esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Technical Results
\begin{table}
\begin{tabular}{l r} \hline \hline
**Start of Operation of the System** & **10/5/2023** \\ \hline Grid Feed-in in the First Year (incl. Module Degradation) & 415,062 kWh/Year \\ \hline PV Generator Output & 1047.3 kWp \\ \hline Assessment Period & 30 Years \\ \hline Interest on Capital & 1\% \\ \hline PV Generator Energy (AC grid) & 2,054,029 kWh \\ \hline Direct Own Use & 8295 kWh \\ \hline Charge of the EV & 1,629,534 kWh \\ \hline Grid Feed-in & 416,199 kWh \\ \hline Down-regulation at Feed-in Point & 0 kWh \\ \hline Auxiliary Electricity Consumption (Annual) & 11,692 kWh \\ \hline Own Power Consumption & 79.7\% \\ \hline Solar Fraction & 66.1\% \\ \hline Yield Factor & 1961.04 kWh/kWp \\ \hline Performance Ratio (PR) & 88.2\% \\ \hline Yield Reduction due to Shading & 0.3\%/Year \\ \hline CO\({}_{2}\) Emissions Avoided & 1,540,522 kg/year \\ \hline \hline \end{tabular}
\end{table} 
Table 10: System data. Figure 6: PV general layout (1047.35 kWp system). The energy balance of the PV system for the LuLu's Hypermarket solar project has been analyzed using data to assess the overall performance and efficiency of the PV system. Starting from this initial potential, the system experiences multiple reductions as a result of intrinsic physical and operational inefficiencies. Table 11 shows basic information for the PV system using the PV \(*\) SOL. The module efficiency, as rated under the STC, is notably 22.01%. This results in a useful energy output of precisely 2,327,960.48 kWh. The calculation takes into consideration additional losses, such as the partial shading particular to each module, improvements in low-light performance, temperature variations, and losses caused by diodes and mismatches in module information and setup. Through a sequence of deductions, the amount of DC energy is reduced to 2,140,035.33 kWh before being regulated by the inverter. The AC output of 2,053,887.35 kWh is achieved through further modifications made at the inverter stage to compensate for voltage variations and conversion inefficiencies. When taking into account small regulatory and standby consumptions, the output is adjusted to 2,054,029.01 kWh. The specific analyses of these values and their individual effects are methodically organized in the corresponding energy balance, shown in Table 12, providing a clear perspective on how each element and environmental factor contributes to the overall energy output of the system. \begin{table}
\begin{tabular}{l r l} \hline Charge at beginning & 38,442 & kWh \\ \hline Charge of the EV (Total) & 2,466,604 & kWh/Year \\ \hline Charge of the EV (PV System) & 1,629,534 & kWh/Year \\ \hline Charge of the EV (Grid) & 837,070 & kWh/Year \\ \hline Discharging the EV for consumption & 0 & kWh/Year \\ \hline Losses due to charging/discharging & 350,776 & kWh/Year \\ \hline Losses in battery & 271,966 & kWh/Year \\ \hline Consumption due to kilometers driven & 1,882,305 & kWh \\ \hline Mileage per year & 10,950,000 & km \\ \hline of which is solar & 7,233,994 & km \\ \hline \end{tabular}
\end{table} 
Table 11: PV \(*\) SOL general output.
## 4 Analysis of Test Results for Diversion Function Pipe Segment Models  
### Analysis of Strain in the Pipe Segment under Different Pressures  
From Figure 13a-e, it is evident that under the different pressures applied to both the sides of the pipe segment, the strain distribution is not uniform. Compressive strains are observed at the top, left upper, and bottom of the segments, while tensile strains are present on the inner side, indicating a tendency to bulge outwards. The maximum compressive strain occurs at the bottom, with a significant value also found at the top. The tensile strain is greatest at the lower left and right sides, where the segment shows a tendency to concave inward. The strain values on the left side of the segment are greater than those on the right, but the difference is relatively small. The tensile strain on the upper right side decreases as the pressure difference increases, and even becomes compressive at 1.5 kPa before increasing again with further increases in the pressure difference. This phenomenon occurs because within the model test box, the pipe segments are mainly  
Figure 12: Model box of diversion function pipe segments.  
subjected to unsymmetrical pressure from the sand and water on both the sides. The sand and water compress the segments from both the left and right, causing the top and bottom to experience compressive forces and a tendency to bulge outward. Additionally, the left and right sides of the segments are pulled inward, resulting in a concave shape. The left and right lower sides experience greater pressure from the sand and water than the upper parts, leading to slightly higher strain values in these locations compared to those in the other areas where tension is experienced.  
