esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology-Based Feature Selection: A Survey
## 3 Feature Selection
### Embedded Methods  
Filter methods are cheap, but selected features do not consider the biases of the classifiers. Wrapper methods select features tailored to a given classifier, but have to run the training phase many times, hence they are very expensive [42; 43]. Embedded methods combine the advantages of both filters and wrappers by integrating feature selection in the training process. For example, pruning in decision trees and rule-based classifiers is a built-in mechanism to select features. In another family of classification methods, the change in the loss function incurred by changes in the selected features, can be either exactly computed or approximated, without the need to retrain the model for each candidate variable. Combined with greedy search strategies, this approach allows for efficient feature selection (e.g., RFE/SVM, Gram-Schmidt/LLS). A third type of embedded methods are regularization methods and apply to classifiers where weight coefficients are assigned to features (e.g., SVM or logistic regression). In this case, the feature selection task is cast as an optimization problem with two components, namely maximization of goodness-of-fit and minimization of the number of variables. The latter condition is achieved by forcing weights to be small or exactly zero. Features with coefficients close to zero are removed. Specifically, the feature weight vector is defined as in [42; 43].  
Many more feature selection algorithms and variations can be found in the literature. Due to its significance in the classification task, feature selection, and feature engineeringin general, is a highly active field of research. For an in-depth presentation, the interested reader is referred to [39; 40; 41]. Comprehensive reviews can be found in [42; 43].
## Materials and Methods
### Data
#### 2.2.4 Atmospheric CO\({}_{2}\) Concentration (XCO\({}_{2}\))  
The atmospheric CO\({}_{2}\) concentration (XCO\({}_{2}\)) data were acquired from the Greenhouse Gases Observing Satellite (GOSAT) in order to evaluate the spatial and temporal behavior of XCO\({}_{2}\) over the 11 years (April 2009 to December 2019) in the BLA. The GOSAT calculates the average XCO\({}_{2}\) in the path of sunlight reflected by the surface [63; 64]. The XCO\({}_{2}\) did not account for all land use and land cover classes, because it has a spatial resolution of 2.5\({}^{\circ}\), or 278 km, at the equator, making it impossible to be detected by most classes [64].
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity
### Track Condition Evaluation and Position Identification Issues  
In the track condition diagrams shown in Figures 4 and 7, the detection location differs depending on the day of measurement. The positions where the acceleration is from 1 to 1.5 [m/s2], which varied depending on the day, are shown in green. Additionally, the histogram in the lower section of the figures confirms the variation in position.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results  
### Analyze the Impact of Changing the Time Window Size  
A respiratory signal of a predetermined length or greater is required to refine the respiratory signal and detect the respiratory rate. Short respiratory signals may not include many respiratory cycles, resulting in poor estimation accuracy, and excessively long respiratory signals may increase the time required for DFT computation to purify the signal and calculate the bpm. Therefore, we conducted an experiment to select the optimal window size considering accuracy and inference speed. The experiment derived the results through 5-fold cross-validation for the training data. Table 3 and Figure 10 show the changes in the accuracy of bpm estimation and processing time according to the changes in the size of the window. As a result of the experiment, the bpm estimation accuracy exhibited the highest performance when the window sizes were 16 and 17 s. Notably, when the window size increased to more than 18 s, the accuracy decreased. This can be advantageous for periodic analysis, including many breathing cycles; however, using a window size larger than a certain level increases the probability of bpm changing or including noise elements, which makes accurately estimating bpm difficult. Therefore, choosing an optimal window size that is neither excessively small nor large is important for ideal respiration estimation. In terms of computation time, when the window size was less than 5 s, the computation time was noticeably short, but the difference in processing speed between time windows of more than 10 s was approximately 0.1 ms, so the increase in window size was not disadvantageous in terms of processing speed. Therefore, in this study, 17 s was used as the optimal window size considering both bpm estimation accuracy and computation time.
