esa-sceva/satcom-chunk-collection huggingface dataset:
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## 2 Description of the Model  
In this work, the 2D version of the Hybrid Optimization Software Suite (HOSS) [24; 25; 26; 27], which is an in-house implementation of the Combined Finite-Discrete Element method (FDEM) [28; 29], is used. For an in-depth description of FDEM, the following references are recommended: [2; 3; 5; 17; 30]. In a previous work [17], the authors performed a detailed sensitivity analysis using the Fourier Amplitude Sensitivity Test (FAST [31; 32]), where seven HOSS parameters were explored. These parameters include tensile and shear strengths, \(\sigma_{n}^{max}\) and \(\sigma_{t}^{max}\), the maximum relative displacements in the normal and tangential directions, \(\delta_{n}^{max}\) and \(\delta_{t}^{max}\), and the shape of the displacement-softening curve (see [17]). The sensitivity analysis study showed that only two parameters had an impact on the stress versus time curve (for a point located at the center of the sample) obtained from HOSS for this type of problem. These parameters are the shear strength \(\sigma_{t}^{max}\) and the maximum relative displacements in the tangential direction \(\delta_{t}^{max}\). The sensitivity results are consistent with a previous work performed on the HOSS model by [PERSON] et al. [18], where only the \(\sigma_{t}^{max}\) is found to be of significant influence on the model. A combination of these parameters defines the specific fracture energy dissipated in mode II, \(E_{t}\), which is given by  
\[E_{t}=\sigma_{t}^{\max}\sigma_{t}^{\max}A_{0}, \tag{1}\]  
where \(A_{0}\) is given by \(A_{0}=\int_{0}^{1}z(x)dx\) (see [17]). The question remains: How do we select all these coefficients or material parameters, when there is not enough experimentally-based information, in a way that the model response is both physically sound and in agreement with available experimental data? As a way of circumventing the information limitation mentioned previously, in this work, an assimilation estimation of these parameters is proposed. In order to perform a comprehensive assimilation estimation of the HOSS model to the input parameters, both the shear strength \(\sigma_{t}^{max}\) and the maximum relative displacement in tangential direction \(\delta_{t}^{max}\) are estimated through data assimilation. It is worth noticing that both parameters \(\sigma_{t}^{max}\) and \(\delta_{t}^{max}\) are independent from each other. The way in which the \(\delta_{t}^{max}\) is perturbed in this work is by changing the energy in tangential direction and therefore obtaining the maximum displacement using Equation (1). A more detailed description of the HOSS model and its parameters is presented in Appendix A and in [17].
## 3 Numerical Experiments and Results  
### Experimental Design  
In this section, we report numerical experiments to verify the effectiveness of the proposed algorithm. The experiment method is as follows: Robust solution (RS) and nominal solution (NS) are determined, and uncertain threat and threat time parameters of the enemy are created randomly to compare the performance of the two solutions. The Monte Carlo simulation method was used to compare the two solutions, and the objective function values of the two solutions are compared by sampling uncertain data in the predefined section [28]. The experiment employed IBM ILOG CPLEX Optimization software package and C#, and the computer used in the experiment was equipped with an Intel(R) Core i5-6600 processor and 3.3 GHz CPU with 8 GB of RAM.  
The parameters in the experiment were generated randomly, as was done in References [9; 11]. Target's threat level, \(f_{j}\), and time to destroy a target, \(p_{j}\), are generated randomly in sections (3,10) and (1,5), respectively. Target's threat time was generated randomly in section (0, \(\sum_{j=1}^{n}p_{j}\)) when considering real wars. Experiments were conducted with four cases with different number of targets: n = 5, 10, 15, and 20. Uncertainty level \(\theta\) was selected from set {0.2, 0.4, 0.6, 0.8}. Each experiment had 15 cases with different random seeds. The numerical experiments were conducted with 240 (\(4\times 4\times 15\)) examples.