Figure 13: Segments strain under different pressures. (**a**) Pressure difference 0.5 kPa. (**b**) Pressure difference 1.0 kPa. (**c**) Pressure difference 1.5 kPa. (**d**) Pressure difference 2.0 kPa. (**e**) Pressure difference 2.5 kPa.  
Figure 14 summarizes the correlation between the strain differences on the left and right sides, as well as the left down and right down areas, with the pressure difference.  
It can be observed that when the pressure differences across the pipe segment are 0.5 kPa, 1.0 kPa, 1.5 kPa, 2.0 kPa, and 2.5 kPa, the strain differences between the left bottom and right bottom symmetric positions of the segment are 1.326, 1.453, 1.497, 1.582, and 1.67, respectively, and the strain differences between the left and right positions of the segment are 0.138, 0.164, 0.556, 1.126, and 1.276, respectively. The strain difference at the symmetric positions increases continuously with the increase in the pressure difference on both the sides. Due to the influence of the unsymmetrical pressure acting on the pipe segment, the distribution and variation in strain are not uniform. The larger the pressure difference is, the less apparent the symmetry of the strain values in the segment is, making it more susceptible to damage.
## 4 Simulation Experiments
### Simulation Model and Parameter Settings  
The performance of the proposed technique is tested using the simulation model shown in Figure 3. The simulation model consists of four modules, which are introduced as follows. The GNSS signal settings module is used to determine the \(C/N_{0}\) and the dynamics of the GNSS signal. The compound signal received by the antenna array is produced in the signal generator module by adding the GNSS signal, interference, and noise. The compound signal is sent to the GNSS antenna array receiver module for processing. The tracking performance evaluation module is used to analyze the tracking error of the receiver.  
Figure 3: Block diagram of the simulation model.  
Figure 2: RMSE results using different estimation methods at different \(C/N_{0}\) conditions. **(a)** Gaussian condition. **(b)** Non-Gaussian condition.  
A four-element circular array with three equally spaced elements at half wavelength and a central element is chosen for performance validation. The GPS L1 C/A signal is considered as the signal of interest. The dynamics of the GNSS signal are assumed to have a constant Doppler frequency unless otherwise specified. A broadband (BB) interfering signal and a continuous wave (CW) interfering signal are injected from different directions at \(t=0.5\) s and \(t=1.2\) s, respectively. In addition to the situation where new jammers appear, the situation where existing jammers disappear from the scene is also considered. The BB and CW interfering signals disappear at \(t=1.8\) s and \(t=2.5\) s, respectively. The parameters used in the simulations are listed in Table 1.  
The existing phase compensation technique cannot be applied directly, and it is inserted into the architecture of the proposed technique for comparison. In this technique, the discriminator output is directly used as the phase bias estimate as described in Section 2.3. This technique is named the simple phase compensation (SPC) technique. The proposed technique is referred to as the robust phase compensation (RPC) technique. The PI algorithm is used for interference suppression. The adaptive array weight is calculated using the sample matrix inversion technique and is updated every 1 ms. The coherent integration time is set to 1 ms. The window size \(L\) for the RPC is set to 30.
