esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Siting and Sizing of FACTS in Distribution Networks Using the Black Widow Algorithm
## 1 Introduction
### Contributions and Scope  
Considering the above-presented review of the state of the art, this research makes the following contributions:  
1. The efficient integration of multiple FACTS into radial distribution networks, i.e., UPFC, SVC, and TCSC, applying the black widow optimization algorithm (BWO) and using discrete-continuous codification.
2. A validation of the effectiveness of the proposed BWO while considering the existing literature reports regarding the optimal placement and sizing of D-STATCOMs (modeled as an SVC device).  
It is worth mentioning that in this research, all the FACTS considered are modeled using cubic functions that represent the investment costs, as reported in [28]. In addition, the multi-period power flow approach based on successive approximations is implemented within the framework of a master-slave strategy, in order to quantify the expected energy loss costs. The BWO approach is the master stage, entrusted with defining the optimal location and size of the FACTS devices, which is then evaluated via the successive approximations power flow (SAPF) method. Regarding the UPFC, it is essential to mention that this research only uses it for managing the reactive power injected into the connected node, as its series component cannot control the active power flow in the distribution branch. This is due to the radial structure of the distribution grid, where the active power flow is only necessary to support the load consumptions downstream of the node and branch where the UPFC is installed.  
The selection of the master-slave approach, which is based on the hybridization of the BWO (master stage) and the SAPF (slave stage) for dealing with the exact MINLP model associated with the location and sizing of FACTS devices in distribution networks, was based on the fact that the MINLP problem can be easily decoupled into two subproblems, i.e., the binary (integer) problem and the continuous problem. The solution of the integer component of the model is easily reached with combinatorial optimization methods (such as the BWO approach), and the continuous part can be efficiently solved with numerical methods applicable to the power flow problem in distribution networks (i.e., the SAPF approach).  
Note that the BWO approach is selected in this research as the master optimization method, as it has been proven to be an excellent population-based optimization technique to deal with continuous and integer optimization problems in the current literature [29]. This algorithm can be easily implemented, and its efficiency lies in its similarities with classical genetic optimization algorithms regarding its evolution rules [30]. In addition, after a complete review of the state of the art regarding the location and sizing of FACTS in distribution networks, no evidence for the application of the BWO and its hybridization with the SAPF was found, which constitutes a clear research opportunity.
## 2 Materials and Methods
### Changes in Impedance According to CPR  
When a patient undergoes a cardiac arrest, basic life support, including CPR, is given. During this time, the size of the thoracic impedance is determined by factors such as shape, sternum, and muscles of the thoracic region [20]. A sternal fracture often occurs during a patient's fall or during CPR, and if the shape of the sternum changes due to such fractures, impedance may be reduced. In addition, impedance is affected by the pressure applied to the chest during CPR and changes in the blood flow [21; 22; 23].  
We refer to a studies that measures the change of mechanical impedance of patients during CPR [24; 25; 26]. In order to investigate the relationship between CPR strength and periodic changes in bioelectrical impedance, CPR experiments were conducted on the experimental body made by applying different modulus of elasticity of springs in the human body model. Figure 1 shows 2 graphs with different elastic factors and the change in impedance according to frequency. In Figure 1, the horizontal axis of the graph represents the cycle of CPR, and the vertical axis indicates the mechanical impedance change of the human body model. Using the different elasticity modulus of the CPR equipment, the impedance was 80 \(\Omega\) when the frequency was 2 Hz in a case of 680 kgf/m and the impedance was 100 \(\Omega\) when the frequency was 2 Hz in a case of 942 kgf/m. At the same frequency, there was a difference of about 20 \(\Omega\), depending on the elastic modulus. On comparing the impedance values of the 2 cases, it can be seen that a larger elastic modulus is related to larger impedance, and the lower the frequency, the larger the impedance.  
Here, frequency indicates the number of CPR compressions, and the change in impedance is sensitive to the CPR cycles and intensity. This can indicate whether the CPR was performed accurately. Based on these changes, we can observe changes in the sternum and thus detect changes in the thoracic impedance [27].  
Figure 1: Impedance by elastic factor: (**a**) Modulus of elasticity at 680, (**b**) Modulus of elasticity at 942.
# Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers
## 3 Results
### Dependent \(t\)-Tests  
A dependent-samples \(t\)-test was performed to compare ratings of LC and SA across listening Sessions 1 (Gender Opposite) and 2 (Gender Known). This initial analysis was performed for the combined group of speakers. For LC, the results indicated that there was not a statistically significant difference (t(11) = 0.157, \(p\) = 0.878) between ratings at Session 1 (mean \(\pm\) standard deviation [SD]: 50.3 \(\pm\) 21.2) and Session 2 (mean \(\pm\) SD: 49.7 \(\pm\) 23.5). Based on the results for SA (Figure 1), the differences in mean ratings also were not found to be statistically significant across Session 1 (mean \(\pm\) SD: 54.0 \(\pm\) 19.9) and Session 2 (mean \(\pm\) SD: 49.7 \(\pm\) 20.4). The differences in mean ratings of SA did, however, closely approach statistical significance (t(11) = 2.192, \(p\) = 0.051).  
When our data were further analyzed by gender, there was only a statistically significant difference between mean ratings of SA for our female speakers (t((5) = 3.291, \(p\) = 0.022) between Session 1 (mean \(\pm\) SD: 53.0 \(\pm\) 17.6) and Session 2 (mean \(\pm\) SD: 44.3 \(\pm\) 14.1). Although not statistically significant (t(5) = 2.181, \(p\) = 0.081), a similar effect was observed for the differences in mean ratings of LC for females between Session 1 (mean \(\pm\) SD: 48.7 \(\pm\) 19.2) and Session 2 (mean \(\pm\) SD: 43.1 \(\pm\) 20.1)--again, a finding that approached statistical significance. Based on these findings, female speakers were judged to be more unacceptable (i.e., SA) and, to a lesser degree, more uncomfortable to listen to (i.e., LC) when known to be female (Session 2) as compared to when thought to be male (Session 1).  
When male speakers were analyzed independent of their female counterparts, there were no statistically significant differences in mean ratings of LC (t(5) = -1.475, \(p\) = 0.200) between Sessions 1 (mean \(\pm\) SD: 51.8 \(\pm\) 24.8) and 2 (mean \(\pm\) SD: 56.7 \(\pm\) 26.5) or for mean ratings of SA (t(5) = 0.035, \(p\) = 0.973) across Session 1 (mean \(\pm\) SD: 55.1 \(\pm\) 23.7) and Session 2 (mean \(\pm\) SD: 55.0 \(\pm\) 25.5). A graphic representation of gender-based differences in judgments of auditory-perceptual features of LC and SA is shown in Figure 2 for female speakers and in Figure 3 for male speakers.  
Figure 1: The mean ratings of all TE speakers across Session 1 (Gender Opposite) and Session 2 (Gender Known) for listener comfort and speech acceptability.  
Figure 2: The mean ratings for female TE speakers across Sessions 1 (Gender Opposite) and 2 (Gender Known) for listener comfort and speech acceptability. Note: *** indicates statistically significant differences in scores between Session 1 and Session 2.
## 3 Mathematical Models for the RT POD of LEO Satellites  
### Preprocessing of Pseudo-Range and Carrier-Phase Measurements after B2b Correction  
Compared with ground receivers, LEO satellites travel more quickly and have shorter signal tracking times for GNSS satellites, and their observations thus have greater gross errors. It is thus necessary to preprocess the spaceborne measurements of the LEO satellites. The on-the-fly data-editing method is adopted in preprocessing the RT POD data of LEO satellites using PPP-B2b [2]. PPP-B2b corrections are used for correcting the broadcast ephemeris and pseudo-range. Next, in the filtering process, the LEO orbit calculated through dynamic integration combined with each GNSS satellite orbit obtained from the GNSS broadcast ephemeris corrected by PPP-B2b is used to compute each spaceborne GNSS receiver clock offset and then check whether all the satellite pseudo-range residuals exceed a given tolerance [20]. If so, the pseudo-range observations with gross errors are eliminated. The carrier-phase measurements are processed using the same method. If necessary, the above process is repeated to delete faulty error measurements and thus ensure that the data are correct.
