esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Single-Stage Co-Optimization of Microgrid Sizing and Energy Management  
Figure 8 illustrates the general framework of the single-stage optimization problem. This approach has few references in the literature due to the computational challenges. Within this approach, MILP is one of the most widely used utilities for solving single-stage optimization problems by meeting microgrids' sizing and energy management. In [71], a MILP framework integrates sizing and energy management into one optimization problem by analyzing scenarios for Reunion Island's transition to renewable energy by 2030 and 2050. Another example can be found in [117], where the optimization problem addresses the optimal sizing and energy dispatch of a residential microgrid incorporating PV, a WT, and the power grid connection. In [118], the authors demonstrate a cost optimization approach incorporating hourly dispatch and load demand within an isolated microgrid. In [119], cost minimization is performed through MILP, complemented by an additional sensitivity analysis conducted over the LPSP. Similarly, in [120], a multi-objective function  
Figure 7: Rule-based decision making in microgrid energy management (alt: alternative, opt: optional).  
is formulated using MILP and a trade-off constraint approach, aiming to minimize costs and emissions simultaneously. Concentrating on minimizing the LCOE, the research presented in [121] also formulates the optimization problem using MILP. The results highlight that a microgrid composed of WT, PV, diesel, and BESS exhibits the lowest LCOE compared to alternative combinations. In [122], MILP is similarly employed; the authors explore scenarios wherein a PV-FC-BESS system fulfills residential energy requirements within a grid-connected microgrid environment. The study in [123] introduces a multi-objective optimization approach for the planning and operation of grid-connected microgrids, incorporating DER. It focuses on achieving economic efficiency and enhancing customer satisfaction by implementing demand-side management and employing a fuzzy logic-based method to maximize satisfaction. In [124; 125; 126], sizing and energy management are addressed through a single-stage optimization problem using a MILP approach to fully meet the load requirements in grid-connected microgrids and isolated operation modes. The objective function in [124] aims to minimize the cost and emissions over the project lifetime, whereas the objective function in [125; 126] focuses solely on cost minimization.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
###### Abstract
This article intends to use the fuzzy fault tree analysis method to analyze the various conditions of the coal mine gas monitoring system failure [15], in order to achieve the following purposes: (1) Identify the basic events and human errors that lead to gas monitoring system accidents; (2) provide clues to avoid the basic causes of accidents, in order to reduce the possibility of accidents; (3) make a comprehensive, concise, and vivid description of the various factors and logical relations leading to disasters and accidents; (4) identify all kinds of inherent or potential risk factors in the system and provide a scientific basis for design, construction, and management; (5) make the relevant personnel and operators fully understand and master the key points of disaster prevention; (6) produce a qualitative and quantitative analysis and systematic evaluation of the safe operation of coal mine gas monitoring systems.
## 5 Results and Discussion
### Comparative Analysis of Image Enhancement with Existing Image Enhancement Techniques  
We performed a comparative analysis of the proposed image enhancement method against existing techniques. We compared the performance of our proposed method with histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE), and brightness preserving bi-histogram equalization (BBHE). The proposed method yielded more enhanced images with outstanding PSNR performance and contrast enhancement. Table 1 shows the comparative analysis of our proposed method against the techniques of HE, CLAHE, and BBHE. As shown in Table 1, the proposed method obtained an improved PSNR and contrast in the HRF and Oasis brain MRI databases. The PSNR improved around 3 dB in the images of both databases, the contrast improved from 6 to 25 in the HRF database, and it improved from 13 to 27 in the brain image database. This shows the capability of the technique for the analysis of medical images such as retinal fundus images from the HRF database and the Oasis brain MRI for brain abnormalities.  
Figure 9: Overall Performance Analysis. (**a**) Overall performance analysis of Oasis image database. (**b**) Overall performance analysis of HRF retinal image database.
