esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Mathematical Modeling of OPF Objective Function
### Problem Formulation  
The goal of the OPF problem is to optimize the OPF equation that has selected objective function through controlling power system control parameters while adhering to several equalities and inequalities restrictions, simultaneously. It can be formulated as follows in Equation (1):  
\[F(x,u) \tag{1}\]  
The objective function of system optimization is denoted by \(F(x,u)\) where the objective function \(F\) is the function to be reduced or maximized, and the control vector \(x\) includes generator active power outputs \(PG\) (except for the slack bus), generator voltages \(VG\), transformer tap settings \(T\), and shunt voltage compensations \(QC\), Therefore, vector \(x\) is stated below in Equation (2):  
\[x\ =\ [PG_{2},\ \ldots,\ PG_{NG},\ VG_{1},\ \ldots,\ VG_{NG},\ T_{1},\ \ldots,\ T_{NT},\ |QC_{1},\ \ldots,\ QC_{NC}\ ] (2)  
where \(NG\) is the number of generators, \(NT\) is the number of transformers, and \(NC\) is the number of reactive power compensators. \(PG_{i}\) controls the output power of the generator attached to bus \(i\). \(Ti\) represents the transformer tap setting connected to the bus \(i\). \(QC_{i}\) indicates the reactive power value of the reactive power compensation unit linked to bus \(i\).  
The state vector determines the status of the power system. \(u\) vector can be written as presented in Equation (3):  
\[u=[PG_{1},\ |VL_{1},\ \ldots,\ VL_{NL},\ |QG_{1},\ \ldots,\ Q_{NG},\ |S_{1},\ \ldots,\ S_{NTL}] \tag{3}\]  
where \(NL\) is the load bus count, \(NTL\) is the transmission lines count, and \(VL_{i}\) is bus \(i\)'s load voltage. \(QG_{i}\) is the reactive power of the generator linked to bus \(i\), whereas \(Sl_{i}\) is the load flowing over transmission line \(i\).  
It is important to note that \(PG_{1}\), which is the output power of the slack bus in the case of the generator's output power, is not included in the control vector as a result of its reliance on the state vector. For this reason, bus 2 is where the output power bus number in the control vector begins.
## 4 Results and Discussion
### Influence of R134a Condensation Temperature  
Figure 5 shows the variation of the COP versus the R134a condensation temperature. For the vapor centrifugal compressor, the suction pressure should not be too low, and the pressure ratio cannot be too large. Hence, during the calculation process, the R134a condensation temperature and the suction temperature of the vapor centrifugal compressor cannot be lower than 75 \({}^{\circ}\)C and 65 \({}^{\circ}\)C, respectively. The pressure ratio of the vapor centrifugal compressor cannot exceed 3. Other operation parameters can be referred to in Figures 1 and 2.  
For CHPVC, the COP increases first and then decreases with the increment of the R134a condensation temperature. The optimal COP is 2.398 at the R134a condensation temperature of 76 \({}^{\circ}\)C. However, for HPTVC, the COP decreases continuously with the increase in the R134a condensation temperature. The major reason is that the temperature lift driven by the vapor centrifugal compressor decreases. Moreover, a significant drop occurs at the R134a condensation temperature between 82 \({}^{\circ}\)C and 83 \({}^{\circ}\)C. The major reason is that the simulated isentropic efficiency decreases dramatically, while the compression process changes from under-compression to over-compression due to the increment of the pressure ratio of the R134a compressor. The optimal COP is 2.419 at the R134a condensation temperature of 75 \({}^{\circ}\)C. Meanwhile, the suction pressure and the pressure ratio of the vapor centrifugal compressor are 0.025 MPa and 2.8, respectively. Additionally, while the R134a condensation temperature exceeds 78 \({}^{\circ}\)C, CHPVC has a higher COP, and the reverse is true for the R134a condensation temperature below 77 \({}^{\circ}\)C. It can be deduced from the simulation results that for CHPVC, a suitable temperature lift of the cascade heat pump is important, and for HPTVC, a large temperature lift driven by the centrifugal vapor compressor is relatively good as its pressure ratio is allowed.  
