esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Impact of the Control Strategies in the Reciprocal Behavior between MVG and LVGs
### Impact of Different Control Strategies on the Behavior of MVG
#### 5.2.1 Influence of the DTR Connection Point on Q\({}^{\rm ex}\)
In summary, regardless of the connection point of the DTRs at the MV feeder and the daily load consumption and PV production profiles, the use of a concentrated local control strategy in LVGs causes less \(Q^{ex}\) than the distributed strategies. The effect of the \(Q^{ex}\) provoked by the various control strategies on the voltage profile of the MV feeder is analyzed below. .2.2 Influence of the Concentrated versus Distributed Control Strategy on the Voltage Profile in MVG  
The influence of the concentrated versus distributed control strategy on the voltage profile in MVG is analyzed by means of power flow simulations at \(t_{\text{crit}}\). The STR's high-voltage bus is selected as a slack bus with a voltage of 1.01 p.u. Figure 24 depicts the voltage profiles of the MV feeder and of the second and 32 nd LVGs for different control strategies at \(t_{\text{crit}}=\) 12:12. Figure 24a-c show the voltage profiles for the distributed \(cos\varphi(P)\) and \(Q(U)\) controls, and concentrated \(L(U)\), respectively. As expected, the concentrated \(L(U)\) control strategy provokes the smallest reactive power exchange through the STR, \(Q^{ex}_{STR}\), as shown in Table 3. Therefore, the corresponding voltage profiles are less suppressed than in the both cases of the distributed control strategy, where the voltage profiles are pushed down more than is needed. In the DTR level, the global effect (GE) provoked by \(Q^{ex}\) is present in all of the cases. GE is the voltage displacement on the transformer buses provoked by the reactive power injection in radial structures [26]. The \(Q^{ex}_{STR}\), OLTC position change, and the GE for different control strategies used in real LVGs are summarized in Table 3. In the case of the concentrated strategy, GE is smaller than in the distributed control strategies. The distributed \(cos\varphi(P)\) has the worst performance. It provokes not only the largest \(Q^{ex}_{STR}\) and GEs, but it also causes an OLTC position change from 13 to 15. Figure 23: Daily \(Q^{ex}\) for different control strategies and various LVGs connected at the MV feeder: (**a**) urban, connected at the feeder beginning; (**b**) urban, connected at the feeder end; (**c**) rural, connected at the beginning; (**d**) rural connected at the end; (**e**) overview of the MVG. In summary, the effects of the concentrated local control strategy in MVG behavior are more favorable than those of the distributed strategies.
## 2 Materials and Methods
### Materials
#### 2.3.3 Scenarios  
Two scenarios were set to validate the simulator. One scenario was created for verification and was run by adjusting the AOI, GSD, and overlap. The AOI for simulation and performance validation is the Geomexsoft building which is one of the testbeds in Figure 9a,b. During the flight, the AOI was easily discernible. GSD was chosen with the surrounding environment in mind, and the overlap was optimized for aerial photogrammetry for 3D modeling [11; 23; 24]. Table 5 lists the specified GSDs and overlaps.  
Figure 9: (**a**) Appearance and (**b**) boundary of the Geomexsoft office building. (**c**) Appearance and (**d**) boundary of the Research Institute for Gangwon building.  
The second scenario was created to validate shaded area detection, comparing real flight and virtual flight regardless of the simulator's own path planning. In this scenario, the flight path was automatically created by Pix4D using AOI, which is the Research Institute for the Gangwon building (Figure 9c,d). The simulator imported the flight path from Pix4D and detected shaded areas according to the path and flight environments.
