esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Analysis  
The thermal analysis was conducted in a synthetic air atmosphere, over the temperature range of 25-900 \({}^{\circ}\)C, and revealed that the thermal behavior differs from one sample to the other. The TG/DTG curves are presented in Figure 2. For the undoped sample, there is a 2.13% loss of mass in the range of 25-689 \({}^{\circ}\)C and then a 0.2% loss of mass in the range of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Space Group**} & **Lattice Parameter** & **Unit Cell Volume** & **Crystallite Size** \\ \cline{3-5}  & & [Å] & [Å] & [Å] & [nm] \\ \hline \multirow{2}{*}{LaMnO\({}_{3}\)} & \multirow{2}{*}{R-3c} & a = b = 5.4749(5) & \multirow{2}{*}{345.9341} & \multirow{2}{*}{25.8} \\  & & c = 13.327 & & \\ \multirow{2}{*}{LaMnO\({}_{3}\):Ga} & \multirow{2}{*}{Pm-3m} & a = b = c = 3.8677(7) & 57.8584 & 17.3 \\ \multirow{2}{*}{LaMnO\({}_{3}\):Y} & \multirow{2}{*}{Pm-3m} & a = b = c = 3.8687(4) & 57.9007 & 26.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The refined unit cell parameters for undoped LaMnO\({}_{3}\) (R-3c space group) and Ga-/Y-doped LaMnO\({}_{3}\) (in Pm-3m space group) samples.  
Figure 1: (**a**) X–ray patterns for undoped and Ga- or Y-doped LaMnO\({}_{3}\) materials synthesized by sol–gel technique. The hkl planes for the cubic structure in the Pm-3m space group in the case of Ga- or Y-doped LMO according to JCPDS card no. 01-4075-0440 and for R-3c space group in the case of pristine LaMnO\({}_{3}\) according to JCPDS01\(-\)089\(-\)0678 are marked on the graph. (**b**) X-ray patterns in the 20 = 57–70 \({}^{\circ}\) range.  
690-820 \({}^{\circ}\)C. Also in this last interval, an exothermic process with a \(\Delta\)H = \(-\)29.48 J \(\cdot\)g\({}^{-1}\) is observed on the HF curve.  
In the case of the Ga-doped LaMnO\({}_{3}\) material, a mass loss of 5.11% (Figure 2) is observed in the range of 25-600 \({}^{\circ}\)C and a mass loss of 2.8% is observed over 600 \({}^{\circ}\)C. This last decomposition process is accompanied by an exothermic process, visible on the Heat Flow curve, with \(\Delta\)H = \(-\)22.59 J \(\cdot\)g\({}^{-1}\). The Y-doped LaMnO\({}_{3}\) shows a higher mass loss than in the undoped and Ga-doped samples. In this case, the mass loss is 7.02% in the range of 25-600 \({}^{\circ}\)C. Over 600 \({}^{\circ}\)C, an exothermic process takes place with \(\Delta\)H = \(-\)27.15 J \(\cdot\)g\({}^{-1}\) and with a mass loss of 3.01% of the sample mass (Figure 2).
## 3 Fundamental Concepts
### Sensing Prioritization  
Prioritization is an effective mechanism to improve the overall performance of wireless sensor networks. This because the information transmitted by sensors may have different importance for the overall monitoring quality along the time, and this principle may be exploited. Thus, depending on the nature of the applications, sensors may have different importance levels, e.g., some may be sensing more critical data or they may be responsible for triggering some action. By doing so, the network may provide differentiated services for the most relevant source nodes and their transmitted packets; for example, a transport protocol could prioritize packets from more relevant nodes, assuring faster and more reliable delivery, or a routing protocol could relay more relevant packets through better paths.  
Actually, prioritization in wireless sensor networks may have local or global scope and some works have been concerned on optimizations based on one or both of these priority scopes [42]. This work is concerned with global prioritization, since assigned priorities are significant for the entire Smart City scenario. In this work, we define an Event of Interest \(e\) in a set of events \(E\) as any event that is relevant to the point of changing the priority of at least one sensor \(s\) in a set of sensors \(S\), increasing or decreasing its priority level. Any event of interest in the proposed approach is detected and classified (associated to a priority level) exclusively processing _tweets_.  
