esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Results and Discussions
The basic idea behind GRL is to force the network to learn feature representations that do not contain domain-specific information by reversing the direction of gradients. Model 3 employed only the first network branch of the proposed method. Model 4 represents the complete model of the proposed method, which constructs auxiliary tasks based on pivot words to extract domain-independent features and integrates cross-lingual shared features and independent features in an end-to-end training framework to achieve unsupervised detection of offensive speech in the target language. From the results, Model 1 performs the worst, indicating that the basic mBERT model struggles with effective cross-domain knowledge transfer. Model 2, by reversing gradient directions, forces the network to learn domain-invariant feature representations, enabling the model to learn more generalized feature representations in domain adaptation and thereby reducing domain discrepancies to some extent, resulting in improved performance. Model 3 achieves suboptimal performance through adversarial minimization of Sinkhorn distance, suggesting that methods based on Sinkhorn distance are more effective than those based on GRL for reducing domain discrepancies. Finally, the complete proposed model (Model 4) achieves the best performance, validating that the proposed method effectively aids in learning more universal and general offensive text features by constructing auxiliary OLD tasks, demonstrating its effectiveness in zero-shot cross-lingual OLD tasks. Figure 6: \(F_{1}\) results for OLD task on the Malay dataset with varying amounts of source language training data. Figure 7: Results of the ablation experiments.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Constraints
#### 3.2.2 Active Power Loss Constraints  
The active power loss constraint is as shown in (28).  
\[P_{\text{loss},j}=\sum\limits_{ij\in L}R_{ij}\frac{P_{ij}^{t2}+Q_{ij}^{t2}}{U _{i}^{t2}} \tag{28}\]  
where \(U_{i}^{t}\) is the voltage at bus \(i\) at time \(t\); and \(P_{ij}^{t}\) and \(Q_{ij}^{t}\) are the active and reactive power flows on branch \(ij\) at time \(t\), respectively.
## 2 Study Area and Datasets
### Datasets
#### 2.2.4 Meteorological Precipitation Products  
We adapt reconstructed monthly precipitation datasets from January 2018 to August 2022 provided by National Oceanic and Atmospheric Administration (NOAA) for the estimation of precipitation over the CONUS. The datasets are provided in the format of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grids at a global scale and are derived from a network of global gauge stations  
Figure 5: Original and corrected GNSS vertical displacement time series of six selected stations (i.e., CLRS, P245, P308, P322, P431, and YBHB). The black and red curves represent the original and corrected GNSS vertical displacement time series, respectively.  
by employing advanced interpolation techniques to fill in the missing data [53]. The daily datasets are available in the format of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grids over the CONUS and are constructed using an interpolation algorithm based on gauge observations collected from multiple sources [54; 55].
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 1 Introduction  
Hypersonic travel in recent decades has been an important area of research interest in the aerospace sector due to its potential in military and civil applications. In particular, hypersonic air-breathing propulsion vehicles can offer a replacement for the conventional subsonic modes of commercial transport with a new class of flight vehicles that can offer time-saving means of transportation for long-range flights.  
Historically, the approach to hypersonic flight research has spanned several decades with constant improvements in performance through improvements in aerodynamics, structures, and propulsion systems leading to successively higher flight speeds. For flight above Mach 3, the main representative was the Lockheed SR-71, which utilised Turbine-Based Combined Cycle (TBCC) engines to propel the aircraft. Powered by two Pratt and Whitney J-58 engines, the aircraft was capable of achieving Mach 3.5 flight speeds, making it the fastest air-breathing aircraft to take off under its own power [1].  