Figure 5: COP versus the R134a condensation temperature.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 5 Speaker Adaptation Methods  
Our research aimed to study the feasibility of developing speaker-adapted systems for the VSR task. By considering the speaker-dependent scenario defined for the LIP-RTVE database, we analysed how estimating specialised end-to-end VSR systems for a specific person affected the quality of speech recognition. Specifically, two different adaptation methods are explored in this work: the conventional fine-tuning technique (Section 5.1), and the use of the so-called _Adapters_ (Section 5.2). Details on the training settings used when applying these adaptation methods are found in Section 6.4.
# Impact of Audio Delay and Quality in Network Music Performance +
## 4 Subjective Evaluation Design  
Given the diverse settings and outcomes in prior work, we chose not to rely solely on the standard Mean Opinion Score (MOS) metric for evaluating sessions. Instead, we developed a more comprehensive questionnaire to assess multiple dimensions of user perception regarding the quality and overall experience of NMP. An initial version of the questionnaire was tested in a pilot study involving four musician pairs [2]. While the pilot included more detailed questions about the participants' musical background, we found that longer surveys led to fatigue and disengagement. Therefore, for the main study, we refined the questionnaire to focus on aspects less frequently explored in existing literature, such as feelings of irritation, anxiety, and preference between audio and video cues.  
To minimize fatigue and potential bias due to repetition, we used different question sets depending on the experimental scenario: delay-related questions were used for Scenario A, and quality-related ones for Scenario B. Since each scenario included 10 repetitions, we aimed to keep the questionnaire concise and engaging. We conducted Scenario A (which involved more questions) before Scenario B.  
The following subsections detail the questionnaire items, grouped into three categories: questions common to both scenarios, questions specific to Scenario A, and those specific to Scenario B.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Repetition** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Sampling rate (kHz) & 44.1 & 36 & 28 & 22 & 16 & 12 & 8 & 18 & 48 & 88.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario B: sampling rates.
# A REM Update Methodology Based on Clustering and Random Forest
## 3 Proposed Approach for REM Updates
### REM Construction  
Given \(K\) cluster centers, the \(K\)-trained RF models, and grid \(G\), we construct the REM for the area of interest. First, each \(\mathbf{f}_{h}\) sample from grid \(G\) is assigned to one cluster among the \(K\) clusters available. Next, the border samples in the area of the intersection of the clusters are determined to avoid a hard change in the REM between clusters. Then, \(P\) neighbor samples are obtained for each sample in the border area based on Euclidean distance. Next, we group all the \(P\) neighbor samples of the border areas, and we remove duplicate points to create grid \(G_{border}\). Finally, we remove the samples of \(G_{border}\) from original grid \(G\) to obtain grid \(G_{in}\). Therefore, we have divided \(G\) into grids of the border areas, \(G_{border}\), and a grid with the internal points in each cluster, \(G_{in}\).  
To obtain the RSSI value for the points of grid \(G_{in}\), we assign each sample from \(G_{in}\) to one of the \(K\) clusters, and we use the corresponding RF model to predict the RSSI value. In the samples in grid \(G_{border}\), we determine the two nearest cluster centers for each sample, and the predicted RSSI value is the weighted average of the corresponding two RF models. In detail, let us consider sample \(\mathbf{f}_{h,border}\) from grid \(G_{border}\). First, we evaluate the distance from \(\mathbf{f}_{h,border}\) to all \(K\) cluster centers, selecting two clusters with the nearest Euclidean distance, denoted as \(d_{\mathbf{f}_{h,border},C_{a}}\) and \(d_{\mathbf{f}_{h,border},C_{b}}\), where \(d_{\mathbf{f}_{h,border},C_{a}}<d_{\mathbf{f}_{h,border},C_{b}}\) and where \(C_{a}\) represents one of the \(K\) available clusters. Then, we use the RF model of cluster \(C_{a}\) to predict the first RSSI value of \(\mathbf{f}_{h,border}\), denoted as \(R_{h,border,C_{a}}\), and the RF model of cluster \(C_{b}\) predicts the second RSSI value of \(\mathbf{f}_{h,border}\), denoted as \(R_{h,border,C_{b}}\). Finally, the RSSI value for \(\mathbf{f}_{h,border}\) is determined with the following weighted average:  
\[R_{h,border}=\frac{\left(\frac{d_{\mathbf{f}_{h,border},C_{b}}}{d_{\mathbf{ f}_{h,border},C_{a}}}\right)^{5}R_{h,border,C_{a}}+\left(\frac{d_{\mathbf{f}_{h,border},C_{a}}}{d_{ \mathbf{f}_{h,border},C_{b}}}\right)^{5}R_{h,border,C_{b}}}{\left(\frac{d_{ \mathbf{f}_{h,border},C_{b}}}{d_{\mathbf{f}_{h,border},C_{b}}}\right)^{5}+ \left(\frac{d_{\mathbf{f}_{h,border},C_{a}}}{d_{\mathbf{f}_{h,border},C_{b}}} \right)^{5}}. \tag{3}\]  
Once all RSSI values are obtained for all samples in grids \(G_{in}\) and \(G_{border}\), we use Matplotlib in Python to create the REM based on the color map. Moreover, we superpose the SLAM map of the room by carefully adjusting the opacity of the REM with the alpha parameter from Matplotlib.
