1).**_EO has a direct and positive effect on FP._  
Explaining only the direct effect of EO on FP provides an incomplete picture. To fully understand the role of EO in organizations, it is necessary to examine the indirect effect it can have through other variables [10]. The literature suggests that EO can lead to the creation and enhancement of other dynamic capabilities, one of which is IT capabilities [20].
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 5 Conclusions  
A method for a PSBU is proposed in this paper. This method uses the characteristics of the measured voltage to discriminate between power swings and faults. To make the characteristics more intuitive, PCA is applied. Moreover, an index \(P_{\text{UI}}\) is proposed, and its change rate is observed to detect faults during power swings. Since the coordinate basis that is needed in the calculation process of the method can be obtained in advance, the proposed method only involves simple multiplication and addition operations. Therefore, the proposed method requires few computational resources, and can be easily implemented. Moreover, it only needs the instantaneous values of the voltage and current when detecting faults during power swings, so the proposed method can deblock the protection relay quickly after a fault occurs during a power swing. Certainly, the proposed method has its limitations, particularly in terms of its resistance to transition resistance. However, this is not a significant concern, since the method is primarily used to differentiate between three-phase symmetrical short-circuit faults and power swings. The transition resistance in three-phase symmetrical short-circuit faults is typically very low. Consequently, the impact of transition resistance on the proposed method can be disregarded.  
To verify the effectiveness of the proposed method, many tests have been performed. The results show that this method can not only reliably block the protection relay after a power swing occurs, but also quickly deblock the protection relay after detecting the fault during the power swing. In addition, the proposed method was finally compared with a traditional method and a novel method in [27]. The comparison results indicate that the proposed method has an advantage in terms of response speed and is less affected by measurement noise.  
Conceptualization, H.W.; data curation, W.Z.; methodology, H.W.; resources, Q.Y.; software, Q.Y; validation, X.L.; writing--original draft, H.W.; writing--review and editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Project of the State Grid Sichuan Electric Power Company, under Grant 521997230003.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
Authors [PERSON], [PERSON], and [PERSON] were employed by the State Grid Sichuan Electrical Power Research Institute. Author [PERSON] was employed by the State Grid Sichuan Electrical Power Company. All the 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.  
Figure 15: \(\Delta\)P under different sampling frequencies for faults.
# Explanation of Air Quality Data Using Takagi-Sugeno Fuzzy Inference System
## 4 Takagi-Sugeno Fuzzy Inference System
Let us note that when working with an ANFIS, similar to using any other neural network, it is necessary to divide data randomly into the training, test, and checking datasets. In our work, to divide data into the mentioned sets, the ratio of 70%:15%:15% was used. Since neural networks represent iterative algorithms, a suitably chosen termination condition is also important. In MATLAB software, we can choose from two options for the termination condition--Error Tolerance and Number of Epochs. We set Error Tolerance equal to 0. We gradually changed the Number of Epochs value. We noticed an interesting phenomenon. In the beginning, we set this parameter to a high value. In some cases, the system error stopped changing after a certain Number of Epochs. However, after the next training run, the error decreased. This is an interesting observation, because in classical neural networks, if the error does not change once, the next training run does not reduce the error. This phenomenon occurs because the ANFIS is a structurally specific type of neural network. While in classical neural networks, the weights are determined based on the connections between the neurons of individual layers, in ANFISs, the weights are determined based on the nodes of the network. Therefore, we recommend running the ANFIS several times. First, a higher value should be chosen for the Number of Epochs (for example, 1000), and when the system error stops changing during training, it is advisable to choose a very low value for the number of training epochs (for example, from 5 to 10). If the error does not change, we can consider the training to be completed. If the error decreases, we continue training by using a small value for the Number of Epochs. However, it may happen that the error starts to grow, so we recommend saving the obtained FIS after each training run. Since MATLAB software computes the error for training and checking samples separately, it is necessary to evaluate the obtained FIS with respect to all used data together (i.e., training, testing and checking). We decided to use RMSE (Root Mean Square Error; see [29]) to evaluate the system we created because it is also used by other models implemented in MATLAB, with which we compared our results. The formula for the computation of RMSE is as follows:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{j}(y_{i}-\hat{y_{i}})^{2}}{j}}, \tag{6}\]  
where \(y_{i}\) is the actual \(i\)-th value of variable \(y\), \(\hat{y_{i}}\) is the predicted \(i\)-th value of the variable and \(j\) is the number of observations in dataset.