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 1 Introduction
Thus, a correspondence model becomes necessary to describe the desired speed with the commands that control the actuators. Some related modeling work can be found in [17], but such modeling demands a lot of bench experiment, primarily because the relationships between the vehicle set speed or desired acceleration, the corresponding pedal depth, and throttle angle are highly nonlinear. Thus, the problems of an MPC controller are calculation time-consuming and modeling difficulty. Other algorithms for autonomous vehicle control include: Ho [18], sliding mode control (SMC) [19], Lyapunov-based control [20], and linear parameter-varying (LPV) control [21]. The preceding challenges show the need for a simple and effective controller that can compensate for all the disturbances without having to rely on an accurate vehicle model, expensive sensors, and time-consuming calibration. In this paper, we pay particular attention to the active disturbance rejection control (ADRC), which emphasizes on using the extended state observer (ESO) to timely estimate the \"total disturbance\" including both internal unmodeled dynamics and external disturbance in the system. ADRC has been widely used in many fields [22; 23; 24], and applying the parameterization method in [25] allows for an easy and intuitive tuning process. Besides, the model information can help obtain more precise ADRC controller parameters associated with system characteristics, and the burden of the ESO can be reduced. Thus, a vehicle longitudinal dynamics model is implemented to construct a feed-forward controller. Notably, this study uses the mean value engine model (MVEM) [26; 27], instead of a map function [16; 17], to model the relationship between engine throttle and engine torque. Finally, we validated the controller a drive cycle under CarSim(r)/ Simulink(r) [28] simulations and road tests. The contributions made in the paper can be summarized as follows. 1. The controller design is based on the concept of active disturbance rejection control (ADRC). 2. A mean value engine model (MVEM) is implemented to replace the map function to obtain more precise ADRC parameter estimation and then reduce the calculation burden of the extended state observer. 3 The simulation validates the adaptiveness of the proposed controller against the well-tuned PID controller and proves the fuel consumption improving potential by limiting the accelerator pedal action. 4 It utilizes the high-definition map (HD-map) to obtain the road slope information in a real autonomous driving longitudinal control system. This rest of this paper is organized as follows. Section 2 describes the longitudinal vehicle dynamics and the internal powertrain model of the vehicle. Section 3 describes the longitudinal controller architecture and the control relationships exploited in this study. Sections 4 and 5 present the results of the CarSim-Simulink simulation followed by an analysis of the results and a discussion of their practical utility. The conclusions of this study are presented in Section 6.
## 4 Discussion
### Effect of Training on Jump Performance  
Both vertical acceleration (jumping power) and horizontal acceleration (sprinting) are important in ball possession, repossession, corner kicks, defense play, and attack on goal [46]. [PERSON] et al. [47] reported that high level competitive players jump higher than lower performance level players. Here we report training effects on vertical and horizontal jump performance. To the authors' knowledge, this study is the first that investigated effects of loaded byometric with short sprints and change-of-direction training on jump performance in young soccer players. Our data are in agreement with several previous investigations which have described improved jump performance following unloaded combined pyometric and short sprint with change-of-direction training [12; 35; 39] and loaded and unloaded combined pyometric and short sprint training [10].  
Recently, [PERSON] et al. [10] studied effects of 10-week loaded and unloaded combined pyometric and short sprint training in junior soccer players (U17 elite males). They observed significant improvement in jump performance (SJ, \(p<0.001\); CMJ, \(p<0.01\); CMJ, \(p<0.01\); and 5 JT test, \(p<0.01\)) after both types of training programs. In addition, [PERSON] et al. [12] studied the effects of a 6-week pyometric training and change-of-direction exercises in prepubertal male soccer players. These researchers noted significant improvement in vertical and horizontal jumps performance (squat jump and long jump, \(p<0.05\)) but no gain in CMJ and 5 JT. Indeed, [PERSON] et al. [36] studied the influence of 8 weeks of combined pyometric and short sprint with change-of-direction training jump performance in young male handball players (U15) and reported improved jump performance (39%, d = 0.26; small; 29%, d = 0.26; small; 26%, d = 0.25; small; and 13%, d = 0.09; small for SJ, CMJ, CMJ, and 5 JT, respectively).  