## 5 Field Application
### Effects of Gob-Side Entry Retaining with CFST  
The convergence deformation of the roof and bottom of the roadway was measured using a deep base displacement meter. The 91-101 working longwall has an average of five production cycles per day with a mining advance of 0.8 m per cycle, and the footage of roadway retaining is 4.0 m per day. Monitoring stations A and B were set up in the middle of the retained roadway for monitoring the deformation of the roof and floor of the retained roadway during the mining. Monitoring station A was located 370 m from the open-off cut point, and monitoring station B was located 680 m from the open-off cut point. Figure 13 shows the displacement curves of the two monitoring points.  
As can be seen from Figure 14, the deformations of the roof and floor of the retained roadway at the two monitoring points showed the same characteristics, which can be divided into three stages. Stage 1: When the monitoring point was located 0-65 m behind the working longwall, the deformations of the roof and floor showed slowly increasing trends. The maximum deformation of the roof located 65 mm behind the working longwall reached about 150 mm, and there was basically no floor heave in the range of 0-65 m behind the longwall. Stage 2: When the monitoring point was 75 mm behind the working longwall, the point entered the dynamic pressure influence zone and the roof of the roadway subsided dramatically in a short time, accompanied by floor heave. Stage 3: When the monitoring  
Figure 12: Flow chart of CTST gob-side entry retaining.  
Figure 13: Displacement curves of retained roadway roof and floor. (**a**) Monitoring point A; (**b**) monitoring point B.  
point was more than 105 m behind the working longwall, the velocity of the roadway roof deformation slowed down, gradually approaching stability. The final subsidence of the roof was about 430 mm, and the floor heave value reached about 80 mm.  
The roof of the retained roadway was probed at monitoring point A, as shown in Figure 14. It can be seen from the figure that the shallow part of the roadway roof was broken, the hole wall was relatively intact at a depth of 1.51 m, the position of the coal-rock interface was reached at a depth of 2.55 m, and there was no separation at the coal-rock interface. At the depth of 5.77 m, it can be seen that there were ring and longitudinal cracks in the hole wall. At the depth of 7.03 m, the rock masses were intact. The depth of the anchorage end of the anchor cable in the 91-101 ventilation roadway was greater than 7 m; therefore, the strength of the roadway anchorage system was less affected by roof activities, and the stability of the retained roadway roof was good. Figure 15 shows the realistic view of gob-side entry retaining with large-diameter, concrete-filled steel tubes in the 91-101 ventilation roadway.
# Outlier Detection and Prediction in Evolving Communities
## 5 Evaluation Results
### Parameter Tuning
For a lower weight of \(w=\frac{1}{3}\), the percentage of nodes with high outlier scores increases in both datasets. A low \(w\) yields higher importance of the nodes' content; the increase in possible outliers can be explained by the fact that even nodes with strong structural connections may achieve high outlier scores if their content differs from their neighbors. In contrast, a higher weight value of \(w=\frac{2}{3}\) means that the outlierness criterion is closer to plain structural outlier detection. In this latter case, the outlier scores tend to be lower in both datasets, falling closer to the case where content and structure are equally weighted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **ttl / obs** & **\#Cons** & **Mean Members** & **Median Labels** \\ \hline \multirow{3}{*}{**Stack Exchange**} & 30/15 & 4118 & 5.80 & 7 \\ \cline{2-5}  & 30/30 & 2848 & 6.10 & 7 \\ \cline{2-5}  & 60/30 & 5225 & 9.25 & 8 \\ \hline \multirow{3}{*}{**Movie Lens**} & 30/30 & 294 & 13.45 & 20 \\ \cline{2-5}  & 60/30 & 631 & 25.21 & 25 \\ \cline{1-1} \cline{2-5}  & 120/60 & 656 & 32.55 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Community features extracted from different sets of time-to-live (ttl) and observation window size (obs) values. Comparing the two datasets, it can be seen that the effect of \(w\) is much stronger for the MovieLens dataset, showing that while the nodes in this dataset are densely connected, they have more diverse content compared to StackExchange. For the rest of our experiments, we set \(w=\frac{1}{2}\) to assign equal importance to both structure and content.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 4 Channel Model  
In our investigations, we consider a G2T FSO communication system that utilizes Intensity Modulation with Direct Detection (IM/DD) using an OOK modulation scheme, which is commonly employed in practical FSO systems. The receiver mounted on the train's roof integrates the photocurrent signal, associated with the incident optical power, by utilizing a detector responsivity R for each bit period. The received electrical signal y is given by [27]:  
\[\mathrm{y}=\mathrm{hRx}+\mathrm{n} \tag{3}\]  
Figure 2: Siding loop model.  
