esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind-diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator's inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind-diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients.  
**Keywords:** frequency regulation; inertial emulation; DFIG-VSWT; hybrid isolated power system; wind-diesel power system
# Performance Improvement in a Wavy Twisted Rudder by Alignment of the Wave Peak
## 4 Conclusions  
In the present study, an improved wavy twisted rudder was proposed based on a previous study [10]. The third wave peak of the WTR was aligned with the centerline of the propeller for the bulb effect. The performance of the AWTR was verified through model tests and numerical analyses. The conclusions are as follows:  
1. In terms of the self-propulsion performance, the efficiency of the AWTR was superior to that of the WTR, although the gain was not large (+0.3%).
2. Regarding the performance of the rudder force, the lift, drag, and torque of each rudder were computed and measured for rotation angles from 5\({}^{\circ}\) to 40\({}^{\circ}\) in increments of 5\({}^{\circ}\), to both the portside and starboard. On the portside and starboard turns, the lift coefficient distribution of the AWTR was greater than that of the WTR in both the model test and the numerical analyses. The stall point of the AWTR was delayed by approximately 5\({}^{\circ}\) compared with the WTR on both turns.
3. The proposed AWTR was superior to the WTR and had the advantages of a high lift-to-drag ratio and normal force and drag, especially in the high-rudder-angle range.
4. The numerical computations were compared with the experimental results. Although discrepancies did exist between them, the trends in both methods were qualitatively consistent.
5. A simulation of the actual maneuverability of the ship (zig-zag, turning circle, stopping) equipped with the developed AWTR will be conducted in the near future.  
This study is the result of collaborative teamwork. Project administration, validation, writing--original draft, Y.-J.S.; conceptualization, supervision, M.-C.K.; investigation, writing--review and editing, J.-G.K.; visualization, J.-W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT and Future Planning (NRF-2019R1F1A105808013).  
Not applicable.  
The study did not involve humans.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
### Measurement Results  
During the wind tunnel tests, the measured vibration time histories exhibited unsteady behaviors in both the 3D prototype prism and 2D section model due to high dominant vibration frequencies and damping ratios. The determination of vibration amplitudes at a certain oncoming wind-velocity point was evaluated by amplifying the root mean square (RMS) of the fixed 55 s time history of responses by a factor of \(\sqrt{2}\). The 55 s duration was proven to be long enough to ensure the result was reliable and repeatable based on the collected 180 s time history.  
Figures 5 and 6 exhibit the time series of crosswind responses and the corresponding power spectral density (PSD) at the middle and quarter points of the 3D prototype prism, respectively. It can be observed in Figure 6 that the crosswind oscillations of the 3D prototype prism are dominated by the fundamental mode at 12.89 Hz without contributions from higher modes. The experimentally measured curves of dimensionless amplitude responses (A/D) versus normalized wind velocity (U/\(f_{0}\)D) and dominating frequency curves are illustrated in Figure 7. VIV lock-in is clearly observed, and the wind velocity for the peak of lock-in is 8.3 m/s. Experimentally, the measured dimensionless amplitude (A/D) responses at the middle and quarter spans of the prototype prism were respectively obtained and were equal to 0.036 and 0.026, with a ratio of 1.38.  
The VIV responses of the 1:1 2D section model are demonstrated in Figures 8 and 9. Nonconstant and unsteady amplitude is observed in the time-domain. However, the resonant single-frequency PSD curves can be obtained and are shown to coincide with the results of the 3D prototype prism tests. Figure 9 presents the heaving mode amplitude-velocity response and its corresponding frequency-velocity response. A single VIV frequency (a dominant peak in PSD close to the heaving mode natural frequency) lock-in is observed. No significant response is observed for the rolling and pitching modes.  
Figure 8: Heaving mode oscillation details (U = 7.8 m/s, U/\(f_{0}\)D = 7.4) of the two-dimensional (2D) section model.  
Figure 6: Crosswind oscillation details (U = 8.3m/s, U/\(f_{0}\)D = 7.9) at the quarter point of the 3D prototype prism.  
Figure 7: Crosswind amplitude and frequency response of the 3D prototype prism.
## 4 Applications of Intelligent Acoustic Monitoring
### Health Monitoring and Ambient Intelligence  
This subsection presents the use of sound in health diagnostics and support for everyday functioning, in the context of speech monitoring and disorder detection [119] and MEMS microphones in biomedical monitoring [120].