Perhaps the most noteworthy aeronautical research effort in history is the rocket-powered hypersonic research aircraft X-15 that holds the record for the fastest manned aircraft achieving a speedof Mach 6.72. The structure of the aircraft was influenced by intercontinental missile development research being conducted at the time, and it was capable of reaching an altitude of over 350,000 feet, demonstrating that the pilots can fly rocket-powered vehicles from the Earth's atmosphere to the edge of outer space and returning with valuable data utilised in the design of succeeding aircrafts and spacecrafts [2]. Since the termination of the X-15 project in 1968, studies of hypersonic flight have been largely limited to missiles and research aircrafts owing to the complications arising from structural requirements, unsatisfactory fuel economy, and recurrent maintenance issues as well as specific propulsion system challenges of air-breathing engines in these flight regimes.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Experimental Results
#### 3.3.2 Ablation Experiments  
To validate the effectiveness of each module, we conduct a series of ablation experiments. The baseline U-Net adopts an encoder-decoder architecture comprising four downsampling and four upsampling operations. The feature maps at the 1/8 and 1/16 scales capture only local features through 3 \(\times\) 3 convolution. Additional comparisons are made between DMLKA and common alternatives such as the squeeze-and-excitation network (SE), convolutional block attention module (CBAM), and atrous spatial pyramid pooling (ASPP) [38; 39; 40]. The experimental results are summarized in Tables 4 and 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **OA** & **P** & **R** & **F1** & **IoU** \\ \hline Unet & 94.5 & 88.76 & 79.02 & 82.99 & 72.19 \\ Deeplabv3+ & 95.97 & 89.5 & 81.24 & 84.56 & 74.53 \\ TransUNet & 94.6 & 89.36 & 82.67 & 85.88 & 75.26 \\ SwinUnet & 93.53 & 86.35 & 80.09 & 83.1 & 71.09 \\ MU-Net & 94.79 & 87.88 & 85.56 & 86.7 & 76.52 \\ QTU-Net & 96.07 & 89.13 & 82.83 & 85.3 & 75.46 \\ DMLU-Net & **96.42** & **89.59** & **85.31** & **86.86** & **77.44** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results based on the LoveDA dataset.  
Figure 8: Computational efficiency and model parameters of various models.  
Figure 7: Partial extraction results of each model on the LoveDA dataset.  
As shown in Table 4, removing the DMLKA module leads to a 2.6% decrease in the IoU, indicating that the DMLKA module plays a crucial role in capturing global contextual information. Further removal of the SSAM results in an additional 1.92% decrease in the IoU, demonstrating its effectiveness in suppressing background noise and non-water features, thereby enhancing water body extraction accuracy. When DySample is removed, the IoU drops by another 0.85%, suggesting that this module contributes to edge refinement and improves the accuracy of water body segmentation. Additionally, replacing MLKA with DMLKA reveals that the incorporated dynamic weight generator significantly enhances the model's IoU. As shown in Table 5, when the DMLKA module is replaced with the SE, CBAM, and ASSP module, the performance of the model declines. This further proves the advanced nature of the proposed model.  
In summary, although the ablation results do not show substantial changes in terms of overall accuracy (OA) and precision (P), the proposed DMLU-Net achieves the best performance in the IoU--the most critical metric for target detection--as well as in recall (R) and the F1-score, which confirms the effectiveness of each component module.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 2 Gesture-Based Human-Robot Interface
### Leap Motion Based Gesture Recognition
#### 2.1.1 Feature Selection  
In order to reduce the influence of the variance of hand sizes and shapes among different people, in this paper the angular features are used. As shown in Figure 1, the bone/joint position \(A_{i}=(x_{A_{i}},y_{A_{i}},z_{A_{i}}),B_{i}=(x_{B_{i}},y_{B_{i}},z_{B_{i}}),C_{ i}=(x_{C_{i}},y_{C_{i}},z_{C_{i}}),i=1,...,5\) can be provided by the leap motion sensor.  
According to laws of cosine, the bending angle \(\theta_{i}\) is  
\[\theta_{i}=\arccos\left[\left(\left|\overrightarrow{A_{i}B_{i}}\right|^{2}+ \left|\overrightarrow{B_{i}C_{i}}\right|^{2}-\left|\overrightarrow{A_{i}C_{i} }\right|^{2}\right)\right/\left(2\left|\overrightarrow{A_{i}B_{i}}\right| \left|\overrightarrow{B_{i}C_{i}}\right|\right)\right].\]  
After conversion, five features \(\left[\begin{array}{cccc}\theta_{1}&\theta_{2}&\theta_{3}&\theta_{4}&\theta_ {5}\end{array}\right]^{T}\) are obtained. These features describe the bending angle of each finger, which are enough to distinguish the simple gestures. For more complex gestures, it is better to incorporate more features such as other joint angles and angles between fingers. In this paper, for simplicity, only the bending angles are used.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Main Error
#### 2.1.3 Receiver Errors  
Receiver errors are primarily caused by equipment errors and tracking point errors.  
