esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Neither of the limitations restrict the service, as most of the roles only have read access; the separate buckets for the sources and ontologies enable the management of the roles also on the bucket level, and the available file system operations enable the maintainer to move, obtain, put, or delete content, which is sufficient for our usage. On the terminology end, our choice of providing terminologies in an RDF graph-format enables the full exploration of complex ontologies, for instance, SNOMED CT, which has poly-hierarchical and attribute relations. Furthermore, wherever possible, we use the original identifiers and only create our own URIs (e.g., for CHOP) where no URIs are provided by the SDO. This approach benefits from having resolvable (deeferenceable) URIs (e.g., for SNOMED CT) where a user can enter the URI in a browser and be redirected to the concept on the SDO homepage. However, we rely on the proper identifier management of the SDO, including backward compatibilities. Finally, it is the responsibility of the institution to comply with the licensing terms and requirements for each terminology of the SDO. The SPHN DCC Terminology Service, in its current version, does not support mappings between terminologies, i.e., the process does not currently generate interlinks between the individual codes. For example, relations between ICD-10-GM diagnosis codes and SNOMED CT codes from the hierarchy of disorders are not considered. All the terminologies are handled individually. Mappings exist between the terminologies provided by several SDOs, but these are under separate licenses, which creates an additional administrative burden that hinders the process of making them available through a terminology service for a distributed environment. Nevertheless, since maps are of high value in the SPHN environment, due to the use of terminologies with partly overlapping content, we plan to host mappings and relations between semantic standards on the SPHN DCC Terminology Service in the future. In addition, depending on the community's needs, further terminologies will be included in this pipeline and made available in the future. For example, the Swiss list of pharmaceutical specialities (Spezialitatenliste) [26], which is a list of all the medication approved and reimbursed in Switzerland, is likely to be included. The list provides the Global Trade Item Number (GTIN) code of medicinal products, to gether with the ATC code(s) of their active ingredient(s). Therefore, a researcher can easily derive the active ingredients of a medicinal product, which is normally of interest, from its product code.
# Multiscale Analysis for Improving Texture Classification
## 1 Introduction
More details about the application of multiscale onto those fields can be found in [17; 18; 19]. The multiscale analysis is a technique in pattern recognition and image processing that analyzes an image or pattern at various scales or levels of detail. This benefits multiple applications, such as object identification, image categorization, and feature extraction. In texture analysis, for instance, multiscale approaches can extract characteristics insensitive to changes in scale. This can be useful for tasks such as material classification, where the observed texture of the material may vary depending on the scale. Thus, we state that an approach that computes features firmly based on information theory, second-order statistics, biodiversity, and taxonomic indices plays a fundamental role in defining an image texture's global and local behaviors. Furthermore, such statistics should bring forth the all-inclusive behavior of a texture at the multiscale level. Moreover, they should take advantage of invariance in rotation, permutation, and scale, as well as having a significant effect in texture characterization, which is valid not only for images solely composed of textures but also for images that contain additional structures in addition to textures. The contribution of this paper is threefold: (i) the combination of BiT descriptors, information theory, Haralick, and GLCM descriptors for texture characterization; (ii) a better discriminating ability while using color and gray-scale features on different categories of images; (iii) a method for texture classification that represents the state of the art on challenging datasets. This paper is organized into five sections. First, the multiresolution concepts used in this work are presented in Section 2. Section 3 presents the proposed approach. The experimental design, the datasets used, our results, and related discussion are presented in Section 4. The last section presents the conclusion and future work prospects.
## 5 Conclusions  
The purpose of the study involved investigating the effects of core diameter, conical angle, and bone densities (osteoporosis and normal bone) on the pullout force and bone damage as a variable of screw design.  
1. When the density doubled, the yield compressive stress increased 3-4 times due to the pore size and cell wall thickness and distribution. Thus, there were differences in the pullout stiffness and strain energy.
2. In the pullout curve, the linear elastic section was related to the stiffness, and the de-bond section was related to the yield strength of the material. When the bone density increased, the stiffness increased and was affected by conical angle of the pedicle screw. When the conical angle increased, pullout strain energy increased due to increases in the contact area between the screw and artificial bone.
3. Irrespective of the density, the length of the upper damaged region and damage ratio increased when the conical angle increased in the CDI and CDII groups. In the CDIII group of grade 10, the length of the upper damaged region and damage ratio were similar irrespective of the conical angle. In grade 20, the lower damaged region was smaller than grade 10, and thread parts in the upper and lower parts were almost eliminated.
4. The pedicle screw was pulled out preferentially from the lower part of the screw, and the part of the artificial bone that was deformed and densified contacted the screw. From the friction bond state caused by the contact of the bone and screw, the damaged part and screw were simultaneously removed by continued stress.