## 4 Case Study

Table 3: Operating conditions implemented during the Testing phase. Figure 10: Novelty detection for the abrupt fault detection. Figure 9: Novelty detection for the condition recognition. Table 5 shows results for the fault detection task. The system breakdown occurred after 1548 s (25.8 min) from the fourth operating condition starting. The novelty detection algorithm implemented for the impulse factor (extracted from the \(y\)-axis of the first accelerometer) creates two clusters. The first one is created after 168 s (2.8 min) after the fourth setting change (yellow dots in Figure 12), while the second cluster is created after 1455 s (24.25 min), which correspond to 90 s (1.5 min) before the system breakdown. If an alarm was triggered at the second cluster creation time (named Fault (1) in Table 5), the system breakdown could have been avoided; cluster 2 could have been labeled as faulty, and classification or regression models could have been trained for future applications. Besides the performance of the condition recognition and abrupt fault detection, there is an advantage from the data quantity point of view. Indeed, according to the proposed architecture, raw signals are stored and transmitted to the cloud only when a new condition (both operating and fault) is detected. Otherwise, only the extracted feature matrix, together with the label, is stored. Table 6 shows results in terms of gigabytes saved. The weight of raw signals is 18.19 Gb. When the operating condition is known, only the features matrix is stored. Thus, only 5.73 Gb are required. In total, a saving of 68.5% of the storage space is obtained. Note that the weight is referred to as the MATLAB variables (extension.mat). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Setting** & **Setting** & **Latency (Sec)** \\  & **Change—Real (Time** & **Change—Detected** & **Latency (Sec)** \\  & **Instants)** & **(Time Instants)** & **(Time Instants)** \\ \hline Setting 1 & - & - & \\ Setting 2 & 4.969 & 4.984 & 15 \\ Setting 3 & 16.022 & 16.032 & 10 \\ Setting 4 & 20.668 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the streaming OCR. Figure 11: Results of Novelty Detection for the condition recognition. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Fault—Real (Time** & **Detected Anomaly** & **Latency (Sec)** \\  & **Instant)** & **(Time Instant)** & **Latency (Sec)** \\ \hline Normal & - & - & - \\ Fault (1) & - & 20.838 & - \\ System Breakdown & 22.218 & 22.123 & -90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of fault detection. In addition, as the algorithm works on streaming data at the edge, the latency of data transmission is not to be taken into account.
## 5 Experiments and Results
### Implementation Details
#### 5.3.3 Time Consumption  
The accuracy and real-time requirements of trajectory prediction are very high. We compare the proposed TPASTGCN model with the RNN-based V-LSTM model and GRU model for data prediction, and the time consumption is compared, as shown in Table 2, which shows the average time consumption for predicting each piece of data.Although the CV baseline model is computationally efficient, it can only predict the vehicle trajectory for a short period of time, and the vehicle trajectory prediction for a long period of time is extremely poor. The proposed model can achieve a faster training process and easier convergence.
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 2 Drill Floor Design and Risk Assessment for Gas Explosion
### Gas Explosion Risk Assessment and Management  
Figure 3 shows the general drill floor design against blast load and the proposed methodology for blast load application. Explosion scenarios are set up based on wind and gas properties, blowout characteristics, and ignition locations. Consequence analysis includes gas dispersion analysis and explosion analysis. These analyses are carried out for each blowout and ignition scenario, and the gas volume and blast pressure time history are obtained for each scenario. FLACS program is used in this study for gas dispersion and explosion analysis. The probability of each explosion scenario is obtained from the event tree and calculations. The exceedance curve is obtained from these probabilities and blast pressures. Generally, a certain probability level, as well as its corresponding pressure, is selected as the design pressure to avoid a too conservative actual design. This study uses the consequence level of blast pressure since the comparison and investigation of each pressure application is the main focus. Moreover, this study considers the characteristics of propagation of blast load in order to reflect the real nature of gas explosion and avoid obtaining a too conservative design for the anti-blast structure. Detailed blowout scenarios and assumptions are described in Section 3.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 3 Mechanisms of Action of Antibiotics and Nanoparticles Against Bacteria