Receiving Equipment Errors: These errors involve time delays between the antenna input and receiver output, including delays in the antenna, cables, and receiver channels. These errors can be corrected through receiver time-delay calibration, as discussed in Section 2.2.  
Tracking Point Errors: The receiver tracks the signal at the end of the third cycle rather than at the beginning, necessitating a period correction of 30 \(\upmu\)s. Theoretically, this correction ensures that there are no tracking-point errors in the receiver's output signal.
# An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
## 5 Bidirectional Secret-Key Agreement over AWGN Channel
### SKA from [PERSON] to [PERSON]  
**(A3. 1)** Let  
\[T_{2i-1}=V_{2i-2}. \tag{40}\]  
Then, [PERSON] sends the raw key \(M_{ab}^{i}\) to [PERSON] by  
\[U_{2i-1}=T_{2i-1}+M_{ab}^{i}, \tag{41}\]  
where \((U_{1},U_{2},\cdots,U_{2l})\in(+1,-1)\)  
**(A3. 2)** [PERSON] and [PERSON] can receive the noisy version of \(U_{2i-1}\) as  
\[V_{2i-1}=U_{2i-1}+n_{ab}^{i}=M_{ab}^{i}+U_{2i-2}+n_{ba}^{i-1}+n_{ ab}^{i}, \tag{42}\] \[W_{2i-1}=U_{2i-1}+n_{ae}^{i}=M_{ab}^{i}+U_{2i-2}+n_{ba}^{i-1}+n_{ ae}^{i}\,, \tag{43}\]  
where the variance of AWGN over the main channel and the eavesdropper's channel are \(\sigma_{m}^{2}\) and \(\sigma_{w}^{2}\), respectively.  
**(A3. 3)** [PERSON] and [PERSON] can obtain the noisy version of \(M_{ab}^{i}\) by minusing \(U_{2i-2}\) and \(W_{2i-2}\) to \(V_{2i-1}\) and \(W_{2i-1}\), respectively. The result will be  
\[\bar{M}_{ab}^{i}=V_{2i-1}+U_{2i-2}=M_{ab}^{i}+n_{ba}^{i-1}+n_{ab}^{i} \tag{44}\]  
and  
\[Z_{ae}^{i}=W_{2i-1}+W_{2i-2}=M_{ab}^{i}+n_{ba}^{i-1}+n_{be}^{i-1}+n_{ae}^{i}\,, \tag{45}\]  
where the variance of AWGN \(n_{be}\) or \(n_{ae}\) is \(\sigma_{w}^{2}\).  
Figure 7: The \(i\)-th round of bidirectional secret-key agreement over AWGN channel.  
### SKA from [PERSON] to [PERSON]  
**(B3. 1)** In the \(i\)th round, [PERSON] has to make  
\[T_{2i}=V_{2i-1}. \tag{46}\]  
Then, he sends the raw key \(M_{ba}^{i}\) in the noisy form, which is  
\[U_{2i}=T_{2i}+M_{ba}^{i} \tag{47}\]  
over the main channel.  