5. Analysis of each variable in the 3D response surface plot indicated that the contribution of the core diameter to the pullout force decreased when the density of the artificial bone increased. Conversely, the contribution of the conical angle increased, and each variable exhibited a complex correlation with each other. Additionally, the sensitivity was also consistent with results such as the analysis of damaged thread height, width, and 3D surface plot.  
The experiments and analyses enabled the deriving of the optimal pedicle screw design by analyzing the effect of core diameter and conical angle on the pullout force, bone damage, and fracture behavior and its correlation.  
Conceptualization, E.S.L. and C.-S.L.; methodology, E.S.L., T.S.G. and C.-S.L.; validation, E.S.L. and C.-S.L.; formal analysis, E.S.L.; investigation, E.S.L.; resources, J.-Y.H., Y.-J.K.; data curation, E.S.L. and Y.H.K.; writing--original draft preparation, E.S.L.; writing--review and editing, E.S.L. and C.-S.L.; visualization, E.S.L. and S.-E.L.; supervision, E.S.L. and C.-S.L.; project administration, E.S.L. and C.-S.L.  
This paper was supported by the R&BD program for regional outstanding researchers, startups and innovative firms.  
The authors declare no conflict of interest. The funders had no role in study design, data collection and analyses, writing of the manuscript, or in the decision to publish the results.
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 4 Proposed TLFC-RD Method
### Two-Level Fusion of Classifiers for Road Detection
#### 4.5.2 Lstm  
LSTM [25] includes two activation functions and three gates, which are used to extract the feature maps from the feature vector \((FV)\). The gates included in the LSTM are forgetting gates, input gates and output gates. Next, the long-term memory is included to create the black box of input and output. This leads to improve the training process of LSTM, therefore it helps to utilize the full historical sequence information. The result of the input gate \((i_{t})\), cell output \((hl_{t})\) and output gate \((o_{t})\) for LSTM are expressed in Equations (16)-(18): \[i_{t}=\sigma(WM_{i}\cdot[hl_{t-1},\ FV]+\sigma v_{i})\] (16) \[hl_{t}=o_{t}\odot\tanh(c_{t})\] (17) \[o_{t}=\sigma(WM_{o}\cdot[hl_{t-1},\ FV]+\sigma v_{o})\] (18)  
where the sigmoid activation function is represented as \(\sigma\), the weight matrix for the input and output gate is \(WM_{i}\) and \(WM_{o}\), the offset vectors/bias for the input and output gate are \(ov_{i}\) and \(ov_{o}\), memory is denoted as \(c_{t}\), and element-wise multiplication is denoted using \(\bigcirc\). The output from the cell is used as a feature map for road detection in CAVs.
## 3 Experiment Results
### Bistatic Results  
Figure 10 illustrates the bistatic 3D RCS patterns for both the chessboard metasurface and a PEC surface of the same size at frequencies of 8, 10, and 12 GHz, with incident waves striking the surface perpendicularly (normal incidence). At these frequencies, the PEC surface reflects nearly all incident waves back in the direction of incidence. In contrast, the proposed metasurface reflects the waves predominantly into four distinct directions, forming four lobes, none of which align with the normal direction, This phenomenon can be explained by the generalized Snell's law [11]: the metasurface generates a phase gradient  
Figure 8: Simulated and measured monostatic RCS.  
Figure 9: Simulated monostatic results in oblique incident.  
Table 2 shows a comparison of the performance metrics of the proposed metasurface against those of other published works. This comparison highlights that the proposed metasurface not only exhibits superior performance in the 10 dB RCS reduction band but also demonstrates outstanding performance in the oblique incident angle.  
Figure 12: Simulated 3D RCS patterns of metasurface and PEC at 8.4, 12.1, and 14.6 GHz with incident angle of 20 degrees. (**a–c**) Chessboard metasurface. (**d–f**) PEC surface.  
Figure 13: Simulated 3D RCS patterns of metasurface and PEC at 8.4, 12.1, and 14.6 GHz with incident angle of 30 degrees. (**a–c**) Chessboard metasurface. (**d–f**) PEC surface.
## 2 Theoretical Calculation of Additional Internal Forces in Composite Foundation Piles and Soil
### Assumptions  
Due to the complexity of the combined load-bearing capacity of pile bodies and soil in the composite foundations, the following assumptions were made to simplify the calculation process:  
1. The pile bodies and soil were considered ideal linear-elastic materials and followed the Mohr-Coulomb yield criterion;
2. The soil layers were assumed to be uniformly distributed and isotropic, and the effects of soil voids and underground seepage were neglected;
3. The stress-strain behavior of the soil varied within the elastic-plastic range, while the pile bodies and cushion layers deformed elastically.