## 4 Discussion
### Efficiency Depending on Double- to Single-Strand Break Ratio  
The enhancement of DNA damage can be further analyzed by investigating the ratio of DSBs compared to SSBs. SSBs and DSBs are both types of damage that can occur to the DNA molecule. An SSB occurs when one of the strands of the DNA molecule is broken, but the other strand remains intact. SSBs can be caused by a variety of factors, including exposure to radiation. SSBs can be repaired by a process called base excision repair, in which the damaged base is removed and replaced with a new one. In contrast, a DSB occurs when both strands of the DNA molecule are broken. DSBs can be caused by exposure to ionizing radiation. DSBs are more severe than SSBs because they can lead to the loss of genetic information, chromosomal rearrangements, and cell death.  
Ionizing radiation is used in radiotherapy to deposit energy in the DNA of cancer cells, resulting in both SSBs and DSBs that cause DNA damage. DSBs are more detrimental than SSBs as they cause the DNA to break into two pieces, making recovery and repair extremely challenging. Since DNA is vital for cancer cell reproduction, lethal DNA damage prevents cell replication and leads to cell death, enabling tumor control through radiotherapy. By increasing the secondary electron yield transport to the DNA, the addition of GNPs can enhance damage. Therefore, it is worth exploring whether altering the nanoparticle size and volume of gold would generate more DSBs during irradiation. Figure 5 displays the percentage of DSBs to total strand breaks. The graph demonstrates that regardless of energy or the number of gold nanoparticles used, the amount of DSBs produced remains consistent. On average, the proportion of DSBs is 1.43%, with a population standard deviation of 0.188. These percentages display minimal variation, suggesting that the pattern of DSB damage remains unchanged. Thus, it appears that reducing nanoparticle size and increasing their number, while keeping the gold amount constant, does not increase the proportion of DSBs in the total number of strand breaks.
## 1 Introduction
In order to calculate the energy consumption correctly, the foot force distribution problem needs to be considered. Although the above studies have given a certain impetus to the high efficient trajectory planning and energy consumption research, the current energy models are either relatively simple or not suitable for hydraulic actuated quadruped robots. Therefore, a complete energy model as well as a universal foot trajectory for a hydraulic actuated quadruped robot needs to be studied. For hydraulic actuated quadruped robots, the hydraulic friction includes the Coulomb friction, static friction, and static decay friction. Therefore, the energy consumption due to friction is big and the heat rate must be considered in the energy model. In order to describe the robot motions better, a universal foot trajectory for a hydraulic actuated quadruped robot needs to be studied. Different gait parameters have to be included in the foot trajectory, and their influences on energy consumption should be studied. This work expounds our study of the quadruped robot foot trajectory in planar motions. The main purpose of this work is to study the effects of different gait parameters on energy consumption. Firstly, the kinematics and dynamics model of the SCalf robot are established. An energy model including the mechanical power and heat rate is set up to calculate the energy consumption. A trot gait foot trajectory based on the cubic spline interpolation is proposed. The energy consumption variation caused by different gait parameters are studied through simulations.
## 5 In-Line Application of LIBS in Industrial Production
### Waste Electrical and Electronic Equipment  
The amount of waste electrical and electronic equipment (WEEE) produced per year has increased exponentially in the last 20 years, reaching 50 million metric tons in 2018 [479]. WEEE is mainly composed of iron/steel, plastics, non-ferrous metals, glass, and printed circuit boards (PCBs). For efficient recycling and recovery of valuable materials from this increasing waste, new technologies for fast and accurate chemical identification of WEEE components are needed [412].  
Figure 20 shows an application of LIBS for the inspection of end-of-life PCBs from disassembled mobile phones [208].  
The optical sensors used for sample recognition within the measuring volume and the LIBS sensor (Figure 20a) were part of a large demonstrator system aiming at automated disassembly, separation, and recovery of valuable materials from WEEE [480]. The photograph of a PCB from a mobile phone (top) and a LIBS raster scan of the PCB with the obtained chemical image of Ta (bottom) are shown in Figure 20b. High intensities of Ta are represented by orange/red color. The LIBS scan of the complete PCB (108 \(\times\) 42 mm\({}^{2}\)) was  
Figure 19: LIBS in-line measurement of liquid converter slag at voestalpine steel plant. (**a**) Photograph of the laser-induced plasma at the slag surface in the slag ladle. (**b**) Concentration of major oxides obtained by calibration-based analysis vs. reference concentration (dashed line: ideal match). Adapted from [373].  