**(B3. 2)** [PERSON] can receive \(V_{2i}\), consisting of the noisy vector \(E_{ba}^{i}\) and \(U_{2i}\), and [PERSON] can receive \(W_{2i}\), which consists of the noisy vector \(E_{be}^{i}\) and \(U_{2i}\),  
\[V_{2i}= U_{2i}+n_{ba}^{i}=M_{ba}^{i}+U_{2i-1}+n_{ab}^{i}+n_{ba}^{i}, \tag{48}\] \[W_{2i}= U_{2i}+n_{be}^{i}=M_{ba}^{i}+U_{2i-1}+n_{ab}^{i}+n_{be}^{i}. \tag{49}\]  
**(B3. 3)** [PERSON] and [PERSON] can obtain the noisy version of \(M_{ba}^{i}\) by minusing \(U_{2i-1}\) and \(W_{2i-1}\) to (48) and (49). \(\bar{M}_{ba}^{i}\) and \(Z_{be}^{i}\) can be received by  
\[\bar{M}_{ba}^{i}=V_{2i}+U_{2i-1}=M_{ba}^{i}+n_{ab}^{i}+n_{ba}^{i}, \tag{50}\]  
and  
\[Z_{be}^{i}=W_{2i}+W_{2i-1}=M_{ba}^{i}+n_{ab}^{i}+n_{ae}^{i}+n_{be}^{i}. \tag{51}\]  
Now, let us calculate the capacity of the main channel and the eavesdropper's channel by \(C_{m}\) and \(C_{w}\). Because the variance of the main channel and the eavesdropper's channel are \(\sigma_{m}\) and \(\sigma_{w}\), we can calculate  
\[C_{m}=\frac{1}{2}\log_{2}(1+\frac{P}{\sigma_{m}^{2}+\sigma_{m}^{2}})=\frac{1} {2}\log_{2}(1+\frac{P}{2\sigma_{m}^{2}}), \tag{52}\]  
and  
\[C_{w}=\frac{1}{2}\log_{2}(1+\frac{P}{\sigma_{m}^{2}+\sigma_{w}^{2}+\sigma_{w} ^{2}})=\frac{1}{2}\log_{2}(1+\frac{P}{2\sigma_{m}^{2}+\sigma_{w}^{2}}). \tag{53}\]  
We can also receive the secret-key capacity \(C_{s}\) over it.  
\[C_{s}= C_{m}-C_{w}=\frac{1}{2}[\log_{2}(1+\frac{P}{2\sigma_{m}^{2}})-\log_{2} (1+\frac{P}{2\sigma_{m}^{2}+\sigma_{w}^{2}})]. \tag{54}\]
## 5 Experiments and Analysis of Results  
In this paper, by combining AES encryption and the TLS protocol to ensure multi-threaded control of the C/S architecture it ensured the security of data in the transmission layer and application layer. On this basis, through the precomputation of AES and multi-threaded I/O asynchrony it reduced the time overheads in the two processes of data computation and data communication, respectively, compared to the traditional AES and multi-threaded combination of the experiments in the literature [6]. After that, the effectiveness of this paper in improving the computation speed was concluded through the data transmission delay, acceleration ratio, and throughput. Finally, by analyzing the data, we illustrate the advantages of this paper such as strong security and excellent computational performance. The experimental environment of this paper is as follows: software platform: Python3.8.10. hardware platform: processor 16-core AMD Ryzen7-6800H.
# Development of a New Evaporation Exposure Model: Chemical Product Evaporation Model (CPEM)
## 2 Theory
### Model Simplification  
#### 2.2.1 Constant Substance Concentration in a Product  
If \(C_{prod}\) is constant, the CPEM can be simplified as follows:  
\[\frac{dC_{air}}{dt}=\frac{KSM}{VRT}\frac{P_{vap}C_{prod}}{C_{prod}+C_{r}\frac {M}{M_{r}}}-\left(\frac{KS}{V}+q\right)C_{air}, \tag{9}\]2.2.2. Unsaturation and Constant product Mass: The ConsExpo Evaporation Model without Saturation  
If it is assumed that there are no saturation kinetics and that the product mass is constant, the CPEM is simplified to the following coupled differential equation:  
\[\frac{dC_{air}}{dt}=\frac{KSM}{VRT}\frac{P_{cap}}{C_{prod}+C_{r}\frac{M}{M_{r}}}C _{prod}-qC_{air}, \tag{10}\]  
\[\frac{dC_{prod}}{dt}=-\frac{KSM}{A_{prod}RT}\frac{P_{cap}}{C_{prod}+C_{r}\frac{ M}{M_{r}}}C_{prod}, \tag{11}\]  
2.3. Saturation and Constant Product Mass: The ConsExpo Evaporation Model or CPEM without Product Mass Change  
If it is assumed that the product mass is constant, the CPEM can be simplified to the coupled differential Equations (12) and (13) as follows:  
\[\frac{dC_{air}}{dt}=\frac{KSM}{VRT}\frac{P_{cap}}{C_{prod}+C_{r}\frac{M}{M_{r}} }C_{prod}-\left(\frac{KS}{V}+q\right)C_{air}, \tag{12}\]  
\[\frac{dC_{prod}}{dt}=-\frac{KSM}{A_{prod}RT}\left(\frac{P_{cap}}{C_{prod}+C_{r} \frac{M}{M_{r}}}C_{prod}-\frac{RT}{M}C_{air}\right), \tag{13}\]  
This coupled differential equation is equal to the ConsExpo evaporation model [3]. Because \(P_{air}\) is less than \(P_{eq}\) in Equation (13), \(C_{prod}\) always decreases, and steady state air concentration does not exist.