## 1 Introduction  
The therapeutic properties of ethnobotanicals are adduced to the functional roles of their pharmacologically active phyto-constituents [1; 2]. Consequently, ethnobotanicals have been reported to possess ameliorative potentials towards some physiological anomalies including hyper/hypo-tension, atherosclerosis, hypo/hyper-glycaemia, erectile dysfunction, antiphlogistic, immunomodulatory effects, anti-allergic activities, anti-neoplastic properties, and the corrective effects/managementof several metabolic syndromes in humans and livestock [3; 4; 5]. In the same vein, functional properties of ethnobotanicals unconnected with the amelioration of abnormal physiological processes in humans and livestock include fungicidal, antibacterial, and antiviral activities [2; 6]. The plethora of desirable functions displayed by ethnobotanicals, as elicited by the myriad phyto-constituents, has been chronicled in several studies.  
_Vitex doniana_ and _Pentaclethra macrophulla_ are folklore herbs common with herbal healers of Southeastern Nigeria; tribal communities in this geographical region use these herbs in folk medicine for the treatment of various ailments [7; 8; 9; 10]. _V. doniana_ and _P. macrophulla_ are respectively known as black plum and African oil bean commonly. Further, _V. doniana_ and _P. macrophulla_ belong to the Verbeneaceae and Leguminosae families, respectively [11; 12]. These plants are widely distributed in the Eastern and Western parts of Nigeria [13; 14; 15]. Decoctions of _V. doniana_ are administered orally in the treatment of gastrenteritis, diarrhea, and dysentry [8; 16; 17]. Similarly, the roots and leaves decoctions of _P. macrophulla_ are used as a laxative and in the treatment of dysentery, while the ripe fruits and stem bark powder are applied externally to heal wounds and burns [10; 18]. Additionally, _P. macrophulla_ have been used as analgesics, anthelmintics, and for the treatment of gonorrhea, diarrhea, convulsion, and inflammatory diseases [7; 12; 18; 19].  
Saponins constitute a major phytochemical grouping, and over 100 vascular plant species have been documented to possess various kinds of saponin [20]. Saponins are structurally diverse and consist of polycyclic aglycones attached to one or more sugar side chains [21]. Traditionally, they are subdivided into triterpen and steroid glycosides [21; 22; 23]. Saponins exert a wide range of pharmacological activities including antibacterial, antifungal, antiviral, anti-parasitic, anti-inflammatory, hypocholesterolemic, and hypoglycemic [20; 22; 24]. In cognizance of the folk practices of cold maceration in water or admixtures of locally brewed alcohol of _V. doniana_ and _P. macrophulla_ before administration, the reported work aimed at extracting saponins from _V. doniana_ and _P. macrophulla_ and respectively evaluating their antibacterial activity.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
## 3 Results
### Multiple Radio Source Localization
The MUSIC spectrum considering element directivity is shown in Figure 19.  
Next, assuming localization for a mobile terminal, the transmission power of the signal source was set to 200 mW and localization performed based on the cell deployment of base stations, as shown in Figure 20. As in the previous scenario, the influence of buildings was not considered. \begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{\times}\mathbf{1}\) & \(\mathbf{\times}\mathbf{4}\) \\ \hline Mean Error [m] & 11579 & 659.8 \\ CDF 50\% [m] & 1093.8 & 264.5 \\ CDF 90\% [m] & 13289 & 886.8 \\ Detection Percentage [\%] & 99.09 & 99.91 \\ \hline \end{tabular}
\end{table} 
Table 8: Localization with varying element spacing (40 transmission sources). Figure 18: Cumulative distribution of localization error with the MUSIC method considering element directivity. \begin{table}
\begin{tabular}{l c c c} \hline  & \begin{tabular}{c} **Directivity Considered** \\ **Element Spacing: \(\mathbf{\times}\mathbf{1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Directivity Not Considered** \\ **Element Spacing: \(\mathbf{\times}\mathbf{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **Directivity Not Considered** \\ **Element Spacing: \(\mathbf{\times}\mathbf{1}\)** \\ \end{tabular} \\ \hline Mean Error [m] & 2073.4 & 516.4 & 1717.9 \\ CDF 50\% [m] & 252.8 & 277.6 & 337.7 \\ CDF 90\% [m] & 825.7 & 918.0 & 2017.5 \\ Detection Percentage [\%] & 99.88 & 99.94 & 99.85 \\ \hline \end{tabular}
\end{table} 
Table 9: Localization using the MUSIC method incorporating element directivity. The MUSIC spectrum obtained for each element spacing is shown in Figure 24.  