### Antibiotic-Resistant Mechanisms in Microorganisms  
Antibiotics are prescribed to treat and prevent bacterial/fungal infections. Antibiotics target certain pathways, such as cell wall synthesis, to produce a mechanism of action against microorganisms [108]. However, microorganisms can prevent the accumulation of antibiotics, which reduces and resists the antimicrobial effects. There are currently three main mechanisms of resistance: (1) mutations in the microorganism's outer membrane to allow a decrease in antibiotic uptake or an increase in efflux of antibiotic from the cells; for instance, due to the presence of an outer bacterial membrane, Gram-negative bacteria are more likely to be resistance to antibiotics as they have to penetrate through the protective barrier; (2) change in the target site where the antibiotic attaches to the microorganism (commonly due to spontaneous mutation) thus reducing the sensitivity to the antibiotic and (3) the production of inactivating enzymes [108; 109; 110; 111].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monometallic/Metal** & **Mean Size (Shape)** & **Bacteria/Fungi Tested** & **Reference** \\ \hline \hline \multirow{8}{*}{20–28.9 nm} & _E. coli_ & \multirow{2}{*}{[92]} \\  & 23 nm & _E. faecalis_ & \\  & _K. pneumoniae_ & & \\  & _E. coli_ & & \\  & 20–28.9 nm & _P. aeruginosa_ & \\  & _B. subtilis_ & & \\  & _S. aureus_ & & \\ \hline \hline \multirow{8}{*}{Zinc oxide (ZnO)} & _E. coli_ & \multirow{2}{*}{[95]} \\  & 20 nm & _S. aureus_ & \\ \cline{1-1}  & & _B. subtilis_ & \\ \cline{1-1}  & 20 nm & _E. coli_ & [100] \\ \cline{1-1}  & 20–40 nm & _E. coli_ & [101] \\ \hline \hline \multirow{2}{*}{Intermetallic Nanoparticles} & **Mean Size (nm)** & **Bacteria/Fungi Tested** & **Reference** \\ \cline{1-1}  & & & \\ \hline Silver-Gold alloy (Ag-Au) & \textless{}200 nm (spherical) & _S. aureus_ & [103] \\ Titanium-Nickel-Copper alloy (Ti-Ni-Cu) & \multirow{2}{*}{N/A} & _S. aureus_ & [105] \\ Platinum-Silver alloy (Pt-Ag) & & _E. coli_ & [106] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Not only do these resistant species pose a threat through ineffective antibiotic treatment, but the resistant species can also pass on resistance genes to other microbes. Resistant genes can be found encoded in gene cassettes located within the transposon and the chromosomes of microbes [112; 113]. Through horizontal gene transfer, these gene cassettes can be transferred to another microbe. Currently, there are three main mechanisms of horizontal gene transfer: conjugation (transfer of genetic material through direct contact between cells with resistance and receptor cells), transformation (uptake and incorporation of short resistant DNA into receptor microbe) and transduction (transfer of resistant DNA to receptor microbe via bacteriophage vector) [114].
## 4 Discussion
The spectral analysis method has potential applications in other spectrum data from photoacoustic imaging, OCT, etc. Spectrum analysis of the spectrum data from photoacoustic imaging and OCT has no successful cases. However, the proposed method provides a possibility of enhancing the classification accuracy through spectrum analysis. Though the Markov clustering method is not parallelizable in choosing clustering centers, it can provide optimal clustering centers of the wavelet coefficients at a high convergent velocity. After clustering centers are determined, WFCRMCA can cluster the larger dataset in parallel. In the WFCRMCA, even though most parameters are stable and can be used in most cases, several parameters, such as \(T_{\text{peak}}\) and \(\text{S}_{\text{cale2}}\), still need to be adjusted manually to increase clustering accuracy for specific applications. It is easy for a user to try different values to derive the best parameters for their application. Future work will continue to focus on optimizing the parameters. The proposed WFCRMCA method analyzes the spectra of one type of object under different conditions with the same features (up, down, protruding, concave) so that it can differentiate the targets. This method has the potential to integrate with other equally essential features to increase the accuracy of classification. Incorporating multiple features in clustering provides more possibilities and challenging topics for future research.
## 4 Simulation Results
### Advantages of the Proposed Approach  
The main advantage of the proposed system lies in its accurate and rapid MPP tracking which leads to enhanced solar farm output, reduced losses, and pressure on the control system. This advantage is clearly illustrated by Figures 24 and 25.  
Figure 24 illustrates the output power of a studied solar panel on the DC bus in normal mode, represented by the yellow area. If the proposed system functions correctly, the area represented in black is added to the output. In a change similar to that shown in Figure 24, the ratio of the black area to the yellow area, which can be considered an improvement in the output power, is approximately 5%. As Figure 25 shows, when the cell enters a shaded area and the output drops, the system with the proposed controller decreases the voltage more quickly, eliminating the black area represented in Figure 25b. This area can  
Figure 23: Output power of the studied series of solar panels under a change in irradiation from 1200 \(\rightarrow\) 900 \(\rightarrow\) 1200 W/m\({}^{2}\).  