# Augmented Reality and Virtual Reality in Exergaming
## 4 Discussion
### Topic Analysis-Keyword Analysis
* **Green cluster**: \"dementia\", \"design\", \"exergames\", \"exertion\", \"head-mounted display\", \"human-computer interaction\", \"immersion\", \"immersive\", \"immersive virtual reality\", \"interactive computer graphics\", \"patient rehabilitation\", \"serious games\", \"user experience\", \"user interfaces\", and \"virtual reality\". * **Blue cluster**: \"adults\", \"behavior\", \"benefits\", \"children\", \"enjoyment\", \"exercise\", \"fitness\", \"health\", \"heart rate\", \"motivation\", \"obesity\", \"physical activity\", \"sports\", and \"video games\". * **Yellow cluster**: \"active video games\", \"engagement\", \"exergaming\", \"gamification\", \"intensity\", \"interventions\", \"intrinsic motivation\", \"multiple sclerosis\", \"scale\", \"stroke\", \"system\", \"validity\", and \"walking\". * **Purple cluster**: \"augmented reality\", \"game design\", \"games\", \"kinect\", \"mixed reality\", \"physotherapy\", \"rehabilitation\", \"stroke rehabilitation\", \"technology\", \"training\", and \"virtual environments\". LDA was also used to identify relevant topics based on the study titles and abstracts. The results of the LDA were cross-checked, compared with, and examined along with those of the keyword analysis. Hence, by combining the outcomes of both LDA and the keyword analysis, nine broad topics were defined. These topics are presented in alphabetical order below: \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Keyword** & **Occurrences** & **Total Link Strength** & **Keyword** & **Occurrences** & **Total Link Strength** \\ \hline virtual reality & 1322 & 1322 & immersive virtual reality & 39 & 177 \\ exergames & 990 & 990 & games & 37 & 169 \\ exercise & 668 & 668 & interventions & 26 & 160 \\ physical activity & 561 & 561 & gait & 26 & 159 \\ rehabilitation & 550 & 550 & stroke & 26 & 143 \\ balance & 424 & 424 & serious games & 28 & 139 \\ exergaming & 395 & 395 & human-computer interaction & 31 & 126 \\ older adults & 385 & 385 & quality of life & 25 & 119 \\ video games & 258 & 258 & heart rate & 27 & 117 \\ augmented reality & 241 & 241 & sports & 23 & 114 \\ performance & 211 & 211 & training & 19 & 112 \\ motivation & 184 & 184 & therapy & 17 & 107 \\ health & 183 & 183 & adults & 21 & 103 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: VOViewer keywords’ total link strength. Figure 10: Keyword co-occurrence network VOViewer—All keywords. * Biofeedback, sensor, and monitoring systems;
* Children and youth populations;
* Cognitive and mental health support;
* Educational, training, and skill-based exergaming;
* Fitness, performance enhancement, and physical activity promotion;
* Motivation and engagement;
* Older adults and neurological populations;
* Rehabilitation, physical therapy, and clinical;
* Well-being and healthy habits.
## 2 Materials and Methods  
### Object of Investigation  
The object of investigation was the first-generation foil bearing, whose structure is shown in Figure 2. The foil assembly consisted of one top foil and one bump foil. One end of both foils (0.1 mm thick) was fixed in a narrow groove (0.3 mm wide), which can be seen at the top of the hole in the bearing sleeve. The nominal diameter of the bearing, which was equal to the diameter of the journal, was 25 mm, and the length of the bearing was 40 mm. Tests were performed on two geometric variants of the bearing where different assembly preloads were achieved. Various materials were also considered during the study. The top foil was made of brass (2.0321) or a copper alloy (CuAl7). The bump foil was made of the same copper alloy (CuAl7) or an Inconel alloy. Two material variants were thus created for the bearings: a brass top foil in combination with a bump foil made of a copper alloy, or a top foil made of a copper alloy in combination with an Inconel foil. Four bearing variants (two geometric variants and two material variants) were subjected to tests. The basic technical parameters of the tested bearings are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Journal diameter & 25 mm \\ Sleeve inner diameter & 25.9 or 26 mm \\ Bearing length & 40 mm \\ Number of top foils & 1 \\ Number of bump foils & 1 \\ Top foil thickness & 0.1 mm \\ Bump foil thickness & 0.1 mm \\ Number of bumps of a bump foil & 29 \\ Bump radius (inner) & 2 mm \\ Bump height & 0.3 mm \\ Angle between bumps & 10\({}^{\circ}\) \\ Journal material & steel (1.0562) \\ Top/Bump foil material & 2.0321/CuAl7 or CuAl7/Inconel \\ Sleeve material & steel (1.0562) \\ Sleeve weight & 96 g \\ \hline \end{tabular}
\end{table}
Table 1: Main technical data of the tested foil bearing.  