where \(x\) represents the transmitted symbol from BS, h denotes the channel state, and \(n\) represents the signal-independent additive white Gaussian noise with variance \(\sigma_{n}^{2}\). The transmitted symbol is \(x\in\{0,2P_{t}\}\), where \(P_{t}\) represents the average transmitted optical power. The channel state \(h\) is assumed to be a composite of three factors multiplicatively independent \(h=h_{a}h_{g}h_{t}\) where \(h_{a}\) represents the path loss, \(h_{g}\) represents the attenuation due to geometric spread and pointing errors, and \(h_{t}\) represents the attenuation due to atmospheric turbulence.
## 3 Results and Discussion
### Life Cycle Assessment: Main Results  
The LCA results in terms of carbon footprint and cumulative energy demand obtained for the panels composed of a single loose material are reported in previous works [12,16] and they are summarized in Table 6, whereas the results related to the composed panels investigated in the present paper are reported in Table 7.  
The best behavior is observed for Rice Husk and Coffee Chaff, both in terms of minimum Global Warming Potential and Cumulative Energy Demand, when compared to Cork and Granulated Tires, especially considering the thermal analysis (RH and CC have lower thermal conductivities in comparison to ELT, about 0.07 W/mK). COR could be very performant, but the contribution of the transports (from Sardinia to Northern Italy) influences the values of the embodied energy of the final product, despite the best thermal resistance. As expected, the results of the mixed panels are not very different from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GWP (1 m\({}^{2}\))** & **GWP (1 R)** & **CED (1 m\({}^{2}\))** & **CED (1 R)** \\ \hline  & **kg CO\({}_{2\,\text{eq}}\)/1m\({}^{2}\)** & **kg CO\({}_{2\,\text{eq}}\)/f.u.(R)** & **MJ/1 m\({}^{2}\)** & **MJ/f.u.(R)** \\ \hline COR & 3.07 & 3.687 & 61.85 & 76.99 \\ ELT & 3.61 & 11.85 & 67.74 & 237.22 \\ RH & 1.11 & 1.881 & 22.13 & 39.17 \\ CC & 0.57 & 3.07 & 1.47 & 29.53 \\ \hline \hline \end{tabular}
\end{table}
Table 6: GWP (kg CO\({}_{2\,\text{eq}}\)) and CED (MJ) for 1 m\({}^{2}\) of panel and for R = 1 m\({}^{2}\)K/W [12,16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Panels Description**} & **\(\Delta\mathbf{T_{a}}\)** & **q** & \(\mathbf{T_{s,average}}\) & \(\mathbf{R_{tot}}\) & \(\lambda_{\text{waste panel}}\) \\ \cline{2-6}  & (\({}^{\circ}\)C) & **(W/m\({}^{2}\))** & (\({}^{\circ}\)C) & **(m\({}^{2}\)K/W)** & **(W/mK)** \\ \hline COR & 21.2 & 14.5 & 34.7 & 0.83 & 0.055 \\ ELT & 20.9 & 30.4 & 36.4 & 0.30 & 0.135 \\ RH & 20.6 & 20.0 & 35.8 & 0.58 & 0.070 \\ CC & 20.4 & 24.0 & 36.4 & 0.39 & 0.076 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermal conductivity of the recycled panels with a sole material and related measurement parameters [12,16].  
each other, especially in terms of GWP. Larger differences are observed in terms of CED, despite the results are aligned with the impacts of the previous analyzed panels.  
In this second analysis, the influence of COR, which is the only one faraway supplied raw material, is not so significant, because it is present in all the mixing compositions of the 6 types of panels. The best performance is found for panel 5, composed of 40% COR, 30% CC, 30% RH, whereas the worst one, not only in terms of thermal resistance but also in the environmental analysis, is panel 3, only composed of COR and ELT (CED (1R) 70% higher than the one for panel 5).