## 4 Prediction of Radiated Sound Power based on the DRSMs
### Prediction of Low-Frequency Sound Radiation  
According to the conclusions mentioned in Section 3.4, the modal participant coefficient of the DRSM (0,1) is very small, and many other structural modes have a major contribution to the vibration of the structure. To use the solving equations method to calculate the modal participant coefficients in this case, the mode shapes of too many modes need to be known, and the displacement equations will involve too many modes. Moreover, for numerical calculation methods such as FEM, the mode shapes of higher-order modes are difficult to calculate accurately. Therefore, the abovementioned orthogonal decomposition method can be used to obtain the modal participant coefficient of the DRSM (0,1); this method only needs to know the modal displacement function of this mode.  
Figure 5: Comparison between the total displacement of a midpoint on the cylindrical shell and the modal displacements of different order modes.  
Figure 6: Modal participant coefficients of different order modes.  
For the new prediction, the orthogonal decomposition method (ODM) is used to acquire the participant coefficient of the DRSM (0,1) according to Equation (2). Then, the number of measuring points required for acquiring the precise participant coefficient of the DRSM (0,1) is analyzed. This method includes integration on the surface of the cylindrical shell, which is why the measuring points should be uniformly selected on the surface. Three schemes of measuring points, namely, \(80\times 64\) (80 points are evenly distributed on the circumference with the same axial coordinate; 64 points are evenly distributed on the generatrix with the same circumferential coordinate), \(40\times 22\), and \(10\times 22\), are respectively used to calculate the modal participant coefficient and predict the radiated sound power of the cylindrical shell in the low frequency band. The prediction results are compared with the total radiated sound power calculated by the FEM, as shown in Figure 7. The screenshot in the upper-left corner of Figure 7 shows that the former two schemes have better prediction effects under 100 Hz, while the third scheme (\(10\times 22\)) has slightly worse prediction results, whose prediction error is less than 3 dB in the frequency band from 50 Hz to 100 Hz. With the vibration displacement data of these \(10\times 22\) measuring points used as boundary conditions, the BEM was used to predict the radiated sound power, as shown in Figure 7. The prediction error of the BEM is almost 8 dB, which means that the prediction method based on the DRSMs can effectively reduce the number of measuring points.
## 3 Results and Discussion
### Determination of the Model Parameters
#### 3.1.2 Mass-Transfer Coefficients  
The rigorous transport model is needed to numerically simulate the PSA process for the separation of biogas containing CH\({}_{4}\) and CO\({}_{2}\). The mass transfer coefficient and the equilibrium adsorption model are the major parameters for verifying the modelling results with experimental data. These parameters were regressed starting from the experimental concentration as a function of time at 308 K, as provided by [PERSON] et al. [8]. In this case, \(c_{0}=P_{0}/RT_{0}\) (\(P_{0}\) = 1 atm, \(T_{0}\) = 308 K). The linear driving force model can be derived into terms of concentration via the following equation:  
\[c_{i}=c_{0}-\varepsilon\cdot q_{i} \tag{5}\]  
It is possible to derive \(q_{i}\) as  
\[q_{i}^{*}-q_{i}=\frac{c_{0}-c_{i}(t)}{\varepsilon} \tag{6}\]  
Figure 3: Regression of the CH\({}_{4}\) (**a**) and CO\({}_{2}\) (**b**) adsorption isotherm from experimental data at various temperatures with MATLAB (Langmuir 3 isotherm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Descriptions** & **IP\({}_{1}\) (\(\times\) 10\({}^{-6}\))** & **IP\({}_{2}\)** & **IP\({}_{3}\)** & **IP\({}_{4}\)** \\ \hline CH\({}_{4}\) & 2.876 & 1918 & 28.77 & \(-\)1152 \\ CO\({}_{2}\) & 12.74 & 1839 & 0.2486 & 626.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Langmuir isotherm parameters.  