Mechanisms which underpin the improved jump performance following pyometric training are commonly explained by neural factors such as more synergistic muscle activation and less antagonistic muscle activation (i.e., intermuscular coordination) and greater motor unit recruitment (i.e., intramuscular coordination) [48]. Furthermore, [PERSON] et al. [26] reported that the use of additional load during executed jumps allowed players to apply greater force against the ground in the direction of intended movement (vertical or horizontal axes) on a longer period of time. This mechanical adjustment generates higher impulses (possibly additional overload) during jumps [49], thus producing a greater adaptation of jumping ability in the loaded group [26].
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 5 Conclusions  
This article has reviewed the wire-grid and surface triangulation approaches as two of the most used MoM-based ones in the field of antenna modeling. The review included their history, applications, and limitations. It also provided a brief presentation on the mathematical background for each approach besides two developed working algorithms on their base. In addition, there is a detailed description of two advanced computer codes with accelerated working principle developed on the base of these algorithms. These codes were used to evaluate the efficiency of these approaches on the example of different antenna types including a rectangle spiral, a spiral, a rounded bow-tie planar antennas, a biconical, and horn antennas. The results of these codes were compared with those obtained using other numerical methods and calculated analytically, and even with the measured ones. The code results showed a good agreement with the results obtained analytically and measured experimentally. They also validated their accuracy comparing to other codes with other numerical methods and proved their efficiency regarding to the computational costs consumed and the same accuracy achieved. The comparison proved the promising capabilities of the wire-grid approach since it can give acceptable results with less resources comparing to surface triangulation approach.  
Conceptualization, A.A.H. and S.P.K.; methodology, A.A.H., A.V.D. and S.P.K.; software, A.A.K. and D.V.K.; validation, A.A.H., D.V.K. and A.A.K.; formal analysis, A.A.H. and A.A.I.; investigation, A.A.H., A.A.I. and A.V.D.; resources, S.P.K.; data curation, A.A.H., D.V.K. and A.V.D.; writing--original draft preparation, A.A.H., A.A.K., A.A.I. and A.V.D.; writing--review and editing, A.A.H. and S.P.K.; visualization, A.A.H., D.V.K., D.M.M. and A.A.K.; supervision, S.P.K.; project administration, A.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation project FEWM-2022-0001.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the funding organization policy.  
The authors declare no conflict of interest.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 6 Conclusions  
This paper presents an EV SRM drive and its G2V/V2G/V2V/M2V/V2M operations. For the established SRM drive, the commutation shifting scheme, current, and speed control schemes are all properly designed to yield good dynamic and steady-state driving performance, including reversible and regenerative braking operations. Moreover, thanks to the varied-voltage DC-link arrangement, improved performance over a wide speed range and reduced battery consumed energy are achieved.  
In idle mode, the isolated grid-connected G2V/V2G operations are successfully conducted based on the battery, CLLC resonant converter, and SMR/inverter. In G2V charging mode, the inverter is operated as a three-phase or single-phase SMR to charge the battery from the utility grid, with good PFC characteristics. Reversely, V2G operation can be performed for sending the real and reactive powers to the grid. Good performances have been verified from the measured results.  
In V2V operation, thanks to the equipped EV power control units and proper power conditioning control arrangement, the EV with exhausted battery energy can be charged by the other EV providing auxiliary roadside assistance. Successful operation has been experimentally demonstrated. Moreover, the M2V/V2M operations of the developed EV SRM drive have also been experimentally demonstrated.  