Figure 2: Geometry and basic dimensions of the tested foil bearing.  
The tested bearings were developed for use in vapour microturbines whose power ranges from 1 to 30 kW. Such microturbines typically use a low-boiling working fluid whose boiling point is lower than water's. This allows the thermodynamic cycle to operate efficiently at much lower temperatures and use low- and medium-temperature waste heat sources. Therefore, a low-boiling liquid with a trade name of HFE-7100 was used as a bearing lubricant. It is a colourless, non-flammable, dielectric fluid with low global warming potential (GWP) and zero ozone depletion potential (ODP). At a temperature of 25 \({}^{\circ}\)C, the HFE-7100 fluid exhibits a density of 1.5 g/cm\({}^{3}\) and a dynamic viscosity of 0.57 mPa\(\cdot\)s. At atmospheric pressure, it has a boiling point of 61 \({}^{\circ}\)C. During the tests, the foil bearing was lubricated with this low-boiling medium in liquid form. Therefore, these were not typical operating conditions for foil bearings.
# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 4 Results
### ACO-BP-Based Driving Style Model Identification and Result Analysis
It further proves the recognition accuracy and recognition value reliability of the ACO-BP model, and the recognition effect has obvious advantages for such an accident-prone driving population as the aggressive type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _MAE_ & _MSE_ & _RMSE_ & **Recognition Accuracy** \\ \hline BP & 0.1222 & 0.1444 & 0.38006 & 90\% \\ ACO-BP & 0.0333 & 0.0333 & 0.1937 & 96.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation results of the driving style model. Figure 8: Simulation results of the model error comparison before and after ACO optimization. The driving style recognition speeds of the BP driving style recognition model and the ACO-BP driving style recognition model are shown in Figures 11 and 12, respectively. The slowest recognition speed for the BP driving style recognition model is 8 ms, and the fastest recognition speed is 5 ms. For the ACO-BP driving style recognition model, the slowest recognition speed is 7 ms, and the fastest recognition speed is 5 ms, with a relatively uniform distribution within the range. The results indicate that, although ACO-BP is a hybrid model, it has not been significantly affected, as the recognition speed remains fast. Com  
Figure 10: Effect of different driving styles recognized by the ACO-BP model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Aggressive** & **Ordinary** & **Conservative** \\ \hline BP & 81.8\% & 95.6\% & 87\% \\ \hline ACO-BP & 95.5 & 100\% & 91.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model recognition accuracy of different driving styles. Figure 9: Effect of different driving styles recognized by the BP model. pared to the BP model, the recognition speed of the ACO-BP model is more even, and the speed range is more compact. Therefore, the ACO-BP driving style recognition model demonstrates better real-time performance compared to the BP driving style recognition model, enabling better adaptation to the actual road conditions of autonomous vehicles. It allows for timely recognition of driving styles, the avoidance of hazardous driving behaviors, and the improvement of driving safety.