The work in [9] proposed a framework that defines priorities as a value within a numeric scale, with 16 different \"relevance levels\". In that work, priorities range from irrelevant (0) to maximum relevance (15). We borrow this principle defining that every sensors \(s\in S\) is associated to a priority level \(s.p\), a positive integer value between \(P_{min}\) (lowest priority) and \(P_{max}\) (highest priority), with \(P_{min}<P_{max}\). When the proposed approach is initiated, all sensors \(s\) have the same priority, \(s.p=P_{min}\), and the priority of each sensor remains the same until it is explicitly changed due to the occurrence of some new detected event or when it is not anymore under the influence of at least one event (the sensor's priority is reseted).
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### PV-Powered Charging Station with V2G Service without Energy Cost Optimization  
The PVCS can operate without energy cost optimization based on the storage priority algorithm shown in Figure 2. In this case, the EVs are charged using PV sources first, followed by the stationary storage system, and finally by the public grid. The surplus PV production is used to charge the stationary storage system. However, if the storage system has reached its capacity or charging power limit and there is still unused excess PV power, the remaining power is injected into the public grid. Participating in a V2G service allows EV users to discharge their EVs for up to 15 min or until the battery is fully discharged during peak periods. Following the V2G operation, the EVs will then be charged to achieve the desired SOC at departure. The charging can be performed using any available power, regardless of the initial charging mode, as long as the charging terminal can support variable power up to 50 kW.
## 2 Materials and Methods
### Assessment of Inhibition of Collagen, Elastase, and Tyrosine Activity  
#### 2.9.1 Determination of Anti-Collagenase and Anti-Elastase Activity  
To assess the anti-ageing effect of the tested extract, the levels of collagenase and elastase were measured in fibroblasts (HDF) exposed to _A. millefolium_ extracts at a concentration of 1.0 and 5.0%. Incubation of cells (at a density of 1 \(\times\) 10\({}^{4}\) cells/well) with individual concentrations of extracts was conducted in 6-well plates for 24 h. In the next step, the cells were washed twice with sterile PSB, and then 150 uL of RIPA lysis buffer were added to each well. The cell lysates obtained in this way were used to determine the levels of collagenase and elastase in cells using commercially available ELISA kits (HumanCOL2\(\alpha\)1 ELISA kit and a Human Ne/ELA2 ELISA kit, Elabscience Biotechnology Inc., Houston, TX, USA). Experiments were conducted according to the manufacturer's instructions. Succinyl-alanyl-alanyl-polyl-valine-chloromethyl ketone (SPCK, Adooq Bioscience, Irvine, CA, USA) was used as a control inhibitor for elastase, and 1,10-phenanthroline (Abcam, Cambridge, UK) was used for collagenase. A microplate reader (FilterMax F5, Thermo Fisher Scientific,Waltham, MA, USA) was used to measure absorbance at 450 nm. Each _A. millefolium_ extract concentration was analysed in triplicate.
## 6 Conclusions  
To improve the energy efficiency of the steering system, an electro-hydraulic flow matching steering system for electric wheel loaders based on closed-circuit pump control is proposed. According to the tilt speed of the electric control handle, the angle of the loader is controlled in a closed-loop based on variable speed control in order to control the speed of the powerful motor and realize the flow matching of the supply and demand of the steering system. Through the simulation experimental study of the proposed steering system and the analysis of energy consumption efficiency, the following results can be concluded:  
1. Through the closed-loop control strategy for the steering angle of the loader, the steering angle can accurately correspond to the angle of the electric control handle to achieve precise steering and improve the controllability of the loader steering system. Additionally, with the electric control handle as the steering lever, the mechanical connection of the traditional steering wheel is eliminated. Compared to the traditional steering wheel steering on the loader, the control range of the electric control handle is small, which greatly reduces the driver's labor intensity. It provides a basis for electronic control development of the subsequent loader steering device.
2. Under various working conditions of in-place steering, the steering efficiency of the proposed closed-circuit pump-controlled electro-hydraulic flow matching steering system is more than 80%, which is higher than that of the loader's traditional full hydraulic steering system. The energy consumption of the proposed system is reduced and the energy utilization of the proposed system is improved. It provides a reference for research into potential energy-saving of loader steering systems.