Figure 23: Cumulative distribution of estimation errors in cell-based deployment with varying element spacing. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(\mathbf{\times 1}\) & \(\mathbf{\times 2}\) & \(\mathbf{\times 3}\) & \(\mathbf{\times 4}\) \\ \hline Mean Error [m] & 710.7 & 456.9 & 207.5 & 116.8 \\ CDF 50\% [m] & 264.9 & 105.1 & 55.1 & 47.5 \\ CDF 90\% [m] & 1909.4 & 1692.9 & 154.7 & 87.7 \\ Detection Percentage [\%] & 92.74 & 99.80 & 99.98 & 99.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Localization of signal sources in cell-based deployment with varying element spacing. Figure 24: MUSIC spectrum in cell-based deployment with varying element spacing.
## 5 Conclusions and Future Work
### Future Work  
In the future, several directions can be explored to further enhance and extend the research presented in this paper. Future research will focus on the following areas:  
* Improving Volume Estimation Accuracy. For foods with complex shapes, explore more advanced 3D reconstruction technologies and introduce more perspectives and depth information to enhance volume estimation accuracy.
* Optimizing System Performance. Enhance the system's operational efficiency while ensuring model accuracy, reducing runtime, and improving the user experience.
* Expanding Datasets. Collect and annotate more food images and nutritional data to broaden the diversity and scale of datasets, further verifying and enhancing the system's performance.
* Expanding the Application Scope of Language Models. Introduce more nutrition and diet-related datasets to improve their generation capabilities and accuracy in diverse language environments.  
* Implementing Diverse Functions. Based on user feedback and needs, develop more practical functions, such as personalized diet plan generation and real-time health indicator monitoring, to enhance the system's usability. We hope to provide a more comprehensive and accurate food classification and nutritional recommendation system through these improvements and expansions, offering excellent assistance in users' health management.  
Conceptualization, S.-T.C.; Methodology, Y.-J.L. and C.T.; Software, C.T.; Validation, Y.-J.L. and C.T.; Formal analysis, Y.-J.L.; Investigation, Y.-J.L.; Writing--original draft, C.T.; Writing--review & editing, Y.-J.L.; Supervision, S.-T.C.; Project administration, S.-T.C. 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.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 2 Seismic Event Conditions  
### Measurement Conditions  
In this study, a numerical simulation method utilizing the ANSYS LS-DYNA 970 software is employed to analyze a specific seismic event that occurred on 18 February 2012. This event serves as a practical case study, analyzing the dynamics of seismic-wave propagation through geological structures. This analysis confirms the accuracy of the simulation's methods. The seismic event with the epicenter shown in Figure 1 was recorded at the coordinates 130.703\({}^{\circ}\) E longitude and 42.587\({}^{\circ}\) N latitude. This seismic event, measured at a focal depth of 578 km and a moment magnitude of 6.9, was recorded using data processed from measurements at the Yanbian (YNB) and Mudanjiang (MDJ) seismic stations. The YNB seismic station, located at 130.550\({}^{\circ}\) E longitude and 42.590\({}^{\circ}\) N latitude, is approximately 12 km from the epicenter and directly above the hypocenter, which is at a depth of 578 km. The station is constructed on a granite base. The MDJ seismic station is situated at 129.592\({}^{\circ}\) E longitude and 44.616\({}^{\circ}\) N latitude, approximately 242 km from the hypocenter. The amplitudes of the velocities recorded at the YNB and MDJ seismic stations, representing the combined velocities of seismic waves traveling in three directions, are directly measured and presented as resultant velocities in Figure 2. As depicted in Figure 2, at the YNB seismic station, the velocity reaches a peak of 1.025 mm/s at 116 s after the onset of the seismic event. Meanwhile, at the MDJ seismic station, located farther from the epicenter, the velocity peaks at 0.768 mm/s at 123 s following the onset of the seismic event.  