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 4 Numerical Experiments
### Convergence Rates for Base Cases  
Our first test is devoted to comparing the convergence rates for base cases. Table 3 provides the total iterations for the studied systems. As observed, NR failed in all studied cases, which denotes a high degree of ill-conditioning [1]. RBS only successfully solved the 89- and 1354-bus cases, while NRJ failed in the 9241- and 13659-bus cases. On the other hand, BEM converged to the low voltage solution in the 13659-bus system. The developed PF solvers successfully solved all the studied cases. In addition, our proposals usually required less iterations than the remainder of the methodologies. These results serve to empirically demonstrate the robustness characteristic and efficiency salient features of the developed PF solvers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Buses**} & \multirow{2}{*}{**Branches**} & \multirow{2}{*}{**Generators**} & \multicolumn{2}{c}{**Load**} & \multirow{2}{*}{**MVar**} & \multirow{2}{*}{**\(n\)**} \\  & & & & & & \\ \hline
89-bus & 89 & 210 & 12 & 5727.9 & 1374.9 & 165 \\
1354-bus & 1354 & 1991 & 260 & 73,059.7 & 13,401.4 & 2447 \\
2869-bus & 2869 & 4582 & 510 & 132,437.3 & 29,007.8 & 5227 \\
9241-bus & 9241 & 16,049 & 1445 & 312,354.1 & 73,581.6 & 17,036 \\
13659-bus & 13,659 & 20,467 & 4092 & 381,431.9 & 98,523.4 & 23,225 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main characteristics of the studied systems.
# Catalytic Degradation of Anionic Organic Dye on Greenly Synthesized CuO/ZnO Nanocomposites
## 2 Materials and Methods
### Experimental Procedures
#### 2.2.3 Catalytic Degradation Experiments  
The catalytic degradation of methyl orange (MO) was performed to evaluate the catalytic activity of the CuO/ZnO nanocomposites. The catalytic degradation tests were performed by mixing 3 mL of MO solution (40 ppm, pH = 4.5) with 0.2 mL of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and 0.01 g of the nanocomposite. The degradation was followed by measuring the UV absorbance every 10 min for 3 h. The absorbance measurement was performed with a UV-Vis spectrophotometer (Shimadzu) at \(\hat{s}_{\text{max}}\) of 465 nm. The percentage of catalytic degradation was calculated by Equation (5):  
\[\%\ Degradation=\frac{\text{A}_{0-\text{A}}}{\text{A}_{0}}\times 100 \tag{5}\]  
where A\({}_{0}\) is the initial absorbance of MO at \(\hat{s}_{\text{max}}\), while A is the absorbance at time t.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 3 Recent SDN Topology Discovery Performance Studies
### Link Discovery Improvement Algorithms
#### 3.1.1 Periodic  
This domain is named periodic because the discovery process is carried out periodically for every period. The authors in [39; 42; 43; 45] used this type as we will discuss. In [39], the authors proposed a new approach called OpenFlow Discovery Protocol version 2 (OFDPv2) to enhance the OFDP performance. OFDPv2 reduces the number of LLDP Packet_Out messages to only one LLDP Packet_Out message per SDN-Switch instead of per active port. They proposed two versions of OFDPv2. OFDPv2-A for SDN-Controller and OFDPv2-B for SDN-Switch. In OFDPv2-A, the SDN-Controller will install an additional set of flow rules in each SDN-Switch using the OFPT_FLOW_MOD message to forward the LLDP packets from each port on the SDN-Switch. The OFPT_FLOW_MOD message is used by the SDN-Controller to process the flow tables of the OpenFlow SDN-Switches. It can add, update or delete flow entries from the flow tables of OpenFlow SDN-Switches. However, added rules consume a lot of Ternary Content-Addressable Memory (TCAM) which is already a scarce resource in SDN-Switches. In OFDPv2 B, they did not install flow rules on SDN-Switches but they send an action list (i.e., a set of instructions to forward LLDP packet for each port) with the LLDP Packet_Out message. However, this makes OFDPv2-B withstand a large bandwidth, especially for in-band control channels. These processes are illustrated in Figure 8.  
Figure 8: Methodology of OFDPv2. Based on Figure 8, the SDN-Controller will send one LLDP Packet-Out for each SDN-Switch in the network. S1 will receive a Packet_Out message and extract an LLDP packet from it. Then it will forward the LLDP packet to all active ports in the SDN-Switch and replaces the source MAC address of the LLDP packet with the MAC address of the
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 1 Introduction
### Problem Statement  
The emphasis on using high-resolution satellite imagery for land cover mapping highlights the limitations of traditional classification methods [8]. While studies showcase the effectiveness of random forests and support vector machines with high-resolution data, such challenges as sensitivity to parameters and class imbalance persist, which requires careful optimization [15,16]. Land cover data is crucial in urban planning to address global challenges, but limitations in satellite image analysis, such as managing large data volumes and complex classifications, remain [17]. To address these challenges using very high-resolution (VHR) imagery, integrated approaches combining object-based image analysis (OBIA) and random forests have been proposed [18]. However, even these approaches face issues like spectral redundancy and the computational demands of incorporating temporal features [18].