3. Using the proposed closed-circuit pump-controlled electro-hydraulic flow matching steering system, with increasing steering speed the steering efficiency increases accordingly. When the material is placed in the center of the loader bucket, the steering efficiency is also higher than that of the offset load and zero-load condition.  
Figure 13: Test curves with different steering speeds under normal load conditions.  
**Author Contributions:** Writing and analyzing, T.G. and B.W.; conceptualization and supervision, T.L.; data curation, H.C.; methodology, Q.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (2020 YFB2009900), the Natural Science Foundation of Fujian Province, China (2021J01295), National Natural Science Foundation of China (51875218 & 51905180), the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (GZKF-202026).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Delaying the Occurrence of Bar Buckling in RC Columns Confined with SRG Jacketing
## 1 Introduction
Steel-reinforced grout (SRG) jacketing presents an alternative composite system that combines high-strength steel-reinforced fabric with cementitious grout [14], appropriate for strengthening techniques of RC deficient structures [15; 16; 17; 18]. Previous studies focusing on uniaxial compression of SRG-confined plain and reinforced concrete have provided evidence of the system's effectiveness in enhancing both axial strength and associated deformation capacity [19; 20; 21; 22]. Aiming to enhance the limited data as per this type of strengthening configuration, the effectiveness of SRG jackets of axially compressed, lightly reinforced RC columns (with \(\rm s/D_{b}\) ratios equal to 12.5 and 6.25) was experimentally investigated and compared with well-detailed SRG counterparts (\(\rm s/D_{b}=4.17\)) corresponding to the highest confinement that can be offered by SRG jacketing. The specimens were designed as to be susceptible to rebar buckling failure with the compression reinforcing bars losing their stability upon yielding. Single- or double-layered SRG jacket configurations were applied to 18 square cross section columns, whereas 6 more were used as control specimens. The experimental results show that the SRG jackets increased substantially the axial strength and deformation capacity, thus upgrading the structural element performance to be conformable to the requirements of modern codes for seismic resistance. The analysis of the data through a simple mechanistic model shows that the lateral confining pressure exerted by the jackets provided to the concrete core the required axial strength reserves to be able to undertake the load release by the compressive reinforcement upon attainment of unstable conditions. This strength reserve along with the jacket strain reserve at corners enables the structural component to increase its compressive strain ductility (sustaining of strength with increasing strain), thus delaying bar buckling failure to occur at higher compressive strain level.
## 5 Case Study: Experimental Results
### User Clustering  
As user-topic modeling is based on the assumption that users with similar latent topics are likely to be engaged in the same article, the proposed method can also be used for clustering purposes. Each user can be represented as a vector of its topic distribution, which means we can use any clustering method based on vector spaces. In this subsection, we conduct two experiments using this approach, one of which is for the purpose of visualization of the user interaction network, while the other is for the assessment of its effectiveness.
# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 5 Experiments and Result Analysis
### Comparison of Different Data Augmentation Methods  
We adopted CLAHE, RBC, horizon flip (HFlip), HFlip + CLAHE, and HFlip + RBC for data augmentation and compared their effects on the proposed RCBi-CenterNet model. The results are reported in Figure 7. Observations and findings are listed as follows.  
* All evaluated augmentation methods showed performance improvements to varying degrees, compared to \"None\", meaning that data augmentation serves an effective strategy to increase the dataset diversity and allows a model to learn richer features.
* HFlip showed the best result, with 7.43%, 8.87%, and 8.47% improvement for Top 1, Top 3, and Top 10, respectively, compared to the model without augmentation. HFlip is more effective than CLAHE and RBC, potentially due to the fact that HFlip is the only method that changes both the position and orientation of the vehicle objects by mirroring an image in the horizontal direction, creating more objects with different pose information for the model to learn.
* CLAHE and RBC, although not as good as HFlip, presented marginal performance boosts. Neither CLAHE nor RBC creates new pose information, but they do add new color features, which showed a limited but still positive impact.
* Combining with CLAHE and RBC, the hybrid methods HFlip + CLAHE and HFlip + RBC did not show remarkable improvement. HFlip + CLAHE outperformed CLAHE but was worse than HFlip, and HFlip + RBC under-performed compared to both HFlip and RBC applied individually. This result is somehow counter-intuitive since HFlip and the other two focus on different aspects to augment the image features and should work better than each method applied individually.