By applying the Gutenberg-Richter formula, a correlation was established between the earthquake magnitude and its energy release, as described by Equation (1) [29].  
\[\lg\ E=aM+b, \tag{1}\]  
where \(E\) denotes the energy in J, and \(M\) represents the magnitude, with the unit in Nm. For surface-wave magnitude, \(a=1.5\) and \(b=4.8\), whereas for moment magnitude, \(a=1.5\) and  
Figure 1: Geographic positions of the hypocenter (130.703\({}^{\circ}\) E, 42.587\({}^{\circ}\) N) and the nearby YNB and MDJ seismic stations for the 18 February 2012, seismic event.  
Figure 2: The seismic-wave amplitude in velocities (combined velocities of seismic waves traveling in three directions). Time = 0 s corresponds to the onset of the seismic event.  
\(b=9.1\)[29]. It is assumed that these coefficients are sufficiently representative of the area being analyzed, despite being initially derived from global data [29].
## 3 Results and Discussion
### Stability Analysis of Bacterorhodopsin in the Presence and Absence of Graphene  
The RMSD data for the protein backbone over time were obtained to determine the stability of the protein and compared for bR in both the presence and absence of graphene (Figure 2a). Based on the RMSD curve, even though the deviations for bR in the presence and absence of graphene are similar, the fluctuations in the presence of graphene are much smaller as evident from the standard deviation (with graphene 4.1 \(\pm\) 0.2 A and without graphene 3.7 \(\pm\) 0.5 A), indicating greater stability. In the case of bR-graphene, RMSD increased by -0.5 A from 10 ns to -65 ns, maintaining the stability as it adsorbed onto graphene. The peak at -70 ns indicates a change in conformation of bR due to its increased interaction energy with graphene. On the other hand, RMSD, for the bR protein alone, shows continuous small fluctuations around a stable average value with less overall deviation. The RMSD of graphene (Figure 2b) maintains a consistent deviation of -1.2 A with fluctuations of \(\sim\)1.5 A, indicating a fairly stable interaction with bR. Similarly, RMSD of the retinal showed a higher deviation in the presence of graphene compared to the control, indicating the effect the adsorption has on the diherals of the chromophore (Figure 2c). Finally, to analyze the behavior of bR in the vicinity of graphene, the distance between the centers of mass of bR and graphene was quantified. Over the entire period of the simulation, it was found that the centers of mass of bR and graphene came \(\sim\)10 A closer than in the beginning of the simulation, showing the attractive energies at play (Figure 2d).
## 5 Conclusions and Future Works  
The 3D scanning system is the most important part of the square lithium battery surface defect detection system. The target images are acquired through the image acquisition device in this system. After pretreatment, feature point extraction, using the system parameter coordinate conversion and other operational steps, get the 3D point coordinates and then complete the 3D reconstruction. The target process parameters are obtained by the camera calibration method, and the laser line extraction method is improved in the 3D reconstruction process based on the grayscale center of gravity method. The extraction accuracy and noise resistance of laser centerline extraction are improved.  
Experimental results show that the average absolute error of the improved laser line extraction method proposed in this paper is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of light strip can be extracted more accurately. It can achieve sub-pixel accuracy, construct the 3D reconstruction model more accurately, and provide a data basis for the subsequent use of the model to calculate defects.  
In future works, the defects in the detection object and the experimental equipment will be perfected. Because the point cloud is filtered according to the height of the plane, the smaller surface of the object may have limitations. So, the relationship between the point cloud data will be used to identify the defects, and the 2D plane vision will be combined to perfect the square lithium battery surface defect detection system.  
Conceptualization, R.Y. and B.W.; methodology, D.H. and L.W.; validation, H.L.; formal analysis, X.L.; data curation, M.H.; writing--original draft preparation, D.H. and B.W.; writing--review and editing, H.L.; funding acquisition, X.L. and D.H. All authors have read and agreed to the published version of the manuscript.  