Figure 24: Area under the curve of the output power of the proposed and conventional PV systems under increased irradiation.  
be interpreted as the pressure applied to the control systems and intermediate circuits to determine the appropriate output of the solar cell. This area is eliminated in the proposed system, and the pressure on the control circuits is reduced by more than 70% while reaching the MPP.  
In summary, the proposed approach enhances the efficiency of the studied solar power plant in several ways:  
* Increased power output during exits from the shadow area: As the irradiation increases, the effective output of the solar farm also rises due to the MPPT improvement.
* Reduced pressure on control circuits during the entrance into the shadow area: When the irradiation decreases or shading occurs, the proposed method reduces the loss and pressure on the control circuits.
* Minimized distortion caused by irregular panel output due to shading: The proposed approach minimizes the DC distortion resulting from irregular panel output caused by shading, ensuring a more regulated power output.
## 6 Simulations and Result Analysis  
This section conducts simulations of the proposed MOEA/D-AMSF and analyzes the experiment results. We first give the specific values of simulation parameters including the network topology and the proposed algorithm. The simulation results are then analyzed from the perspectives of algorithm convergence, solution domination, and solution diversity to verify the effectiveness of the proposed algorithm.
# PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything
## 3 Proposed Approach--PINE Protocol  
### Node Characteristics  
1. Sender node \((S)\): The sender creates a lattice-encrypted message for the receiver. It intelligently decides and forwards the encrypted message to the next available cooperative node. It generates the incentives for all the relay nodes involved in message-passing at the end of the communication cycle. The sender node combines direct and overheard information to calculate the resultant incentive of the network nodes involved in message-passing. Finally, an \(InTab\) is maintained to choose the right nodes.
2. Forwarding nodes \((F_{j})\): These nodes pass the encrypted message to the next hop. Each forwarding node intelligently identifies the next hop by considering the incentive value obtained from interpreting the \(NetInfo\), intending to minimize the communication with maliciously behaving nodes. At the end of the communication cycle, \(F_{j}\) is provided with an incentive based on the network behaviour feedback received at node \(S\), where \(j=0\) to \(R-1\).
3. Receiver node \((R)\): The receiver node listens to the channel, receives the encryption message, and decrypts it to analyze the information. After successful/unsuccessful decryption, the receiver sends an acknowledgement or negative acknowledgement (_ACK/NACK_) packet destined to the sender via the same route by which the packet reached the receiver (_known as the bi-directional route_). It should be noted that the receiver node possesses the same capability as the sender node, as the receiver can become a sender in future communication with other nodes.
4. Non-cooperative nodes \((NC_{j})\): These nodes tend to breach network operations to disrupt network stability. These nodes are malicious packet droppers and are involved in circulating direct false \(Netinfo\) with other nodes, namely, nodes \(S\) or \(F_{j}\). These nodes are penalized by node \(S\) and may or may not participate in upcoming communication cycles depending on the incentive value.
5. Cooperative nodes \((C_{j})\): Contrary to \(NC_{j}\), cooperative nodes tend to maintain network stability by not involving themselves in malicious activities such as packet-dropping and by involvement in identifying \(NC_{j}\) nodes and reporting them to the \(S\) node. Cooperative nodes are involved in communicating the overheard true information with the \(S\) node if the direct nodes to \(S\) misbehave. Cooperative nodes tend to communicate the direct true \(NetInfo\) if in contact with node \(S\).
# Study on a Quality Evaluation Method for College English Classroom Teaching
## 2 Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Methods
### Fuzzy Comprehensive Evaluation Method
#### 2.2.1 Single-Level Fuzzy Comprehensive Evaluation Model  
The fuzzy comprehensive evaluation model is composed of the index set \(U\), the evaluation set \(V\), and the judgment matrix \(R\). The steps of the single-level model of comprehensive evaluation are described as follows.  