###### Abstract
One of the effective methods is the use of tuned mass damper (TMD), which was first proposed in 1909 [26]. Many research studies have been carried out to improve the efficiency of TMD systems for various dynamic applications. TMDs were first proposed for the mitigation of wind-induced vibrations. Later, TMDs were also implemented for reducing the effects of seismic excitations [27; 28]. Studies in the past have illustrated shortcomings associated with the use of TMDs for the mitigation of seismic responses of structures. These shortcomings arise primarily from the nonlinear behavior of the primary structure in severe seismic loading and the associated period shift that causes a detuning [30; 32]. As long as the structure behaves within the linear range of behavior, which is the case in the present study, this shortcoming is less pronounced. In the field of nuclear engineering, the TMD systems have been popularly used to mitigate the seismic response of structures [33; 34; 35; 36; 37]. For instance, [PERSON] et al. developed multiple TMDs to reduce the vibration of the piping system in NPPs due to earthquakes [34; 37]. Only a few studies in the past have used TMDs for the mitigation of the responses of nonstructural components. [PERSON] [38] illustrated that the use of a higher-mode TMD could significantly reduce the spikes of floor acceleration spectra of building structures mitigating the seismic force demands on nonstructural components. [PERSON] et al. [39] used TMDs to reduce the seismic responses of piping systems. [PERSON] et al. [33] used a TMD to improve the seismic response of an electric facility. This study aims to fill the technical gaps which have not been particularly highlighted in the present literature. The main objective is to evaluate the seismic responses of electrical cabinet subjected to the high- and low- frequency ground motions. The TMD is also used as a control device, which can reduce the response of the electric cabinet to improve the stability of electrical components. Based on the obtained outcomes, the correlation of seismic performance between two groups of earthquakes and two numeric models with and without applying TMD is investigated.
## 4 Conclusions  
Water and nitrogen deficiency are two of the main limiting factors, which has a great influence on plant condition and yield. In this study, hyperspectral and physiological measurements were combined in an attempt to characterize the spectral response of celery and sugar beet to varied nitrogen fertilization under different water availability conditions. The use of remote sensing methods allows for non-invasive research, which increases the possibilities of continuous monitoring of the plants growing conditions. Nitrogen fertilization increased leaf N content in both low and optimal water statuses. Excess water supply (high water) improved the nitrogen accumulation in sugar beet leaves, whereas the N content in celery leaves decreased. Among the tested classifiers, the RF classifier showed the best results in classifying nitrogen content at different water availabilities. In future research, the impact of water availability on the content of other macro elements in plant leaves will be examined.  
Conceptualization, A.S. and M.S.; methodology, A.S. and M.S.; software, A.S.; formal analysis, J.K.; data curation and calculations, A.S. and M.S.; writing--original draft preparation, M.S. and A.S.; editing, M.S. and P.B. ([PERSON]); supervision, W.K.; project administration, P.B. ([PERSON]); visualization, M.S. and P.B. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This paper was partly financed from funds of the Polish National Centre for Research and Development in frame of the project: \"MSINiN\", contract number: BIOSTRATEG3/343547/8/NCBR/2017. The research was partially financed by the National Science Center (NCN-Poland) grant No. 2016/21/D/ST10/01947.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{4}{*}{**Category**} & All & 0.778 & 0.074 & 0.797 & 0.778 & 0.781 & \\  & N33 & 0.778 & 0.000 & 1.000 & 0.778 & 0.875 & \\  & N67 & 0.889 & 0.074 & 0.800 & 0.889 & 0.842 & 0.81 \\  & N100 & 0.677 & 0.074 & 0.750 & 0.667 & 0.706 & \\  & N133 & 0.778 & 0.148 & 0.636 & 0.778 & 0.781 & \\ \hline \multirow{4}{*}{**Category**} & All & 0.806 & 0.065 & 0.841 & 0.806 & 0.810 & \\  & N33 & 0.667 & 0.000 & 1.000 & 0.667 & 0.800 & \\  & N67 & 0.778 & 0.148 & 0.636 & 0.778 & 0.700 & 0.81 \\  & N100 & 0.889 & 0.000 & 1.000 & 0.889 & 0.941 & \\  & N133 & 0.889 & 0.111 & 0.727 & 0.889 & 0.800 & \\ \hline \multirow{4}{*}{**Category**} & All & 0.806 & 0.065 & 0.819 & 0.806 & 0.808 & \\  & N33 & 0.778 & 0.000 & 1.000 & 0.778 & 0.875 & \\ \cline{1-1}  & N67 & 0.778 & 0.111 & 0.700 & 0.778 & 0.737 & 0.81 \\ \cline{1-1}  & N100 & 0.778 & 0.074 & 0.778 & 0.778 & 0.778 & \\ \cline{1-1}  & N133 & 0.889 & 0.074 & 0.800 & 0.889 & 0.842 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results of RF model obtained for classification celery plants subjected to four nitrogen supply levels and three water treatment levels. LW: low water supply, OW: optimum water supply, HW: high water supply.