## 2 Materials and Methods
### Data Analysis
#### 2.2.2 Feature Selection and Extraction  
Selection of the right features is an essential step toward the successful implementation of the ML method. Extracting the features from an image mostly depends on the intensities and the texture of the pixels. The number of pixels in the horizontal and vertical directions in the kymogram comprises a 2-D matrix. Each cell in the matrix (pixel) comprises three image components with a numerical value ranging between 0 and 255, corresponding to the three color channels (i.e., red, green, and blue). The features were calculated based on the intensity values of only the red and green channels. The blue channel was excluded from the analysis due to the high noise level and the absence of essential information. In this work, three features were extracted, namely, two intensity features and a gradient feature. Different number and combinations of the aforementioned features were utilized in the development of the proposed algorithms to determine which features should be used to perform an accurate vocal fold edge representation.  
Intensity Features: The pixel intensities of the red and green channels in a kymogram were considered as two features. Since the regions of interest in the kymogram (glottal areas) have lower intensities (darker) than the neighborhood regions, selecting the pixel intensities as a feature was essential to facilitate distinguishing the glottal area from the laryngeal tissues in the kymograms. However, relying only on the intensities as features was not enough to segment the image because of the high level of noise in the present video data and appearance of dark pixels in places other than the glottis.  
Gradient Feature: The image gradient can be used to detect the glottal area edges given the contrast between the intensity of the glottis and the surrounding regions. Hence, the kymogram image gradient was used for feature extraction. The positive and negative gradients were computed along the \(x\)- and \(y\)-axis in the kymogram with a step size of 8 pixels. In the negative gradient, the pixels with positive values were assigned a value of zero and vice versa. An overall gradient magnitude was calculated by taking the square root of the sum of squared of the four negative and positive gradients in the two directions.
# A Damage Model for Concrete under Fatigue Loading
## 2 Microcrack Mechanism in Concrete under Multi-Axial Loading
### The Causes for the Mode-II Microcracks under Multi-Axial Loading  
When the concrete is subjected to a biaxial tensile load, a micro-cell within a representative volume element (RVE) was introduced and is shown in Figure 1. In detail, the stress flow curve becomes concentrated when it approaches the crack tip, and the plane stress on the plane horizontal and vertical to the direction of the crack propagation is able to be described by the normal and shear stress as follows, \(\sigma_{\text{h}}\) and \(\tau_{\text{h}}\), \(\sigma_{\text{v}}\) and \(\tau_{\text{v}}\), respectively (Figure 1c). Due to sufficient normal stress \(\sigma_{\text{v}}\) or stress intensity factor (SIF) \(K_{\text{I}}\) at the crack tip, the crack will initiate and grow through the direction where the maximum SIF exists (i.e., transverse to the direction of maximum principal tension, Figure 1d-e). Therefore, the crack type, namely the mode-I crack, decreases the effective load area of the micro-cell in the maximum principal tension direction, resulting in the stiffness degradation of the specimen [33; 34; 35; 36; 50].  
However, mode-II microcracks have not been well considered in modeling concrete under multi-axial stresses in the literature [6; 12; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Through different approaches, including experimental observations, mechanical analysis and atomic simulations [15; 16; 17; 18; 19; 20; 21], it is validated that the real crack (excepting some pre-existing cracks) in the material is blunt, caused by the appearance of mode-II microcracks.  
Specifically, mode-II microcracks are produced by the local shear stresses (i.e., the shear stress \(\tau_{\rm h}\) in Figures 1c and 2a) on the face and nearby area of relevant cracks. In a biaxial tensile load case, the directions of local shear stresses are arbitrary due to the random location of initial defects. It is distinguished from that in a uniaxial tensile case [22]. Moreover, several researchers [34; 35; 36; 37; 38; 39; 40] observed that the mode-II microcracks, rather than the dislocation-induced plastic flow, appear in complex composite materials such as concrete. Further description and explanation can be found in the literature [6; 51].  
Figure 1: Sketch of stress flow curve around the random selected micro-cell of the representative volume element (RVE) in the specimen under multi-axial tension, and the related coordinates and dimension, where \(\sigma_{1}\) denotes the maximum principal tensile stress.  
Note that it is speculated that the local constraint condition around the micro defect is stable during the crack initiation and/or propagation under multi-axial tension; otherwise, the mode-II dominant failure will appear. The development of the mode-II microcrack leads to certain energy dissipation, and it may also release the tip stress concentration of the mode-I cracks, since it causes a relatively blunter crack tip.