## 3 Results and Discussion
### Evolution of Thermophysical Properties
#### 3.2.2 Specific Heat Capacity  
The evolution of the specific heat capacity of the inner shaft lining concrete on Day 1, Day 3, and Day 7 after pouring is depicted in Figure 7. The specific heat capacity of the shaft lining concrete decreased significantly with an increase in age, and the decline rate gradually decreased with an increase in age. The shaft wall thickness poorly affects the decline rate of specific heat capacity with age, as reflected by the parallel evolution curve of specific heat capacity at each given thickness (Figure 7). The shaft lining thickness largely influences the evolution of the specific heat capacity of the inner shaft lining concrete. The specific heat capacity increased with increasing thickness at the same age, whereas the increase rate gradually decreased. This is mainly because with the increase of age, the hydration reaction of shaft lining leads to the increase of strength, while the moisture content of free water gradually decreases, which leads to the decrease of specific heat capacity. And the thin thickness of the shaft lining results in slow internal hydration reaction rate, which leads to slower concrete strength growth and greater porosity of the thinner shaft lining at the same age. Therefore, the specific heat capacity decreased with increasing age and increased with increasing thickness. When the thickness increased from 1.0 m to 1.2 m, the specific heat capacity increased by more than 10.2%; meanwhile, when the thickness increased from 1.2 m to 1.5 m, the specific heat capacity increased by only about 1.3%. The specific heat capacity of the inner shaft lining concrete with thicknesses of 1.0, 1.2, and 1.5 m evolved between 0.66 and 0.98 KJ/(kg.k) within the first 7 d after pouring. Finally, the evolution equation for specific heat capacity with age within the first 7 d after pouring is fitted based on experimental data (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Specific Heat** & **Time (d)** & **Thickness (m)** & **Fitting Equation** & **R-Squared** \\  & **Capacity (KJ/(kg.k))** & & & & \\ \hline
1 & y & x & 1.0 & \(y=0.0133x^{2}-0.1433x+1.01\) & 1 \\ \hline
2 & y & x & 1.2 & \(y=0.0088x^{2}-0.105x+1.0663\) & 1 \\ \hline
3 & y & x & 1.5 & \(y=0.0075x^{2}-0.095x+1.0675\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evolution equation for specific heat capacity with age within the first 7 d.  
Figure 6: Evolution of thermal conductivity with age on Day 1, Day 3, and Day 7 after pouring.