## 2 System and Channel Model
### Channel Model  
We consider a composite optical channel model [30], where the channel power gain is proportional to the path loss, atmospheric turbulence, and pointing error. Specifically, the overall channel power gain \(I_{SR}\) can be calculated by  
\[I_{SR}=I_{SR}^{l}I_{SR}^{p}I_{SR}^{a}. \tag{3}\]  
Here, \(I_{SR}^{l}\) denotes the power gain of the path loss, \(I_{SR}^{p}\) denotes the power gain of the pointing error, and \(I_{SR}^{a}\) denotes the power gain of atmosphere turbulence following a gamma-gamma distribution. In particular, the PDF (probability density function) of \(I_{SR}\) is shown as  
\[f_{I_{SR}}(x)=\frac{\alpha_{SR}\beta_{SR}\epsilon_{SR}^{2}}{I_{SR}^{l}A_{SR} \Gamma(\alpha_{SR})\Gamma(\beta_{SR})}G_{1,3}^{30}[\frac{\alpha_{SR}\beta_{SR} }{I_{SR}^{l}A_{SR}}x]\epsilon_{S_{SR}^{2}}^{z_{SR}} \tag{4}\]  
Figure 1: Configuration of UAV-assisted FSO communication network.  
Here, \(G_{1,3}^{3,0}[.]\) denotes the Meiger-G-function, \(\alpha_{SR}\) and \(\beta_{SR}\) are parameters related to the atmosphere turbulence, while \(A_{SR}\) and \(\epsilon_{SR}\) are parameters related to the pointing error. \(I_{SR}^{l}\) is denoted by  
\[I_{SR}^{l}=\exp(-kL_{SR}), \tag{5}\]  
where \(k\) denotes the attenuation coefficient, and \(L_{SR}\) denotes the distance from the BS to the UAV.  
In terms of the second-hop links, following a similar analysis process, the PDF of \(I_{RD_{i}}\), \(i=1,\ 2,\ \cdots,\ M\) can be denoted by  
\[f_{I_{RD_{i}}}(x)=Q_{RD_{i}}G_{1,3}^{3,0}[\frac{\alpha_{RD_{i}}\beta_{RD_{i}}}{ l_{RD_{i}}^{l}A_{RD_{i}}}x_{i}]^{e_{RD_{i}}^{2}-1,\alpha_{RD_{i}}-1,\beta_{RD_{i}} -1}], \tag{6}\]  
where \(Q_{RD_{i}}\) denotes \(\frac{\alpha_{RD_{i}}\beta_{RD_{i}}e_{RD_{i}}^{2}}{l_{RD_{i}}^{l}A_{RD_{i}}^{l} \Gamma(\alpha_{RD_{i}})\Gamma(\beta_{RD_{i}})}\). Here, \(\alpha_{RD_{i}}\), \(\beta_{RD_{i}}\), \(\epsilon_{RD_{i}}\), \(I_{RD_{i}}\) and \(I_{RD_{i}}^{l}\) are the channel parameters for link from the UAV to sensor \(i\), \(i=1,\ 2,\ \cdots,\ M\), which have the same physical meaning as \(\alpha_{SR}\), \(\beta_{SR}\), \(\epsilon_{SR}\), \(I_{SR}\) and \(I_{SR}^{l}\) in the first-hop link. Then, the CDF of \(I_{RD_{i}}\) can be derived as follows:  
\[F_{I_{RD_{i}}}(x)=Q_{RD_{i}}xG_{2,4}^{3,1}[\frac{\alpha_{RD_{i}}\beta_{RD_{i}} }{l_{RD_{i}}^{l}A_{RD_{i}}}x_{i}]^{0,\epsilon_{RD_{i}}^{2}-1,\ \alpha_{RD_{i}}-1,\ \beta_{RD_{i}}-1,\ -1}]. \tag{7}\]
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 4 Interference Alignment in Cognitive Radio: Second Paradigm
### Water-Filling Techniques For IA in CR Networks
#### 4.2.1 Spatial Water Filling (SWF)
As the success of the SU communication depends on the availability of unused TOs, this work focuses on two very specific contributions. The first is a fast coarse sensing method that detects unused TOs, based on the eigenvalues of the received signal covariance matrix. Secondly, a more accurate sensing method based on the generalized likelihood ratio test (GLRT) is applied after coarse sensing to fine tune detection of the unused TOs. The proposed solution provides a significant throughput while causing no interference to the PU-Rx, and that the sensing detects the spatial holes of the PU network with higher detection probability.