## 5 Solution Algorithm
### Valid Inequalities  
The aforementioned equations (i.e., Equations (1)-(21)) are able to be further strengthened by applying the following valid inequalities. Capacity constraint valid inequality:  
\[CE_{i}^{\text{rs}}\geq\sum_{j\in P}X_{ij}^{\text{rs}}\quad\forall i\in P, \forall v\in V,\forall s\in S \tag{26}\]  
Constraint (26) refers to the capacity constraint of empty container repositioning. It defines that the repositioning number of empty containers cannot exceed their storage number at any port. Mutual exclusion valid inequality:  
\[(1-z_{i}^{\text{rs}})\sum_{j\in P}X_{ij}^{\text{ps}}\leq\sum_{j\in P}DE_{ij}^ {\text{rs}}\quad\forall i\in P,\forall v\in V,\forall s\in S \tag{27}\]  
An extra binary variable \(z_{i}^{\text{rs}}\) is introduced here. \(z_{i}^{\text{rs}}\) is equal to 1 if and only if the \(i\)th port on the voyage along the route of a ship is a deficient port. Constraint (27) is known as the logical inequality, which stipulates that empty container repositioning will not be carried out when the port is deficient (that is, the remaining empty containers in the previous stage of the port are not able to satisfy the requirement for containers at this stage.) General valid inequality:  
\[\lfloor\mu_{1}\rfloor\bullet XS_{ij}^{\text{rs}}+\lfloor\mu_{2}\rfloor \bullet XF_{ij}^{\text{rs}}+\lfloor\mu_{3}\rfloor\bullet XE_{ij}^{\text{rs}} \leq\mu_{1}DS_{ij}^{\text{rs}}+\mu_{2}DF_{ij}^{\text{rs}}+\mu_{3}DE_{ij}^{ \text{rs}}\quad\mu\in R^{+} \tag{28}\]  
Constraint (28) is a general valid inequality, which is inspired from the Chvatal-Gomory procedure [60; 61]. (The proof of the validity of cuts is introduced in Appendix A).
## 4 Experiments and Evaluation
### Evaluation
The frame-rate for BAM is: one camera under 540 fps, with 1024 \(\times\) 480 resolution, another under 1080 fps, with 512 \(\times\) 320 resolution. The sampling data calculated by B-spline representation is six times the higher frame-rate, e.g., 6480 FPS, which exceeds the limits of our HFR cameras. The uncertainty data of five fragments scattered in this explosion are shown in Tables 2 and 3. All these supplementary data are calculated by B-spline representation. Based on estimated velocity and acceleration, the momentum and force of each fragment can also be provided. These physical parameters are very important for damage assessment, etc. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Stereo** & **SBA** & **BAM** \\ \hline Duration & 538 ms & 538 ms & 883 ms \\ MSP (mm) & 33.29 & 20.94 & 25.88 \\ MESP (mm) & 31.44 & 12.03 & 12.57 \\ MERE-C1 & 0.040 & 0.00012 & 0.00053 \\ MSERE-C1 & 0.39 & 0.00014 & 0.00072 \\ MERE-C2 & 0.041 & 0.00011 & 0.00079 \\ MSERE-C2 & 0.41 & 0.00014 & 0.0015 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy evaluation of BA. Figure 7: Boxplot of (**a**) position error, (**b**) reprojection error of each camera.
## 3 Materials and Methods
### Reasoning Engine and Rules Based on SWRL
#### 3.3.1 Energy Rules  
An energy rule is used to control the communication frequency according to the energy state of the batteries. When the battery energy is more than 50%, the communication device uses higher frequencies; when it in the range of 10%-50%, the communication device uses lower frequencies; and when the battery energy is lower than 10%, the communication device and sensor enter a dormancy state to avoid energy depletion. Sampling and communicating intervals of 0 represent the dormant state. In order to do this, the rule defines a situation using the stored knowledge (e.g., \"there is a WSN node, this node has a battery, a communication device and some measurement device. The battery has very-low energy, and the communicating frequency or the measurement frequency is not slow\"), and defines an action for it. This action consists in dropping a particular triple (the frequency of the communication device and the measurement device of WSN) and replacing it by a new one.  