_Applied_  
_sciences_  
_Article_  
**Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms**  
**[PERSON] 1, [PERSON] 1,2, [PERSON] 3, [PERSON] 4,*(c) and [PERSON] 3,*(c)**  
1 College of Computer Science and Engineering, Tabiah University, Yanbu 46421, Saudi Arabia; [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.A.M.)
2 Mathematics and Computer Science Department, Faculty of Science, Menoutia University,  
Shebin El Kom 32511, Egypt
3 Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University,  
Mansoura 35516, Egypt; [EMAIL_ADDRESS]
4 Electrical Engineering Department, Faculty of Engineering, Kafelsheikh University,  
Kafelsheikh 33516, Egypt
* Correspondence: [PERSON]DRESS] (R.A.E.-S.); [PERSON]ESS] (M.A.E.)  
**Abstract:** The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter \(\alpha\) adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature.  
**Keywords:** metaheuristics; solar cell systems; elephant herding optimization; alpha tuned EHO; cultural-based; biased initialization; parameter identification; single diode; double diode; three diodes 4.0./.
###### Abstract  
Computed tomography (CT) is the primary source of diagnostic radiation in pediatric patients. Patient head size and tissue attenuation are critical factors for estimating CT radiation doses. This study aimed to determine a size-specific dose estimate based on the water-equivalent diameter (SSDE\({}_{\text{Dw}}\)) for pediatric CT head examinations, categorized by age group, and to investigate the parameters influencing the SSDE\({}_{\text{Dw}}\). This retrospective analysis included 274 pediatric patients aged 0 to 15 years who underwent non-contrast CT head examinations using an age-based protocol without automatic exposure control systems. The SSDE\({}_{\text{Dw}}\) was calculated using the CTDI\({}_{\text{vol}}\), and the conversion factor was derived from AAPM Report No. 293, based on the water-equivalent diameter (Dw). We found that the SSDE\({}_{\text{Dw}}\) of age groups of 0 to 6 months, 6 months to 3 years, 3 to 6 years, 6 to 12 years, and 12 to 15 years were 15.4 (14.8, 15.8), 20.1 (19.6, 20.6), 25.3 (24.6, 25.7), 28.1 (27.3, 28.8), and 35.1 (34.6, 36) mGy, respectively. Age and body weight significantly affected the SSDE\({}_{\text{Dw}}\), with high R-squared values of 0.87 and 0.63, respectively (\(p<\) 0.001). The SSDE, particularly when based on the water-equivalent diameter (SSDE\({}_{\text{Dw}}\)), is a valuable supplement to the DLP and the CTDI\({}_{\text{vol}}\) as it closely relates to patient dose, especially for pediatric head scans of different patient sizes.  
multi-detector computed tomography; size-specific dose estimate; head CT examination +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Materials and Methods  
Machine learning for optical communications is an emerging topic that will certainly gain more interest in the near future. Machine learning includes a broad spectrum of tools that allows for the interpretation and understanding of data through a trained algorithm. Broadly speaking, there are two main types of ML algorithms: supervised and unsupervised. Supervised learning algorithms are trained using labeled examples, such as an input where the output is known. On the other hand, unsupervised algorithms do not assume a known output for a set of input values [29]. Input variables -or predictors- are denoted with an \(X\) whereas the output variable or response is denoted with a \(Y\). Usually, the predictors are more than one and it is assumed that a general relationship among them exists,  
\[Y=f(X)+\varepsilon \tag{1}\]  
where \(f\) is an unknown function and \(\varepsilon\) is a zero mean random error, statistically independent of \(X\). Supervised learning is focused on estimating this function \(f\) for prediction and inference purposes. Having estimated \(f\), one can predict the output for a specific set of input variables. The accuracy of this prediction depends upon the so-called reducible and irreducible errors. The reducible error refers to the inherent inaccuracy due to any inappropriateness of the function \(f\), whereas the irreducible error is included otherwise, since \(Y\) is a function of \(\varepsilon\). On the other hand, inference refers to the understanding of the relationship between inputs and outputs and the impact of certain changes in inputs on their corresponding output [29]. Problems with a quantitative response are called regression problems, whereas problems with a qualitative response are called classification problems. The various ML algorithms are also categorized as parametric or non-parametric. A parametric model assumes a specific form or shape for the function \(f\), such as a linear or polynomial regression model. A non-parametric, on the other hand, does not make any assumptions about \(f\); rather, it tries to estimate its form based on the training data points [29]. This paper is focused on supervised learning methods for regression models.