Formal analysis, W.-K.G.; Writing--original draft, W.-K.G.; Writing--review & editing, C.-W.Y.; Supervision, C.-W.Y. and C.-M.L.; Project administration, C.-M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction
### Research Aim  
This research employs an in silico mechanistic platform for modeling molecular pathways to quantify the synergistic effects of multi-combination compounds. Specifically, this research aims to understand the underlying mechanistic explanation of the positive effects of two phytonutrients--the flavonoids apigenin and hesperidin--on joint pain. Researchers have identified the need for computational approaches such as CytoSolve(r) to develop \"cocktails\" of compounds, given the challenges of time and resources needed from conventional in vitro and in vivo methodologies [12]. In this study, we analyze the individual as well as the synergistic combination of these two phytonutrients. Apigenin, commonly found in leafy green plants and herbs such as celery, parsley, herbs, sorghum, and dried oregano, has strong anti-inflammatory and antioxidant properties [13; 14; 15]. Hesperidin naturally occurs in citrus fruits and is also a potent anti-inflammatory and antioxidant agent [16]. The role of apigenin and hesperidin has been extensively studied in various pathologies such as cancer, cardiovascular diseases, renal dysfunction, infections, neurodegeneration, etc., due to their antioxidant and anti-inflammatory properties [13; 14; 15; 16].  
Effects of apigenin and hesperidin on inflammatory joint pathologies such as osteoarthritis are poorly understood. In this study, a computational systems biology framework is used to understand the effect of apigenin and hesperidin on inflammation and the subsequent pain in joints. Such computational--in silico--methodologies facilitate the study of complex systems with multiple biochemical interactions concurrently, which is harder to carry out using more conventional lab-based in vivo/in vitro methods. An in-silico systems biology approach is employed in this study to (1) identify joint pain molecular mechanisms that are potentially targeted by apigenin and hesperidin and to (2) quantitatively predict how apigenin and hesperidin work in synergy to attenuate joint pain.
## 2 Development of the System for Point-Data Pre-Processing
### Module 4--Fuzzy-Based Reduction of Point Data
The input consists of two state variables--\(\mathcal{Q}\) and _MRE_ (Figure 6(b)) whose real values are fuzzified _into fuzzy sets_ with appropriate input spaces, where the fuzzy sets with input values are defined by appropriate membership functions. Triangular membership function was used for all membership functions. Considering the previously mentioned character of \(\Phi\) and \(\Psi\), as well as the practical experience with their values, for the input variable \(\mathcal{Q}\) an input space of 0\({}^{\circ}\) to 2\(\cdot\)90\({}^{\circ}\) was defined, that is, from 0\({}^{\circ}\) to 180\({}^{\circ}\). This space was segmented _into three fuzzy sets_ using linguistic terms to describe angle characters: _sharp_, _right_ and obtuse(Figure 8(a)). Input space for the state variable _MRE_ is defined on the basis of real-application experience with _MARE_ = 0.05 mm, as the pivotal parameter and was segmented into three fuzzy sets--_slight, moderate and significant_ (Figure 8(b)). The output from this fuzzy system is variable _RC_ (a non-dimensional value) which, for simplicity sake, has been allotted an output space from 0 to 100. To allow finer control of _RC_ parameter, the input space was segmented with finer resolution, resulting in a total of nine fuzzy sets denoted with linguistic qualifiers--_minor, very low, low, medium-low, medium, medium-high, high, very high_, and _huge_ (Figure 8(c)). The knowledge base comprises a total of nine control fuzzy rules:  
1. If (\(\Omega\) is _Acute_) and (MRE is _Slight_) then (RC is _Huge_)
2. If (\(\Omega\) is _Right_) and (MRE is _Slight_) then (RC is _Very-high_)
3. If (\(\Omega\) is _Obtuse_) and (MRE is _Slight_) then (RC is _High_)
4. If (\(\Omega\) is _Acute_) and (MRE is _Moderate_) then (RC is _Mid-high_)
5. If (\(\Omega\) is _Right_) and (MRE is _Moderate_) then (RC is _Mid_)
6. If (\(\Omega\) is _Obtuse_) and (MRE is _Moderate_) then (RC is _Mid-low_)
7. If (\(\Omega\) is _Acute_) and (MRE is _Significant_) then (RC is _Low_)
8. If (\(\Omega\) is _Right_) and (MRE is _Significant_) then (RC is _Very-low_)
9. If (\(\Omega\) is _Obtuse_) and (MRE is _Significant_) then (RC is _Minor_)
## 3 Data and Methods  
### Gravity Surveying and Data Adjustment  
The TGR and the reservoir-induced earthquake monitoring system is shown in Figures 1 and 2. There are two LCR gravimeters onsite for mobile gravity observations. The accuracy of the LCR gravimeters is greater than \(10\times 10^{-8}\) ms\({}^{-2}\), and the drift is less than \(\pm 5\times 10^{-8}\) ms\({}^{-2}\) per hour. Furthermore, once every three years, the LCR gravimeter is attuned with China's gravity calibration baseline. The assessment schedule and paths are arranged with two assessment rounds annually. The first starts from March to April and the second from October to November. The annual observation data from October to November were selected to avoid the influence of rainfall and other seasonal effects.  