(1) Determine the evaluation index set \[U=\{u_{1},u_{2},\cdots,u_{m}\}. \tag{3}\]  
Here, \(u_{i}(i=1,2,3,\cdots,m)\) is the evaluation index of the same level.  
(2) Determine the evaluation results set  
\[V=\{v_{1},v_{2},\cdots,v_{n}\}. \tag{4}\]  
Here, \(v_{j}(j=1,2,3,\cdots,n)\) is the evaluation result, and \(n\) is the number ofgrades. This set specifies the selection range of the evaluation results for one evaluation factor. The grades of the result set can be a qualitative value or a quantifiable value.  
(3) Determine the weight vector  
\[W=(w_{1},w_{2},\cdots,w_{n}). \tag{5}\]  
Here, \(w_{i}(i=1,2,3,\cdots,n)\) indicates the importance degree of index \(u_{i}\). It is the weight which meets \(\sum\limits_{i=1}^{n}w_{i}=1\) (\(0\leq w_{i}\leq 1\)).  
(4) Determine the membership matrix  
Assume that the evaluation index \(u_{i}\) is evaluated in order to obtain a fuzzy vector \(R_{i}=(r_{i1},r_{i2},\cdots,r_{im})\) for the relative evaluation set \(V\). \(r_{ij}\) is the degree of \(v_{j}\) for the index \(u_{i}\), and \(0\leq r_{ij}\leq 1\). If the number of the indexes(\(n\)) is evaluated, then the matrix \(R\) with \(m\) rows and \(n\) columns is obtained, which is called the membership matrix. In this matrix, each row is an evaluation result for each index, and the whole matrix contains all the information obtained by the evaluation result set \(V\) for the evaluation index set \(U\).  
(5) Fuzzy comprehensive evaluation  
The fuzzy composition of the weight vector and the membership matrix is used to obtain the fuzzy evaluation result.  
\[B=W\circ R=[w_{1},w_{2},\cdots,w_{m}]\circ\left[\begin{array}{cccc}r_{11}&r_ {12}&\cdots&r_{1n}\\ r_{21}&r_{22}&\cdots&r_{2n}\\ \cdots&\cdots&\cdots&\cdots\\ r_{m1}&r_{m2}&\cdots&r_{mn}\end{array}\right]=(b_{1},b_{2},\cdots,b_{m}) \tag{6}\]  
where \(\circ\) in the expression is the generalized fuzzy composition operator.  
\[b_{j}=\sum\limits_{i=1}^{n}w_{i}r_{ij}(j=1,2,3\cdots,m) \tag{7}\]  
Therefore, the general process of the single-level model of fuzzy comprehensive evaluation is shown in Figure 1.
## 3 Results and Discussion
### Sludge Volume Index (SVI)  
As observed in Figure 5a, the solid agglomeration flocs (sludge) were clearly visible in the container after FeSO\({}_{4}\) treatment, whereas it was difficult to visualise the similar flocs after the Fe-catalytic Fenton treatment. Instead, solid Fe (insoluble) adhered to the walls and randomly dispersed at the bottom of the container. The FeSO\({}_{4}\) treated effluent retains tint even after the vacuum filtration through 0.45 \(\upmu\)m; thereby, for a real application, it further requires treatment for colour removal. In contrast, the Fe-treated effluent did not endure the tone. As suggested by authors like [10; 13; 19], ferric ions reaction with hydrogen peroxide produces colour in the Fenton reaction (21).  
\[\text{Fe}^{3+}+\text{H}_{2}\text{O}_{2}\rightarrow\text{Fe}\;(\text{OH})_{3} \tag{28}\]  
Likewise, the considerable high SVI was obtained for FeSO\({}_{4}\). Figure 5b compares the SVI values of both treatments. Mean SVI for FeSO\({}_{4}\) and Fe(0) was \(144.3\pm 5.4\) and \(25.5\pm 1.9\), respectively. The analysis of comparison was found significant with a t-test obtaining _a_\(p\)-value = \(1.32\times 10^{-4}\) at 95% CIs. Therefore, a heterogeneous Fe(0) Fenton oxidation can be a substitute for classical homogeneous Fenton oxidation to mitigate the sludge burden.