Figure 20: LIBS measurement of printed circuit boards (PCBs). (**a**) Schematic setup for optical inspection and LIBS analysis. (**b**) Photograph of PCB from a mobile phone (top); chemical image of Tantalum in the PCB measured by LIBS. Adapted from [208].  
repeated several times to penetrate the housings of the electronic components and to access the bulk material. The chemical information gained by LIBS can be used in the next step for selective laser unsoldering and removal of the identified components (e.g., capacitors) from the PCB [208].  
In related studies, LIBS sensors have been developed to control a hydrometallurgical Cu recovery process in discarded PCBs [412] and to analyze heavy metals and brominated flame retardants in polymers and WEEE pieces on a conveyor belt [347].
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
###### Abstract  
The uniaxial strength of \(2\times 2\) twill carbon fiber-epoxy composite with circular open holes is governed by the stress concentration induced by the notch, as well as by the variation of the material response with the stress localization. In this study, these concurrent phenomena were studied using an experimental-numerical approach, considering the effect of the composite heterogeneity. An innovative simplified mesoscale model using shell elements to replicate the woven pattern was developed. The material properties of the model were identified with a data-driven optimization scheme that minimizes the difference between the experimental full-field strain, measured with Digital Image Correlation, and the one predicted by the model itself. The elastic properties identified for the material at tow level are analogous to that of a unidirectional composite, with longitudinal and transverse moduli of around 102 GPa and 15 GPa, respectively. The calibrated model was used to analyze the stress concentration and localization induced by circular notches with increasing diameter. A size effect describing the increase in the carbon fiber tow strength following the increase in stress localization was observed. The mesoscale material strength was found to increase by about 2.5 times in the presence of the notch, from 2131 to 3922 MPa in fiber direction, partially balancing the stress intensification effect of the hole that leads to an approximately three-fold stress increase in the material.  
Digital Image Correlation; stress concentration; open hole; notch; twill; multiscale; FEMU 2023134300. [[https://doi.org/10.3390/app13074300](https://doi.org/10.3390/app13074300)]([https://doi.org/10.3390/app13074300](https://doi.org/10.3390/app13074300))
## 2 Literature Review  
### Urban Traffic Congestion and Control Strategies  
Urban traffic congestion remains a critical problem that directly impacts mobility, sustainability, and productivity in rapidly expanding areas. Traditional infrastructure and fixed-time signal control cannot fulfill the needs of increasing traffic demand. This has led to the development and implementation of real-time, traffic-actuated management systems capable of adaptation to stochastic traffic conditions in urban areas. The challenges of traffic control on urban networks have encouraged innovative actuated traffic control and management systems that use real-time data to enhance and improve traffic flow quality [20]. The implementation and usage of Artificial Intelligence (AI), the Internet of Things (IoT) concept, and big and open data analytics have sped up the development of traffic control strategies. The authors in [21] proposed an integrated ITS-AI framework that dynamically adjusts signal plans using real-time sensors and collected traffic data, obtaining a nearly 15% improvement in traffic flow efficiency and an almost 20% reduction in average travel times. Another approach is the use of machine learning systems using video feeds from urban traffic networks that achieve over 93% accuracy in identifying and responding to congestion patterns, optimizing traffic on Indian streets [22]. The inability to adapt existing traffic infrastructure to new traffic situations led to the development of new solutions for traffic-dependent strategies, with the usage of modern technologies, to enable a more harmonized and efficient traffic flow [23; 24]. With the focus on the implementation of the urban mobility concept, adaptive traffic control is one stable trend where real-time data collection and processing allow quick responses to variations in traffic situations. In the research presented in [25], proactive and reactive behavior for sharing traffic condition information is considered, where vehicles monitor the road traffic condition and proactively share this information when needed, considering adaptive multi-hop communication. Even though some researchers consider the main problem of traffic congestion to be that of inadequate intersection traffic control, the research in [26] developed an identification method to define the impact of both road infrastructure and traffic signal control. Another approach is the use of big data applications, which enable real-time predictive traffic management. The study in [27] introduces a traffic prediction framework based on multimodal data using machine learning techniques and validated with large datasets, making it a practical solution for real-time traffic management.