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
###### Abstract
These methodologies use a wide range of qualitative, semi-quantitative, and quantitative approaches [6]. In the meantime, the determination of an absolute risk is a complex, time-consuming, and expensive process, in addition to the high inherent uncertainties associated with the result. On one hand, this renders the determination of an absolute risk an impractical feat, and on the other hand, the implementation of the concept of risk-based decision-making is inevitable, especially for infrastructure-related projects. A wide range of methodologies is already in use in different industries such as infrastructure management, oil, and gas, as well as environmental protection. Despite extensive investigations on the condition assessment of external post-tensioning systems in structures [7; 8; 9], a lack of informed decision-making framework for the selection of the inspection method can be recognized as a critical knowledge gap. This paper aims at developing and customizing a risk-based methodology for the selection of the inspection method (RBSIM) that is applicable to the external post-tensioning system and tendons. To address the knowledge gap, nondestructive evaluation (NDE) methods, corresponding accuracy for the detection of the section loss, the current condition of the post-tensioning system, and the accuracy of current condition are considered to measure the likelihood of \"error in detection of section loss\". To help the situation, statistical logic is employed to convert the qualitative approach to a quantitative approach. Aggregation of the likelihood for several attributes associated with each method-condition is performed using the joint probability density function [10]. Figure 1 shows the general procedure of the proposed risk-based selection of inspection method (RBSIM). The approach proposed is applied to an actual post-tensioning system of a bridge which was under investigation by the authors for potential damages to steel elements from corrosion and section loss. Data and information for the case study that is based on the proposed procedure is presented in the paper. The results, in this case, converged to the selection of one of the two-stage NDE methods. This method had the minimum risk within the available NDE methods.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 7 Future Direction  
Machine learning such as deep learning algorithms should be implemented for extracting higher abstract levels of weeds and their relation to the seasonal changes of the paddy for more accurate weed identification. It is challenging to implement remote sensing techniques into paddy. However, when referring to the previous study, [PERSON] et al. [96] successfully classified Cynodon dactylon (bermudagrass) in a vineyard by integrating OBIA with a decision tree (DT) algorithm. [PERSON] et al. [46] also managed to produce a weed map of _Convolvulus arrenesis_ L. (bindweed) in a soybean field. Meanwhile, [PERSON] et al. [94] successfully generated a grass and sedge weed map in a paddy field using a deep learning technique. This study has similarities in shape, texture, and pattern that machine learning and deep learning techniques can classify. In addition, the integration of various platforms, such as ground-based and machine vision technologies, should be considered. Besides, various yield-determining factors, such as climatic or agronomic, should be considered during the developmental stages of paddy. By maintaining the vigorous development of paddy, the existence of weeds can be minimized due to the biological mechanisms of the crops, which can be used to suppress the growth response of weeds towards the crops during the competition process.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## Case Study: Spatial Interpolation of Meteorological Data in Thailand
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interpolation Method** & **Mean MAPE (\%)** & **Mean RMSE** & **Mean PAEE** & **Mean NMSE** & **R\({}_{\textbf{I}}\) (\%)** \\ \hline OK & 2.2915 & 2.2028 & 0.0229 & 0.5170 & - \\ MWK & 2.1813 & 2.1124 & 0.0218 & 0.4770 & 4.1040 \\ MWKK with k = 5 & 2.2087 & 2.1217 & 0.0220 & 0.4833 & 3.6795 \\ AMWKK with k = 5 & 2.1672 & 2.0804 & 0.0216 & 0.4641 & 5.5571 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different interpolation methods for annual humidity data in 2018.  