An example of self-configuration with energy rules when battery energy is low is presented in Figure 7. From the proposed ontology definitions (see above extracts), some classes (-a-) are extracted for inference. The extracted property (-b-) is their initial relations. From (-1-) to (-7-) is the inference procedure according SWRL rule. First, some individuals are identified to be member of the extracted classes. The node1 individual is identified to be \"WSN Node\" since it is a WSN node. The battery (-2-) is identified to be Battery which is a subclass of \"Device\" class and is a subsystem of \"WSNNode\" class (-3-). Individual \"a\" is identified to be \"Energystate\" which is a state of \"battery\"(-4-, -5-). Second, from these initial facts, executing the rule engine with the SWRL rule base, and then finally enables to infer the communication device and sensor enter a dormancy state (-6-, -7-). This example also show how SWRL rules are mapped to ontology.
## 1 Introduction
Various numbers of sub-populations, various topologies (trigonal pyramid with four sub-populations, cube with eight sub-populations, hyper-cube with 16 sub-populations, and ring with 2, 4, 8, 16 sub-populations), various policies, various interaction models (only using migration, only using abest, and using both migration and abest), and various numbers of individuals have been investigated in simulation results. The following gives a summary of the paper contributions:  
* A proposal of a new evolutionary computation method, namely MP-GMBSO with migration, in order to realize improvement of quality of solution,
* An application of MP-GMBSO with migration-based method to total optimization of energy networks in a smart city,
* Verification of efficacy of the conventional GMBSO based method for total optimization of smart city by comparing with the conventional DEEPSO, BSO, MBSO, and GBSO based methods,
* Verification of efficacy of the proposed MP-GMBSO based method with migration for total optimization of smart city by comparing with the original GMBSO (GMBSO with one population) based method, and the MP-GMBSO based methods with various interaction model (only using migration, only using abest, and using both migration and abest), various policies, various topologies, various numbers of individuals, and various numbers of sub-populations,
* It is verified that quality of solution is the most improved by the proposed MP-GMBSO with migration-based method using the ring topology with 16 sub-populations and 320 individuals, and the \(W\)-\(B\) policy (the worst individual of a sub-population is substituted by the best individual of other sub-populations) among all of multi-population parameters. The rest of the paper is organized as follows. Section 2 explains a concept of the smart city model. Section 3 introduces problem formulation. Section 4 explains the details of the conventional BSO, MBSO, GBSO, and GMBSO. The proposed MP-GMBSO is also explained as well. Section 5 shows the proposed MP-GMBSO based total optimization method for energy networks in a smart city. Section 6 compares the conventional GMBSO based method with the conventional DEEPSO, BSO, MBSO, and GBSO based methods. It also presents a comparison of the proposed MP-GMBSO with migration-based method with the original GMBSO with a single population, MP-GMBSO with only abest model, and MP-GMBSO with both migration and abest model, and investigation of various migration topologies, migration policies, the number of sub-populations, and the number of individuals. Finally, Section 7 concludes the paper.
# Defects in Silicon Carbide as Quantum Qubits: Recent Advances in Defect Engineering
###### Abstract  
This review provides an overview of defects in silicon carbide (SiC) with potential applications as quantum qubits. It begins with a brief introduction to quantum qubits and existing qubit platforms, outlining the essential criteria a defect must meet to function as a viable qubit. The focus then shifts to the most promising defects in SiC, notably the silicon vacancy (V\({}_{\text{Si}}\)) and divacancy (V\({}_{\text{C}}\)-V\({}_{\text{Si}}\)). A key challenge in utilizing these defects for quantum applications is their precise and controllable creation. Various fabrication techniques, including irradiation, ion implantation, femtosecond laser processing, and focused ion beam methods, have been explored to create these defects. Designed as a beginner-friendly resource, this review aims to support early-career experimental researchers entering the field of SiC-related quantum qubits. Providing an introduction to defect-based qubits in SiC offers valuable insights into fabrication strategies, recent progress, and the challenges that lie ahead.  
defects; silicon carbide; quantum qubits +
Footnote †: journal: Applied Sciences