## 6 Conclusions and Future Work  
As soon as a pull request is submitted, it should be allocated to integrators for further investigation. Due to the number of pull requests in popular projects, integrators often have difficulty processing them on time. Additionally, it takes a lot of time to analyze such existing works, which leads to a growing number of pull requests. The time and effort involved in reviewing can be saved by using an automated duplicate detection method. An optimal pull-request duplication-detection model is proposed for OSS repositories by using a hybrid deep learning technique. A hybrid leader-based optimization (HLBO) algorithm is used to extract textual data from the pull requests. Multiobjective alpine skiing optimization (MASO) is used for the change similarity computation between pull requests. Finally, the hybrid GAN-GS technique is used for the pull-request duplicate detection. The proposed HDL-ODPR model is validated against the datasets such as DupPR-basic and DupPR-complementary data. From the simulation results, we showed that the effectiveness of proposed HDL-ODPR model is very high as opposed to the existing state-of-the-art models. The duplicate detection accuracy of proposed HDL-ODPR model is 92.644% and 96.87% achieved for standard benchmark datasets are DupPR-basic and DupPR-complementary, respectively. The proposed approach can be applied to a larger number of repositories from different social coding platforms in the future. The experimental results will be further improved upon by investigating other pull-request information or features.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Detection Techniques**} & \multicolumn{3}{c}{**Metrics (\%)**} & \multicolumn{3}{c}{} \\ \cline{2-6}  & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **SRE** \\ \hline GDN-GS & 96.87 & 95.23 & 96.12 & 95.6729 & 93.56 \\ PRs-text [21] & 87.414 & 86.991 & 88.024 & 87.505 & 86.114 \\ PRs-text-similarity [32] & 82.184 & 81.761 & 82.794 & 82.275 & 80.884 \\ PRs-Improved [33] & 76.954 & 76.531 & 77.564 & 77.045 & 75.654 \\ Random forest & 61.264 & 60.841 & 61.874 & 61.355 & 59.964 \\ K-means clustering & 50.804 & 50.381 & 51.414 & 50.895 & 49.504 \\ Hierarchical clustering & 56.034 & 55.611 & 56.644 & 56.125 & 54.734 \\ Support vector machine & 71.724 & 71.301 & 72.334 & 71.815 & 70.424 \\ k-nearest neighbor & 66.494 & 66.071 & 67.104 & 66.585 & 65.194 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis with DupPR-complementary dataset.  
Data Availability StatementOur proposed model is validated against the public standard benchmark datasets wihivh are DupPR-basic and DupPR-complementary data [21].  
The author has declared that there is no conflict of interest[PERSON]
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 5 Verification of Frame Experiment
### Framework Dynamic Testing and Response Construction with Additional Virtual Mass  
1. Dynamic testing of undamaged structure: An acceleration sensor was installed in the middle position of substructures, and the other side was hit with a modal force hammer several times to obtain multiple sets of loads and acceleration response data. The measured data were processed to determine the experimental frequencies of the undamaged structures.
2. Dynamic testing of damaged structure: The sensor was installed in the middle position of the substructures, and the structure was hit with a modal force hammer on the other side to obtain multiple sets of loads and acceleration data.  
The acceleration response data of the virtual structure was constructed using the additional virtual mass method, and the structure frequency was identified according to the Eigen system realization achievement method. The virtual structure formed after the  
Figure 10: Frame model. (**a**) experimental picture; (**b**) FEM.  
virtual mass was added to the \(i\)th substructure and is denoted as. The average values of the fourth-order frequency of the 9 virtual structures are listed in Table 4.
## 3 Results and Discussion
### Dynamic Strain Sensing Properties  
#### 3.4.1 Hysteresis and Plastic Deformation  
The effects of CNT content and structure formation on hysteresis during the dynamic strain sensing test were analyzed. All samples were stretched to 50% and 100% tensile strains, and hysteresis analysis was performed in terms of the relative resistance change (R/R\({}_{0}\)) that occurred in the first cycle. According to Figure 5a,b, the hysteresis of 1 wt.% MWNT/NR and 5 wt.% MWNT/NR decreased from 640% to 422%, and 315% to 292, respectively, at 50% strain, when a hybrid structure was adopted. Hysteresis occurs because the electrical network of the CNTs that are initially formed is deformed [17; 22]. Therefore, when the strain range is large and the CNT content is small, the deformation of the electrical pathway is large, which leads to a large hysteresis. However, sealing with an NR layer can reduce the hysteresis in a single composite. Further, owing to the excellent adhesion between the NR and composite layers, the hybrid structure can prevent cracks on the composite surface. In addition, the elastic recovery characteristics of pure NR can control the plastic deformation of the conductive layer, which can reduce hysteresis. Plastic deformation is a phenomenon whereby plastic does not return to its initial length when it relaxes after stretching. Because CNTs in elastomers can interfere with the mobility of NR chains when relaxed after strain, plastic deformation of CNT composites is an inevitable issue. In the case of our hybrid structure, it exhibited excellent adhesion between the interfaces, thus reducing plastic deformation. Therefore, hysteresis can be reduced by reducing plastic deformation. The reduced plastic deformation, based on content and structure, is presented in Table 2. Because such hysteresis dominates the CNT content and the initially formed electrical network, the effect of introducing the hybrid structure may not be exhibited well for 5 wt.% MWNT/NR. Similar trends are observed in Figure 5c,d. The hysteresis of 1 wt.% MWNT/NR and 5 wt.% MWNT/NR decreased from 710% to 220%, and 405% to 385%, respectively, at 100% strain in the hybrid structure. That is, the lower the CNT content and the higher the tensile rate, the greater the hysteresis reduction effect of the hybrid structure. However, the higher CNT content was relatively insufficient, owing to the well-constructed initial electrical network.  