northeastern regions, lower pressure is observed in these areas. Conversely, the central and southern parts resembled flat areas, reflecting higher pressure levels. It was observed that pressure increased from north to south as altitude decreased and geographical features were taken into account. A difference in pressure distribution obtained from OK and the moving kriging variants was identified in the southern region, where lower pressure levels were broadly distributed using OK. In the central region, the high-pressure intensity (1001.09-1071.40 hPa) achieved from the OK model was more widely spread than those of the AMWKK, MWKK, and MWK estimates. Meanwhile, all moving window kriging methods generated a few distinct pressure values across the region. The MWK and AMWKK methods exhibited a similar distribution pattern, except in the central region. This also supported the results in Table 1, where the most effective performance was achieved by the AMWKK model, followed by MWK. The spatial distribution maps for the annual average of hourly relative humidity are plotted in Figure 5, with the minimum value being 70.80% in the northeast and the highest value being 82.25% in the southernmost part of the study area. In every spatial model we obtained, the southern part of the study area always had a high relative humidity, as the values changed based on their distance from the ocean. This spatial distribution map was consistent with the relative humidity pattern, which indicates that the low values in northeast Thailand were connected to the arid climatic zones, while the southern areas directly adjacent to the sea coast had high relative humidity levels. Similar to pressure, the OK interpolation provided a different distribution pattern from other moving window kriging models, especially in northeastern Thailand, where relative humidity had high-value dispersion in some areas. Meanwhile, the MWK and AMWKK methods produced indistinct geographical predictions with similar distribution maps. This corresponded to the results in Table 2, in which the AMWKK provided the best estimation performance, and MWK the second best, respectively. Figure 4: _Cont._  
Figure 4: The annual pressure kriging estimate maps using the different spatial interpolation methods: (**a**) OK, (**b**) MWK, (**c**) MWKK with \(k=6\), and (**d**) AMWKK with \(k=6\). Figure 5: _Cont._
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 3 Mathematical Model
### Objective Function  
This model aims to minimize the LCC of FTU configuration scheme in distribution network. In order to take the impact of FTU configuration scheme on reliability of distribution network into account, the outage loss is incorporated. The objective can be calculated as:  
\[\text{min}LCC=C_{inv}+\sum_{i=0}^{n}\frac{1}{\left(1+r\right)^{t}}(C_{mt}+C_{ cns}) \tag{7}\]  
where \(C_{inv}\) is initial total investment cost; \(C_{mt}\) is annual cost for FTU maintenance; \(C_{cns}\) is annual outage loss of distribution network. Because of the long planning period, the \(C_{mt}\) and \(C_{cns}\) need to be converted into present value. \(\frac{1}{\left(1+r\right)^{t}}\) is the discount coefficient. \(r\) is the discount rate, and \(n\) is the years of planning period.
Both reactive oxygen and reactive nitrogen species (ROS and RNS) in an acidic environment are important for the bactericidal activity [3; 16]. The important role of peroxynitrite for a strong bactericidal effect has also been reported [17]. In terms of mechanism of antimicrobial action, PAW treatments can disrupt the outer and cytoplasmic membranes of bacteria, and be accompanied by leakage of intracellular components from the cells [3]. Plasma activated liquids have shown toxic effects not only on microorganisms, but also on the cells of higher organisms. Plasma generated reactive chemical species can cause mitochondrial dysfunction, caspase activation, cell cycle arrest, apoptosis, senescence or necrosis in mammalian cells [18]. Measuring the toxicity of these solutions in a model dependent manner, can provide helpful information as to whether or not PAL could be further used for antimicrobial activity on abiotic and biotic surfaces. A dominant function for H\({}_{2}\)O\({}_{2}\) in the cytotoxic effects of PAL has been established [19] but, importantly, this compound is not the only chemical species that causes the cytotoxic effects. [PERSON] et al. reported that hydrogen peroxide could not account alone for the toxicity in cell lines, but nitrites and hydrogen peroxide synergistically triggered cell death [20]. Peroxynitrous acid/ peroxynitrite can be generated by the interaction of nitrites and hydrogen peroxide [21; 22] and can subsequently induce cell death by both cellular apoptosis and necrosis depending on the production rates and endogenous antioxidant levels [23]. The aim of this study is to assist the development of PAL as novel decontamination agents (for abiotic surfaces), and/or as antiseptic agents (for biotic surfaces, such as skin) which could be used as wound disinfectants, hand sanitisers or for washing at surgical sites. This study helps to build up an understanding of how plasma reactive species in liquid translate to different biological effects by employing two types of plasma activated water with very distinct ROS- and RNS-rich chemistries. Ideally, PAL should retain high level antimicrobial activity, but cause minimal or acceptable cytotoxicity in surrounding or exposed tissues. Plasma activated water, as the simplest form of a plasma activated liquid, generated using a spark (SD) and a glow (GD) cold atmospheric plasma discharge, respectively, was investigated for its antimicrobial properties on Gram-negative and Gram-positive bacteria. The PAW was characterised in terms of its chemical composition and stability of the chemistry and antibacterial effect to a high temperature and pressure. The effect in other biological systems, namely the impact on mammalian cell growth and activity of the enzyme lactate dehydrogenase (LDH) were also determined. A significant finding of this work is the bactericidal stability of plasma activated water at different elevated temperatures and pressure, making heated PAW a novel disinfect with a broad range of potential applications.