esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Assessment under Distorted Weak Grid Condition  
### System Configuration  
In order to verify the theoretical analysis, the PSIM software-based simulations and experiments are carried out by using three-phase grid-connected inverter system. The system parameters are given in Table 2. Figure 15 shows the experimental configuration of the system. The overall system is composed of three-phase inverter connected to the grid through an LCL filter, a magnetic contactor for grid-connecting operations, and an AC power source to emulate three-phase grid voltages in the ideal as well as distorted grid conditions. The entire control algorithm is implemented on a 32-bit floating-point DSP TM320F28335 [35]. Additional inductances are employed to implement the grid impedance change. The sampling and inverter switching frequencies are chosen as 10 kHz. Figure 16 depicts the photograph of the experimental test setup.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbol** & **Value** & **Units** \\ \hline DC-link voltage & \(V_{DC}\) & 400 & V \\ Filter resistance & \(R_{1},R_{2}\) & 0.5 & \(\Omega\) \\ Nominal filter capacitance & \(C_{f}\) & 4.5 & \(\mu\)F \\  & & 10.0 & \(\mu\)F \\  & & 30.0 & \(\mu\)F \\ Filter capacitor resistance & \(R_{cf}\) & 16 & \(\Omega\) \\ Nominal inverter-side filter inductance & \(L_{1}\) & 1.7 & mH \\ Nominal grid-side filter inductance & \(L_{2}\) & 1.0 & mH \\ Grid voltage (line-to-line rms) & \(e\) & 220 & V \\ Nominal grid frequency & \(f_{g}\) & 60 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters of a grid-connected inverter.  
Figure 14: Comparison of the stability limit under the grid impedance variation for different LCL parameter sets: (**a**) Direct grid current controller; (**b**) Integral-resonant state feedback LQR controller.
## 1 Introduction
However, in the context of ethane leakage in complex large-scale systems, due to the lack of data and high levels of uncertainty, it may be difficult to accurately obtain the failure probabilities of the system. Therefore, the fuzzy set theory combined with FTA has emerged as a risk assessment model to address these challenges [25]. The fuzzy set theory, by introducing the concept of membership degrees, can more accurately describe fuzzy phenomena and uncertainties in complex systems. For instance, [PERSON] [26] introduced the fuzzy set theory to construct a dynamic risk assessment model for hydrogen leakage in hydrogen refueling stations. However, in complex systems, expert opinions may have differences in consensus, and it is necessary to aggregate opinions to obtain more reliable results. Since traditional fuzzy sets only contain membership functions and ignore the hesitancy in expert evaluations, it is necessary to adopt some aggregation techniques to combine fuzzy numbers. To represent the interaction relationships between risk events, Bayesian Networks (BNs) are crucial. BNs can capture and quantify the probabilities of unknown events through probability distributions and conditional probabilities. BNs have been applied in various risk management domains such as environmental risk [27], emergency management [28], cybersecurity [29], and supply chain management [30]. [PERSON] [31] constructed a risk-based accident model for quantitative risk analysis of submarine pipeline leakage failure by combining the bow-tie approach and BNs. Although this method can effectively customer service the limitations of FTA, it ignores the fuzzy phenomena in complex systems. [PERSON] [32] innovatively proposed Fuzzy Bayesian Networks (FBNs), which combine the fuzzy set theory and BNs and can effectively solve the problem of data uncertainty in complex systems; however, this fuzziness does not take into account the consensus mechanism among experts. This paper extends the previous research and proposes a new and more compatible FTA-FBN risk assessment method by introducing the weakest \(t\)-norm algorithm and the similarity aggregation method based on the integration of a fault tree and a fuzzy Bayesian network to reduce the uncertainty in the diagnosis of the leaking risk of ethane storage tanks. The weakest \(t\)-norm algorithm can be utilized to reduce the accumulation of ambiguity and improve the fuzzy accuracy of the system. The Similarity Aggregation Method (SAM) takes into account the similarity among the opinions of each expert, particularly when there is divergence or ambiguity in expert opinions. The SAM can reduce uncertainty by aggregating similar opinions, thereby addressing the shortcoming of previous FTA-FBN models that only considered expert weights. The remainder of this paper is structured as follows: Section 2 introduces the relevant theories and methods. Section 3 presents a practical case study, where both an FTA and an FBN model are established to conduct a quantitative analysis of the risk of ethane tank leakage. Section 4 concludes the paper with the research findings.