Figure 5: Hysteresis curve of 50% strain range for (**a**) 1 wt.% single composite and (**b**) 5 wt.% hybrid composite. Hysteresis curve of 100% strain range for (**c**) 1 wt.% single composite and (**d**) 5 wt.% hybrid composite.
## 4 Results and Discussion
### Result of Individual Bias  
Figures 7 and 8 show considerable variation in the reporting of software and the tools researchers chose for the research on transformer condition monitoring using the IoT. Notably, 20% of the research papers did not specify IoT cloud platforms, and 12.5% did not specify their preferred microcontrollers. This is troublesome since it restricts the capacity to evaluate the proof of concept and functionality of their proposed IoT systems for transformer condition monitoring. The absence of disclosure might raise the possibility of presentation bias, rendering it harder to assess the validity of information offered by these research papers. Studies [3; 108; 109; 21; 25; 43; 44; 90; 105; 110; 115; 116] did not specify their preferred microcontrollers. Studies [5; 108; 21; 22; 25; 29; 30; 40; 42; 44; 48; 52; 53; 59; 64; 86; 89; 90; 105; 106; 108; 110; 116] did not specify their preferred IoT clouds for real-time data monitoring and visualization.  
Figure 6: Publications’ quality assessment scores.  
Figure 7: Research distribution by IoT cloud platforms.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Performance Stability  
Another variable that should be tested under various outlier ratios is the stability of the results obtained by running the algorithm multiple times. To this end, the Church dataset was used. The algorithm was repeated 50 times for each instance of the data. For outlier ratios ranging from 10 to 70%, the variations of accuracy was not considerable (\(\alpha\) = 99% \(\pm\) 2%). However, at an 80% outlier rate, the average accuracy decreased to 92% \(\pm\) 8%. Although this was a noticeable change in the performance of the algorithm, the median of the accuracies was yet reasonably high (95%). Regarding the number of models hypothesized before termination, a large range of changes was observed at lower outlier ratios. As explained earlier, this happened due to the stopping criterion in GA. However, this was itself an advantage for large outlier ratios (more than 50% outliers).
## 2 Materials and Methods
### Experiments
#### 2.1.2 Liquid Phantoms  
Liquid phantom media with required optical properties were prepared by mixing pre-determined volumes of an absorbing solution (with negligible scattering) and scattering solution (with negligible absorption), and diluting the resulting solution with deionized (DI) water, using protocols described previously [33; 43]. A suspension of 1.0 \(\upmu\)m diameter polystyrene microspheres (Polybead microspheres, 07310, Polysciences Inc., Warrington, PA, USA) was used as the scatterer and its scattering coefficient was calculated from Mie theory [44]. The absorber solution was prepared by dissolving bovine hemoglobin (H3760; MilliporeSigma, MO, USA) in DI water, whose absorption coefficient was experimentally measured using a transmission spectrophotometer (Cary 100 UV-Vis, Agilent Technologies, Wilmington, DE, USA). The volume-fractions of absorber and scatter mixed together determined the absorption and scattering coefficient of the final solution [33]. The liquid media used for phantoms were prepared with absorption coefficient of \(\mu_{a}\) = 0.075 cm\({}^{-1}\) and reduced scattering coefficient \(\mu_{s}^{\prime}\) = 12 cm\({}^{-1}\). These values for absorption and scattering at 785 nm (the laser wavelength) are within ranges previously reported for different tissue types including bone, abdomen and white matter of the brain [45; 46; 47].