The joint assessment is a round-trip starting in Wuhan with onward and backward field assessment paths. It was observed that the gravity value differences of the starting and ending stations, for both the onward and backward assessments on the same path, kept lower than \(30\times 10^{-8}\) ms\({}^{-2}\) for the two gravimeters and \(25\times 10^{-8}\) ms\({}^{-2}\) for the same gravimeter; if not, the assessments were restarted. After adjustment, the precision of the mobile gravity surveillances was from \(9\times 10^{-8}\) ms\({}^{-2}\)\(\sim 11\times 10^{-8}\) ms\({}^{-2}\) from 2002 to 2009, as shown in Figure 3a.  
Figure 2: Codes of the gravity station and area selection for the interpolation algorithms: A represents riverside and B hills.
## 4 Influence of Vehicle-Bridge Coupling Vibration on Anti-Disturbance Performance of Concrete
### Influence on Concrete Compactness  
Ultrasonic test was conducted on each group of NC specimens according to Chinese standard T/CECS 02 [26], and the test results are shown in Figure 10.  
It can be learnt from Figure 10a that the sound velocity of concrete specimen can be improved with the coupling vibration effect of vehicles and bridge. In addition, it can  
Figure 10: Ultrasonic test results. (**a**) NC specimens; (**b**) UHPC specimens.  
Figure 9: Comparison of shrinkage test result. (**a**) NC specimens; (**b**) UHPC specimens.  
be considered that vibration can improve the density of concrete. However, when the vibration amplitude increases to 4 mm or the frequency increases to 9 Hz, the ultrasonic velocity test result of NC specimens decreased. It indicated that excessive amplitude or frequency is unfavourable to the internal structure of concrete.  
While for the ultrasonic test results of NC-2 specimens shown in Figure 10a, it can be seen that the ultrasonic velocity of the upper part and the lower part of the concrete sample under either static state or vibration disturbance conditions (3 mm and 6 Hz) are roughly the same. It can be known that the density of NC material can be increased after optimizing the amount of coarse aggregate. In addition, good uniformity of NC-2 material can be presented, and the anti-disturbance performance of NC-2 material can be better as well.  
It can be seen from Figure 10b that the vehicle-bridge coupling vibration has little effect on the ultrasonic sound velocity of UHPC under different vibration frequency and amplitude. It is mainly due to that the two kinds of UHPC adopted in this test are Materials with low porosity and high density. Vibration disturbance within the range of vibration parameters adopted in this test does not significantly change the compactness of UHPC specimen. While for the lower part of the specimen UHPC2-3-6, the slight decrease in ultrasonic sound velocity is mainly cause by the uneven distribution of steel fiber.
## 5 Experimental Section
### Implementation Details
#### 5.2.2 LP Recognition  
The LPCR modules were trained by using the LP images generated through the proposed LP-GAN generators. Subsequently, the results of the experiments on recognizing the characters of the real LP images show that the LP images generated by the LP-GAN generators were similar to the real LP images and could be used as training data for the LPCR module in the ALPR system. The LPCR modules based on the modified YOLOv2 were trained with an input image size of 416 \(\times\) 416 pixels, a starting learning rate of 0.00025, a batch size of 64, weight decay of 0.0005, and momentum of 0.9. The training was conducted using four NVIDIA Titan Xp GPUs.