## 2 Materials and Methods
Sample morphology and coatings' thickness were acquired using a field emission scanning electron microscope (SEM) Tescan Mira3 (Tescan Orsay Holding, Brno-Kohoutovice, Czech). Samples were observed in the top section after metallization. Cutting and tape tests were carried out according to the ASTM D 3359-09 standard [31] by making a grid incision with a specific cutter on the coated samples and then applying adhesive tape to cover the cut area. The test adhesive was vigorously removed and the involved area was inspected. According to the standard, the test passed if the area involved had detached flakes of coating at intersections less than 15%. The adhesion tests were carried out according to the ASTM C633 [32]. The test consisted of coating one face of a substrate fixture, bonding this coating to the face of a loading fixture, and subjecting this assembly of coating and fixtures to a tensile load normal to the plane of the coating. The tensile load to the assembly of the coating and fixtures should not permit eccentric load or bending moment to the specimen. A cyanoacrylate was employed as adhesive. Adhesion or cohesion strength was calculated as _Maximum load/cross-sectional area_. The adhesion strength of the coating was given a failure if rupture was entirely over the coating-substrate interface. The cohesion strength of the coating was validated if rupture was only within the coating. This test was performed at room temperature and at \(-30\)\({}^{\circ}\)C.  
Icing wind tunnel (IWT) tests were performed in the CIRA IWT facility, which is a closed-loop circuit, refrigerated wind tunnel with three interchangeable test sections and one open jet configuration, whose main mission is to perform icing tests. The cloud generation for icing conditions simulation is carried out by the spray bar system, which is able to generate water droplets with diameters (median volumetric diameter--MVD) and concentrations (liquid water content--LWC) covering nearly all the envelope as prescribed by the CS-25 Appendix C for both continuous and intermittent cloud conditions. Two samples A and two coatings, i.e., 1 and 2, were contemporary tested by dividing each sample into two parts, one of which was coated with a coating of A1 and A2, and the other left uncoated as a reference (AR) (see Figure 1). The two samples were mounted with the two reference sides in opposite positions, as shown in Figure 1b, in order to minimize the effect of a possible cloud inhomogeneity. To have a three-dimensional shape of the accumulated ice overlapped with the neat airfoil, 3D laser scan reconstructions were performed (Figure 2). Three-dimensional ice measurements and reconstruction were performed using an all-purpose portable measuring arm system integrated with a laser scanner for fast 3D surface data capture (ROMER 8525-7). Innovmetric PolyWorks was employed to get data points, align the ice point clouds to the 3D model of the corresponding \"clean\" sample, and, finally, generate a polygon mesh. Figure 1: Different views of samples A mounted in the IWT facility; (**a**) front view, (**b**) lateral view, and (**c**) top view.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 1 Introduction
### Collaborative Inhibition and Collaborative Facilitation in Computer-Mediated Communication
Another research [24] pointed out that individuals with higher levels of openness and conscientiousness tend to choose traditional FtF teaching methodologies compared to those who prefer online settings. Finally, one study showed that even if people slightly differ in their personality traits, these differences did not explain the variance in learning within online or FtF contexts [21]. With regard to the state anxiety, several studies indicated that people can learn in online settings if they have low levels of anxiety [21; 25; 26]. The effects of gender grouping in group performance during collaborative learning settings have been explored in some studies [27]. So far the results have been mixed. Some researchers showed that single-gender grouping works better than mixed-gender grouping, since participants of single-gender grouping are more focused on the purpose of the task [28; 29]. On the contrary, other contributions highlighted that in mixed-gender groups individuals' knowledge tends to diverge more than in single-gender groups, and these processes should be important elements for positive results in collaborative learning [30]. For some authors, having a majority of females in a group is one of the best predictors of successful group outcomes [31], whilst other researchers suggest that having a female-only and a balanced-gender group are two types of good grouping interventions for CSCL [32]. Little research focused on the effects of gender grouping on female and male participants' learning in VEs, and these studies showed controversial results [32]. Some studies indicated that male participants in mixed-gender groups significantly outperformed compared to female participants in mixed-gender groups [33] or to males in single-gender groups [32]. Females in single-gender groups performed significantly better than those in mixed-gender groups [30] or no statistical difference was found in females among different gender grouping conditions [32]. These different results in the effects of gender grouping on female and male participants learning could be explained by the individual attitudes in collaborative learning. Female participants tended to feel more comfortable in single-gender groups [32; 34] or gender-minority grouping [32]. In contrast, male participants in single-gender groups tended to feel significantly more uncomfortable than males in mixed-gender [32; 34], gender-balanced, and gender-majority grouping [32].