## 2 Categorization and Classification of Quality of Service Routing Protocols
### Deterministic Routing Protons (Hard Real Time)
In this technique, a mobile user is guided to locate the mobile object correctly without sending flooding requests to localize the mobile object. This protocol helps to preserve the energy consumption in order to prolong the network lifetime. In this approach, source and target names are used for mobile users and mobile objects respectively. The WSN helps the source node identify the target node, and, also, keeps the tracked information of a targeted node. The approach is based on active and sleeping modes. The source node is not required to communicate to the current location of the targeted node when detecting the location. This protocol applies a face routing process explained in [42] which is based on the idea of Gabriel Graph discussed in [43] for chasing the target correctly. It also focuses on the velocity of the targeted node and its  
Figure 4: Three level cluster hierarchy [38]. direction of movement. The protocol saves enough energy as compared with other object tracking WSN protocols. However, mobility scenarios are not completely explained and a latency issue still exists. QoS-based routing protocol for wireless multimedia sensor network (QRPWMSN) was introduced in [44] to perform routing on each data packet according to existing QoS standards by considering the delay, energy efficiency, and reliability. This protocol is based on the geographical information model. The protocol uses a genetic algorithm and a queuing theory. QRPWMSN weights each delay in order to consume less energy by maintaining the reliability for determining the best efficient path. All the nodes are fixed and possess individual identifiers illustrated in Figure 5.  
The advantage of this protocol is that it improves the transmission with less congestion However, the protocol is a bit complicated due to its use of the individual identifier that can increase the latency and reduce the throughput. In addition, it does not have mobility support and also consumes a substantial amount of energy for any transmission.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Strategies for Enhancing ZnO-Based Nanomaterials Antibacterial Activity  
#### 4.4.1 Alkaline Earth Metal Doping into ZnO  
Common alkaline earth metals including Ca, Mg, Al, and Sr, are frequently used to introduce significantly altered NPs, such as lattice defects and ionic radius differences between metal ions and Zn\({}^{2+}\) ions, which can improve optical properties and photodegradation activity of catalysts. Taking Sr as an example, it played an important role in enhancing the catalytic degradation ability of commonly used metal oxides such as ZnO, TiO\({}_{2}\), which can be used for photocatalytic degradation of organic pollutants in wastewater and photocatalytic antibacterial [186]. Due to the lack of local d orbitals in alkaline earth metals and the presence of local d orbitals in transition metals, the doping of alkaline earth metals is more effective in reducing the optical threshold energy of semiconductors than that of transition metals [187].  
Antibacterial activity of Mg-doped ZnO nanostructures was investigated by [PERSON] et al. [188] towards _E. coli_, _P. aeruginosa_ and _Staphylococcus aureus_. The zones of inhibition diameter of the Zn1\(-\)xMgxO sample against the selected bacteria pathogen are displayed in Figure 15. The results showed that all samples were susceptible to bacteria. The presence of more reactive sites allows surface defects to create space for ZnO to interact with microorganisms [189]. Therefore, the increase of surface defects in ZnO nanostructuresincreases the reaction sites and the rate of interaction with microorganisms. Bacteria are 250 times larger than nanoparticles [105], while the bacterias have a larger relative surface area, which makes it easier for nanoparticles of much smaller size to enter the interior of bacterial cells and cause damage.
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Dynamic-Obstacle-Avoidance Strategy Based on Artificial-Potential-Field Method  
The environment in which drones perform near-ground searches is dynamic and changes in real time due to the presence of other drones as well as flying animals. It is inevitable that drones will be in the same airspace as other dynamic obstacles in the course of their missions. The 3D JPS algorithm can only use re-search to replan the global paths in response to dynamic environments. With the dynamic-obstacle position constantly changing in real time, this will significantly reduce the drones' obstacle avoidance response speed. Therefore, in this paper, the drone flight trajectory in the path collision region is adjusted by introducing an artificial potential field to avoid the dynamic-threat-induced-collision phenomenon by means of dynamic potential field forces.  