## 4 Experimental Results
### Accuracy of Re-Identification of Non-Compliant Groups  
We designed multiple scenarios in order to test the accuracy of the proposed system in the re-identification of non-compliant groups and the enforcement of physical distancing constraints. As shown in Table 4, eight experiments were based on group re-identification. Groups of participants were asked to violate physical distancing constraints within the FoV of one of the three mounted CCTV cameras and then walk to the other side and perform the same action within the FoV of another CCTV camera. Here, the purpose was to re-identify the non-compliant groups while repeating the violation of physical distancing constraints. We stored the video streams captured by all the mounted CCTV cameras while conducting the experiments for creating our own dataset and testing the overall accuracy of the re-identification deep learning model with respect to the environmental conditions in our designed test scenarios. We used YOLOv4 [49] to crop the images of the persons from all video frames and then annotated each person with a personal as well as camera identity. In this way, we created our own dataset, which consisted of 7687 images of 15 different persons captured through three different CCTV cameras. After annotation of the dataset, we divided it into two categories: Gallery and Query images. Ten percent of the total images were categorized as Query images and rest were used as Gallery images. The results of the re-identification module based on the deep learning model on our created dataset are shown in Table 5  
Figure 9 shows the enforcement of physical distancing constraints based on the re-identification of a non-compliant group, and Figure 10 presents some of the results that were generated while testing group re-identification.  
Figure 10: Images of persons re-identified by re-identification module based on images stored in database (Gallery) and given query images.  
Figure 9: Re-identification of non-compliant group and enforcement of physical distancing constraints through robot. (**a**) A non-compliant group consisted of four persons detected by Camera I. (**b**) Same group re-identified by Camera II with re-identification score, while again violating the physical distancing constraints.
## 7 Emerging Research and Innovations
### Integration with Other Agri-Food Waste Streams  
The agri-food sector generates significant waste due to inefficiencies in the supply chain, contributing to environmental degradation and greenhouse gas emissions, which account for 19-29% of total emissions. About 25% of resources are used to produce food that is lost or wasted. The improper disposal of organic waste, particularly from processed fruits and vegetables, poses substantial economic and environmental challenges [82].  
According to the same author [82], utilizing food waste through a biorefinery approach can help achieve sustainable development. This concept integrates biomass conversion processes to produce fuels, power, and other valuable chemicals. By refining agro-industrial by-products, essential nutrients can be extracted, enabling market flexibility. By-products from the processing of tomatoes, olives, grapes, and apples are rich in health-promoting bioactives. For example, tomato waste, which accounts for 5 to 19% of the processed mass, contains carotenoids, polyunsaturated fatty acids, and pectin. Olive oil production can result in 30% waste, with olive pomace still containing valuable nutrients. Grape pomace also provide antioxidant-rich residues.  
An Integrated Biorefinery Model could merge olive and grape pomace, utilizing supercritical CO\({}_{2}\) extraction to obtain oils, which can be used in nutraceuticalals and biodiesel production. Subsequently, the solid residue can then be utilized for biogas production through anaerobic digestion. Overall, integrated biorefineries can mitigate the environmental impact of food waste while generating high-value products and energy, promoting more efficient use of diverse agri-food processing wastes [82].
## 3 Research Design  
### Proposed Algorithm  
#### 3.1.1 Fixation Point Coordinate Transformation Method  
This study proposes a fixation point coordinate transformation method based on geometry, which relies on calculating the shortest distance between a point and a line segment while incorporating the precise derivation of vertical projection point positions. This method can handle various image transformation scenarios, including rotation, scaling, and perspective distortion, as illustrated in the coordinate transformation process shown in Figure 1b, while ensuring computational accuracy and flexibility. The following section provides a detailed description of the design and computational process of this method.
## 3 LCI System Design Framework  
The prospect of designing an LCI system from first principles can be somewhat daunting. For practical implementation, the system will be built with a design specification of requirements, thus a robust methodology and knowledge of the various parameters which affect the outcome of critical operating qualifiers such as system resolution, operating range and measurement speed is required. Furthermore, many parameters are inter-linked making the design process somewhat iterative and, in many cases, trade-offs or compromises will need to be made to achieve a system  
Figure 3: Depth-dependent loss in signal sensitivity from sample reflector. (**a**) overlaid signals for sample measurements up to 2.17 mm, (**b**) an example signal at a depth of 2 mm, (**c**) dataset (**a**) after normalisation, (**d**) the same signal in (**b**) after the normalisation relationship.  
that can perform in the desired manner under the required circumstances of operation. Figure 4 shows a system design framework based around metrology design operating criteria depicted by the blue highlighted elements with the offshoots in grey representing parameters impacting them. Some of the parameters are broken down into smaller elements with the positive and negative symbols representing if the parameter should be increased or decreased to maximise each design criteria based on our exemplar; other designs may vary based on prioritisation of certain requirements.  