The support of the Lianyungang 521 High-level Talent Training Project (LYG06521202203), Qing Lan project for the excellent teaching team of Jiangsu province (2022), National Natural Science Foundation of China (51975568), the Natural Science Foundation of Jiangsu Province under Grant (BK20191341), the Independent Innovation Project of \"Double-First Class\" Construction of China University of Mining and Technology (2022 ZZCX06), and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX23_2681) in carrying out this research are gratefully acknowledged.  
Not applicable.  
All the data that produce the results in this work can be requested from the corresponding author.  
The authors declare that there are no conflict of interest regarding the publication of this article.
## 2 Materials and Methods  
### Methods of Sampling and Characteristics of Test Material  
Substrates for anaerobic digestion were obtained from the organic fraction of municipal waste separated in the MHT plant in Rozanki (Poland). Autoclaving was preceded by the separation of large waste items from the mixed waste stream and mechanical homogenization (pre-preparation) to achieve grain sizes of \(<\)350 mm. The pre-prepared waste was treated in autoclaves under saturated steam conditions, at 120 \({}^{\circ}\)C-150 \({}^{\circ}\)C and 2-5 bar. The production and condensation of steam took place in a closed system. Mechanical treatment took place after the autoclaving process to separate recyclable materials. After autoclaving, the separated ORF had a particle size of \(<\)10 mm and comprised ca. 30% of the waste.  
A sample of ORF for anaerobic digestion was collected from a randomly formed heap of about 5 m\({}^{3}\), from which the laboratory sample was separated by the trapezoidal diverging method [13]. In accordance with the methodology, the sample was divided into three laboratory parts with a total volume of 45 L, which were then transported in sterile containers to the testing site. One part was subjected to basic physico-chemical and respirometric tests. The remaining two parts were ground to a grain size of \(<\)1 mm and then used as the research substrate. The grinding was necessary to facilitate feeding of the waste into the reactor through the narrow hole of the shutoff valve. Additionally, according to [PERSON] et al. [14], grinding enhances the solubilization of organic matter, which was important during the preparation of the feeding mixture (described in _Section 2.3_). Grinding also affects the process of methanization of organic matter [14].  
The physico-chemical properties of ORF are presented in Table 1. The ORF sample for anaerobic digestion had a neutral pH and an organic substance content of 58% DM. Morphological analysis indicated that this fraction was homogenous and had a glass share of 1-2% by mass, which limits its use as soil conditioner. The fraction showed high hygroscopicity--opening a container with a sample caused the dry matter content to decrease from 84% to 69% within 7 days. At 31% humidity, the AT4 value was 26.09 g O\({}_{2}\)/kg DM, which indicated the need to stabilize the fraction prior to storage. The analytical methods used for physico-chemical characterization of ORF are described below in Section 2.4.
## 3 Remote Terminal Unit (RTU)
### Embedded Software in the RTUs  
An embedded software is typically based on a microcontroller or digital signal processor (DSP) which has to perform one or more dedicated functions (commonly in real time). The embedded software which runs in the MCU PIC18F8723 is programmed in C language and using the compiler from Custom Computer Services (CCS). The most important routine is described below.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
###### Abstract  
A new geometry for uniplanar, ultra-wideband monopole antenna has been proposed for operations in the 1.8-30 GHz band, thanks to its fractal structure in the form of a cardioid. The antenna has extremely small dimensions at 0.21\(\lambda\)\(\times\) 0.285\(\lambda\) at the lowest frequency of 1.8 GHz. A parametric analysis of the influence of certain antenna dimensions on its characteristics was performed in order to achieve the widest possible impedance bandwidth. This antenna is designed for low-cost FR-4 substrate because it is primarily intended for use in broadband energy harvesting and IoT systems, but it is also suitable for applications in communication systems. Simulation results show that the antenna has a reflection coefficient (\(S_{11}\)) below \(-\)10 dB in the entire 1.8 GHz to 30 GHz frequency range, which covers all existing cellular bands: 3G, 4G, 5G Wi-Fi, ISM, satellite communication and radar bands. The antenna exhibits gains up to 5 dBi.  
fractal antennas; slot antennas; ultra-wideband antennas; bandwidth dimension ratio +
Footnote †: journal: Electronics Letters  
Article