In the process of dynamic-obstacle avoidance, the drone is required to monitor its alert range in real time, and when a dynamic threat enters the alert area, the artificial-potential-field method will be used to complete the obstacle avoidance behavior according to the real-time dynamic changes in the force until the threat leaves the alert area. After completing the obstacle avoidance behavior, the drone needs to determine whether there is direct reachability between the node where the current drone is located and the source path node through the 3D Bresenham's line algorithm, so as to achieve trajectory regression.
###### Abstract  
This paper presents an enhanced generalized extended state observer (EGESO) based sliding mode control (SMC) technique for dealing with the disturbance attenuation problem for a class of non-integral chain systems with mismatched uncertainty. In the proposed control law, the robust SMC with reaching phase elimination is applied in the proposed control law, which uses the estimated states of a system. The stability analysis is thoroughly examined for both EGESO and SMC. The efficacy of the proposed controller is verified using specific examples, and later it is applied on a single-link flexible manipulator. Through simulation and experimentation analysis, it is observed that the proposed controller is giving a robust transient response as compared to existing GESO based controllers.  
generalized extended state observer (GESO); mismatched system; sliding mode controller; non-integral chain form; real time experimentation Article  
Sliding Mode Controller with Generalized Extended State Observer for Single Link Flexible Manipulator
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 5 Experimental Results
### Quantitative Analysis
In grammar correction tasks, the source and target sentences are typically very similar, with errors manifesting in only some parts of the sentence. Hence, it is vital to avoid overcorrection. In this context, the linguistic fluency of ChatGPT sometimes emerges as a drawback. Nonetheless, there are instances where understanding the context and modifying word expressions accordingly are essential, suggesting that linguistic fluency is not always unnecessary. Therefore, it is crucial to discern the capabilities and types required for the task at hand and to validate these against the abilities of the LLM. The examples provided for 'neologism' and'remove' types serve as a comparison between the correction results of a commercialized system and those produced by ChatGPT. The commercialized system was chosen based on its superior performance in Korean grammar correction [[https://bit.ly/3](https://bit.ly/3) CtaPI4]([https://bit.ly/3](https://bit.ly/3) CtaPI4). The ChatGPT results were derived with a temperature setting of 0.2 and 8 shots. For the 'neologism' type, the commercialized system failed to detect and consequently correct the error. In contrast, ChatGPT successfully identified and rectified the neologism error. This capability can be attributed to the provision of explicit examples and descriptions regarding the neologism type of ChatGPT. This suggests that ChatGPT can display commendable performance with new types of errors using just instructions, without the need for additional training. However, for the'remove' type, while the commercialized system effectively corrected the text in alignment with the target sentence, ChatGPT overcorrected. This can be interpreted similarly to the 'tense' type, where linguistic fluency might have inadvertently introduced negative consequences. Thus, it is evident that ChatGPT's potential abilities are influenced by explicit example information and temperature settings. This demonstrates that adjusting the few-shots or temperature is an effective method to enhance ChatGPT's efficacy in K-GEC tasks. Moreover, given that this influence varies based on the error type, a nuanced analysis is imperative.
## 5 Global Welding Robot Path Planning
### Global Path Planning Based on PSO  
Because of the advantages of PSO, it is chosen as the optimization algorithm for global path planning. The steps of the globe welding path planning based on PSO are presented as follows [16]:  
Step 1. Initialization: the iterations \(N\) of the PSO is set as 100, the population size is set as 50, and the learning rates \(C_{1}\), \(C_{2}\) are set as 1.0. The weight is set as 0.4. The positions and velocities of the particles are initialized randomly. The local route can be obtained according to the local path planning algorithm. Each particle's fitness is evaluated in the light of Equations (6) and (7). Then, the personal best positions and the global best position of the particles are updated. The above parameter settings are shown in Table 4.  
Step 2. Update the positions and velocities of the particles according to the equation of the PSO. Update the fitness, personal best positions, and the global best position.  