By replacing Equation (6) in Equation (3)  
\[\frac{\partial c_{i}(t)}{\partial t}=\text{K}_{i}(c_{0}-c_{i}(t)) \tag{7}\]  
It is possible to derive the equation to solve the K\({}_{i}\) of methane and carbon dioxide from the available data. The result of the regression is shown in Figure 4.  
According to the parametric analysis results, it is found that the modelling results are fitted very well with the experimental data, and the mass transfer coefficient of CO\({}_{2}\) is 0.027 s\({}^{-1}\), which is larger than that for CH\({}_{4}\) of 0.0008 s\({}^{-1}\). It can be seen that there is a significant difference between these two kinetic parameters. The reason is that when the biogas is injected in the adsorption bed, most of the CH\({}_{4}\) is released through the outlet without adsorption. This is due to the low selectivity of the adsorbent, which is related to a very low mass transfer coefficient. However, in the case of CO\({}_{2}\), the large amount of CO\({}_{2}\) was adsorbed due to fast adsorption kinetics.
## 2 Materials and Methods
### Compilation of Reference Reactions from the Literature  
A comprehensive literature review recovered genetic modifications to improve the production of endogenous metabolites in _E. coli_ using glucose as the sole carbon source. The selected genes encode enzymes with known enzymatic reactions. Transcription factors or other genes associated with regulation were excluded from consideration because they are not annotated in the GEMs. Some reactions were not considered because they only make sense in a regulatory context. For instance, the overexpression of _thrA_ is required to restore the growth of _E. coli_ when the overproduction of L-serine as a tryptophan precursor strongly represses the biosynthesis of the essential amino acid L-threonine [9].  
Additionally, modifications requiring specialized culture medium formulations were excluded, such as the _citT_ gene, which requires the incorporation of citrate from the environment for tryptophan production [6]. Genes like _tryrA_ and _pheA_, which result in mutant auxotrophic strains for L-tyrosine and L-phenylalanine during tryptophan production, were annotated as targets for a knockdown. Genes with eliminated feedback inhibition were identified as potential candidates for overexpression.  
The modifications annotated encompassed overexpression, silencing, or knockdown of specific genes, with an additional nine reactions rendering low flux used as a reference to assess the impact of silencing techniques on metabolite production or its absence of effect [61]. Notably, no modifications affecting biomass were found as these alterations do not directly influence the targeted metabolites' production. To train the model using enzymatic reactions with low effects and those related to biomass production, we selected reactions exhibiting similar behavior, as described in Section 2.10, upon introducing perturbations.  
The literature review also excluded instances involving heterologous pathways, except in cases where these pathways altered the reversibility of a pre-existing reaction of the metabolic model, necessitating independent simulations.  
Annotations were made at the enzymatic reaction level, considering only those reactions associated with each gene in the publication. This consideration was followed when multiple reactions were annotated in the metabolic model. The complete list of genetic modifications is accessible in Supplementary Table S2[4; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70].
## 5 Test Systems and Simulation Cases
### IEEE 33-Bus System  
This test system operates at 12.66 kV and consists of 33 nodes, 32 branches, and a unique slack node located at node 1. The configuration of this feeder is depicted in Figure 3a, and its parameters can be found in [3]. Its initial active power losses are equal to 210.9876 kW. The total active and reactive power demands of this system are 3715 kW and 2300 kVA, respectively. The voltage and power base values considered are 12.66 kV and 1 MW, respectively.