The operating domain should also be taken into account during the design phase, such as operating medium, environmental conditions and sample surface characteristics and their impact on the mentioned design parameters outlined in Figure 4. The following sections consider each step of the flowchart in Figure 4 with more in depth discussion on the various parameters and the cross-linking that occurs.
## 5 Conclusions  
In this paper, a novel control method for transient stability enhancement using the active and reactive power control of a VSWG is presented. In addition, the effect of the coordinated reactive power control between the PV system and the VSWG on the stability enhancement is demonstrated. The effects of the control methods were evaluated by transient simulations performed using a modified IEEE nine-bus power system network. The conclusions obtained through the case studies can be summarized as follows:  
1. The transient stability can be enhanced by releasing the kinetic energy accumulated in the rotor of the VSWG. In this work, the kinetic energy injected to the grid is controlled according to the frequency measured in the local area.
2. The transient stability can be further enhanced by combining the reactive power control of the VSWG. In this work, the reactive power injected to the grid was controlled according to the grid voltage. As the frequency fluctuations are restrained, the kinetic energy released to the grid can be reduced, and hence the deceleration of the rotor of the VSWG can be suppressed.
3. The transient stability can be further enhanced by combining the reactive power control of the PV system with the VSWG control. In this work, the reactive power output of the PV system was also controlled based on the same control scheme as that of the VSWG.  
As the proposed method is based on the local control strategy, it is not optimized as a closed-loop system. As the proposed method does not require wide-area measurement technologies such as a phasor measurement unit (PMU), however, power producers can easily use it as an ancillary service. As the system frequency is used as the control input, the effect of the control method on the transient stability enhancement may, in principle, be significant as use of VSWG systems implemented with control units increases. Further analysis of the proposed method considering the capacity and the location of the VSWG and PV system in a more realistic grid model (IEEJ standard model possessing the distinctive characteristics of the Japanese power system [21]) will be conducted in future work.  
Conceptualization, M.Y. and J.T.; methodology, M.Y. and J.T.; software, M.Y.; validation, M.Y.; formal analysis, M.Y. and J.T.; investigation, M.Y., M.I. and J.T.; resources, M.Y.; data curation, M.Y.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y., M.I. and J.T.; visualization, M.Y.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Number JP20K04448.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Evaluation of Seasonal Precipitation  
The climate system in Malaysia is largely affected by the southwest (SWM) and northeast (NEM) monsoons, with the latter bringing the heaviest rain, so separate evaluation of the two monsoons is important in order to explore the mechanism of precipitation products. For the NEM, the best performances were found by using 3B42 RT, 3B42V7, and APHRODITE, with R\({}^{2}\) values greater than 0.5. The 3B42 RT and 3B42V7 slightly overestimated precipitation, while the remaining products underestimated it by 4.6%-24.5% (Table 4). For the SWM, the 3B42V7 showed the best performance with R\({}^{2}\) and RMSE of 0.37 and 84.47 mm/month, respectively. By contrast, APHRODITE underestimated the precipitations by an average of 16%.  
Overall, the SPPs showed better estimations for the NEM compared to the SWM. These results differ from the findings of [PERSON] and [PERSON] [48] in the Philippines, who found that SPPs (in their case CMORPH and TRMM) performed better during the SWM. By contrast, [PERSON] _et al._[49], in India, showed that TRMM 3B42V6 had a lower RMSE, bias, and FAR for NEM compared to SWM. These differences may be explained by the origin of precipitation with convective weather systems (which dominate during the NEM) being more accurately detected by satellite sensors [50].