Step 3. If the number of iterations is not larger than 100, then return to Step 2; otherwise, jump to Step 4.  
Step 4. The global best position is the result of the global welding path planning.  
Based on the above steps, the shortest collision-free welding path can be achieved as follows: 15\(\rightarrow\)13\(\rightarrow\)14\(\rightarrow\)7\(\rightarrow\)8\(\rightarrow\)9\(\rightarrow\)10\(\rightarrow\)11\(\rightarrow\)12\(\rightarrow\)3\(\rightarrow\)6\(\rightarrow\)2\(\rightarrow\)5\(\rightarrow\)1\(\rightarrow\)4. The global path length is 599.720 mm, the final global welding collision-free robot path planning is shown in Figure 10, and the iterative process of PSO is shown in Figure 11.
# Research on the Decoupling of the Parallel Vehicle Tilting and Steering Mechanism
## 5 Conclusions  
1. This paper proposes a tilting and steering parallel mechanism. The proposed parallel mechanism consists of a spatial steering mechanism and a tilting mechanism. The parallel mechanism can be used as the suspension for ATV.
2. This study establishes a mathematical model that may be used to analyze the DCPM. The model is infinitely rigid, ignoring the effects of vehicle dynamics. This model is a kinematic model. A decoupling method has been proposed that is based on the model.
3. The effectiveness of the decoupling method has been proven by an experiment on a prototype. The low-speed (25 km/h) experiments verified the kinematics of the proposed parallel mechanism. The prototype can reduce the influence of vehicle tilting on the steering angle of the outer and inner wheels by up to 0.64% and 0.78%, respectively. The SGCR of the prototype is between 1.198 and 0.961.
4. The proposed parallel mechanism that is based on the proposed decoupling method simultaneously achieves the following purposes: a) ignoring the effects of the nonlinear change of the tires, (b) decoupling the tilting and steering parallel mechanism of the vehicle, and (c) making the wheels on both sides of the vehicle satisfy the ASC.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods
### Labeling  
The voice of each subject was captured during the EEG recording. The audio recording was used to identify and time aligns labels. The audio recordings were downsampled to match the sampling frequency of the EEG record and the individual speech and non-speech state samples were marked as 1 or 0.
## 4 Theoretical Analysis
### Computational Complexity
#### 4.1.2 Complexity of the Dissemination Algorithm  
The node receiving an HCI packet may perform the following tasks:  
1. Checking whether the node has already received an HCI packet.
2. Constructing the core polygon of the hole contained in the HCI packet.
3. Checking whether the node stays inside the vicinity region of the hole contained in the HCI packet.  
To conduct the first task, the node needs to investigate all the core polygons and the holes stored in its memory. For each of them, the node identifies whether it is the same as the one contained in the HCI packet. The computational complexity for this task is \(O(h\times v)\) (1).  
In the second task, the node performs a loop over the vertices of the hole contained in the HCI packet. For each vertex, the node checks whether it is a core node (i.e., a node staying on the edges of the core polygon candidates). This job causes a computational complexity of \(O(v\times n\times k_{n})\). Then, the node determines the vertices of all the core polygon candidates which are the intersection points of all lines going through the core nodes and making specific angles with the \(x\)-axis. This job requires a complexity of \(O(n\times k_{n})\). Finally, the node computes the area of each core polygon candidate and chooses the core polygon candidate with the smallest area to be the core polygon. The computational complexity of this job is \(O(n\times k_{n})\). In consequence, the total complexity of the second task is \(O(v\times n\times k_{n})\) (2).  
In the third task, the node just needs to compute the distances to the core polygon stored in the HCI packet and compare them with the threshold value. Thus, the computational complexity of this task is \(O(n)\) (3).  
From (1), (2) and (3), it is deduced that the computational complexity of the dissemination algorithm is \(O(v\times(h+nk_{n}))\).