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 3 Results
### Testing the Okada-GA Inversion Model Using the Observation Data
#### 3.2.2 The 2018 Lombok Earthquake Event  
The Okada-GA inversion model was also tested on the 2018 Lombok earthquake's time series data. Due to limited observation data, the time series data used for testing were 1 Hz real-time kinematic-GPS data recorded at one observation point during this major earthquake. As in the testing experiment for the 2011 Tohoku Oki earthquake described previously, the displacement of the 2018 Lombok earthquake was first calculated. The displacement results were then used as input data for the Okada-GA inversion model.  
In this testing experiment, the predetermined parameters used for the inversion, i.e., hypocenter location, strike, and dip, referred to the focal mechanism information published by the International Seismological Centre (ISC) [39], as can be seen in Table 7.  
The estimation results of the parameters length, width, rake, and slip were 73 km, 73 km, 60\({}^{\circ}\), and 0.171 m, respectively. Figure 9 shows the comparison between the GA displacements and the referenced permanent displacement of the 2018 Lombok earthquake case. The GA displacement and the referenced displacement are represented by the blue vector and the black vector, respectively. The red pentagram indicates the epicenter of the 2018 Lombok earthquake.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Lat. (\({}^{\circ}\))** & **Long. (\({}^{\circ}\))** & **Depth (km)** & **Strike (\({}^{\circ}\))** & **Dip (\({}^{\circ}\))** & **_Mw_** \\ \hline \(-8.33\) & 116.24 & 18 & 262 & 70 & 6.9 \\ \hline \end{tabular}
\end{table}
Table 7: The predetermined source parameter of the 2018 Lombok earthquake event used for testing, and the referred _Mw_.  
Figure 8: Magnitude updates per unit of elapsed time (t) in the 2011 Tohoku Oki earthquake for each tested fault model calculated by the proposed method.  
From the source parameters estimation results, the \(Mw\) estimation was calculated using Equations (7) and (8). The estimated \(Mw\) result was 6.898, with the \(t_{pd}\) = 64 s and the \(t_{GA}\) = 28.263 s, using the hardware specification previously described. Hence, the total elapsed time required by the Okada-GA inversion model proposed in this study to complete the source parameters and magnitude \(Mw\) estimation (\(t_{est}\)) of the 2018 Lombok earthquake event was 92.263 s.
# Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
## 1 Introduction
The findings demonstrate that the cutting force coefficients derived through the calculation of the cutting power are in agreement with that determined by the dynamometer. Significant attention has recently been paid to the establishment of optimization as well as predictive models to understand the impact of machining parameters on cutting force, where artificial intelligence techniques are used as an alternative to standard approaches such as milling, drilling and grinding. A hybrid optimization algorithm based on ANN and a genetics algorithm (GA) was utilized to model the surface roughness and cutting force in a milling machine in [16]. [PERSON] et al. presented a hybrid algorithm based on adaptive particle swarm optimization, a least squares algorithm, and a support vector machine (APSO-LS-SVM) to monitor and predict tool wear in the drilling process as reported in [17]. In addition, the monitoring of tool condition in the grinding process based on a support vector machine (SVM) and a genetics algorithm was reported in [18]. Furthermore, ANN was utilized for surface quality control in machining systems based on multisensor data fusion [19]. Similarly in [20], the ANN model was used to monitor surface quality in a taper turning CNC machine. In this work, we present the use of Gaussian process regression (GPR) in estimating the cutting force in the turning process. As far as we know, the literature has insufficient detail regarding the usage of GPR for estimating the cutting force in the turning process. Furthermore, the technique of GPR has not been examined before for modeling and predicting cutting force values in a turning process. The aim of the current study is to assess the precision of GPR in the modeling of experimental results for turning AISI 4340 alloy steel. The suggested GPR technique is discussed in the following section. The findings of the simulation provide the expected outcomes and demonstrate the predictive precision of GPR in contrast with SVM and ANN methods