## 5 Experimental Results
Unlike our proposed algorithm, these studies employ heuristic approaches and therefore rely on repeated runs to obtain the optimal result. However, since our proposed approach is not heuristic, it produces the same result in each run. Table 11 compares these three algorithms using metrics such as the number of \(\text{D}_{\text{min}}\), the number of runs (NR), and execution time (ET). The results show that, like other approaches, our proposed algorithm achieves values close to the optimum for small graphs. For large graphs, it demonstrates superior performance compared to the others. Moreover, in terms of execution time, our algorithm is significantly faster than the others. Due to the heuristic nature of the other approaches (the heuristic algorithms are run more than once and the best result is used, or the average of all the results is used as the results), they encountered issues with optimal reduction in large datasets due to time constraints in repeated runs. However, the dynamic nature of our proposed method allows for time savings and yields results close to the optimum. In our study, a comparison is made with the work presented in [36], which offers a solution to the dominating set problem based on centrality values. From the experiments conducted on the Iridium Satellite Network and the European Natural Gas Pipeline network, the dominating set cardinalities obtained in our study are 18 and 8, respectively. The running time of our algorithm for the Iridium Satellite Network is 0.083 s, and for the European Natural Gas Pipeline network, it is 0.071 s. These results are optimal for both networks. In contrast, the method applied in [36] produced dominating set cardinalities of 19 for both datasets, which are not optimal. As discussed in Section 4.2, the second Malarya centrality value, which is calculated based on the centrality of neighboring nodes, is more closely related to the dominating set problem. All experimental results in this study demonstrate that the proposed method is highly effective and applicable for solving the dominating set problem.
## 2 Materials and Methods
### Determination of Hydrolytic Enzymes  
Proteolytic, amylolytic, phosphatase solubilising, lipolytic, pectinolytic, hemicullolytic, cellulolytic, and ammonifying activity were determined. The method used was based on the colorimetric changes. Petri dishes containing Potato Dextrose Agar (PDA) for fungi and TSA for bacteria and the corresponding substrate were used. Ammonifying activity was determined in 9 mL liquid medium using Potato Dextrose Broth (PDB) and Tryptone Soya Broth (TSB) for fungi and bacteria, respectively. The inoculums of fungi were small pieces of mycelium of \(0.5\times 0.5\) mm, taken from PDA Petri dishes, incubated approximately 96 h at 28 \({}^{\circ}\)C. The inoculums of bacteria consisted of 30 \(\mu\)L of overnighted culture at 30 \({}^{\circ}\)C.  
Briefly, the substrates used by each enzyme were 1% of sodium caseinate (ACROS Organics, Madrid, Spain) for proteolytic activity [13]; 1% of starch (Scharlab SI, Barcelona, Spain) for amylolytic activity [14]; 2.5% of phosphate tricalcium (PanReac Quimica S.L.U, Barcelona, Spain) for soluble phosphate solubilizing activity [15]; 1% of tributiryn (Merck) for lipolytic activity [16]; 0.5% of pectin (Sigma-Aldrich, Madrid, Spain) for pectinolytic activity [17]; 0.5% of xylan for hemicellulose activity [18]; 0.5% of microcrystalline cellulose and 0.005% of nigrosin for cellulolytic activity [19], and 0.02% of asparagine (Sigma-Aldrich) for ammonifying activity [20]. Incubation temperatures were 28 \({}^{\circ}\)C for fungi and 30 \({}^{\circ}\)C for bacteria. Incubation time varied depending on the enzymatic activity analysed. Thus, for proteolytic, amylolytic, and phosphate enzyme activity, the microorganisms were incubated 3 days; for lipolytic, pectinolytic, and hemicellulose activity 5 days; for cellulolytic activity incubation 7 to 10 days; and for ammonifying activity 15 days. Each determination was performed in triplicate. Positive results were considered after the appearance of a clear halo around the colony growth after the incubation time. In the case of amylolytic activity, it was necessary to add 1-2 mL of iodine solution to decipher a clear halo around the colony. The results of pectinolytic activity were determined by the addition of 2 mL of 0.05% red ruthenium dye solution. This was incubated for 1 h at room temperature and washed with distilled water. A clear halo around the colony was considered to be a positive result. In the case of ammonifying activity, the addition of 1 mL Ne\(\beta\)ler reagent (Sigma-Aldrich) and observing the formation of orange precipitate was necessary. More details are described in Table S1 of the Supplementary Material.