esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology, Fundamentals, and Materials
### Optimization Alternatives: Improvement Strategies and New Technologies
Standardized systems, platforms, processes, and interfaces comprise the crucial infrastructure for easier implementation of Industry 4.0 [34]. Industry 4.0 targets the implementation of interconnecting, smart, and self-controlled structures of processes and systems [16]. For the successful implementation of Industry 4.0 transformation, three core and nine fundamental technologies are required to be part of the entire system [35]. For these technologies, the capabilities that they provide are analyzed for each of them: adaptive robotics, CPS, additive manufacturing, cloud technologies, virtualization technologies (virtual reality (VR) and augmented reality (AR)), simulation, data analytics and artificial intelligence (AI), communication and networking, real time location systems (RTLS) and radio-frequency identification (RFID), cyber-security, sensors and actuators, and mobile technologies [36]. For the research performed, this model, with twelve Industry 4.0 technologies and their related capabilities [36], is selected as basic framework for the integration in the conceptual model. In this context, recent findings of the literature have shown that the industry varies in the benefits expected by those technologies for industrial performance which indicates that companies should think systemically about the implementation of such technologies in order to achieve a higher maturity level of Industry 4.0 [4].
## 2 Material and Methods
### Small-Angle X-ray Scattering (SAXS) Analysis  
Small-angle X-ray scattering (SAXS) analysis was carried out to evaluate the polymorphism of the lipid mixtures. Diffractograms were obtained on the D1B-SAXS1 beamline at the Brazilian Synchrotron Light Laboratory (LNLS, Campinas, Brazil). Measurements were performed at room temperature, using a silicon-W/B4C toroidal multilayer mirror, collimated by a set of slits defining a pinhole geometry, at a wavelength of k = 1.499 A and detected on a Pilatus 300 k detector (Dectris Ltd., Baden-Dattwil, Switzerland). The sample-to-detector distance was 814 mm, covering a scattering vector \"q\" (q = (4\(\pi\)/\(\lambda\))sin\(\theta\)) and ranging from 0.1 to 4.0 nm, where 20 is the scattering angle. A standard silver behenate powder was measured to calibrate the sample-to-detector distance, detector tilt and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Samples** & **Carvacrol (mg)** & **Stearic Acid (mg)** & **Beeswax (mg)** & **Carnauba Wax (mg)** \\ \hline SA 10\% & 0.10 & 0.90 & — & — \\ SA 25\% & 0.25 & 0.75 & — & — \\ SA 50\% & 0.50 & 0.50 & — & — \\ BW 10\% & 0.10 & — & 0.90 & — \\ BW 25\% & 0.25 & — & 0.75 & — \\ BW 50\% & 0.50 & — & 0.50 & — \\ CW 10\% & 0.10 & — & — & 0.90 \\ CW 25\% & 0.25 & — & — & 0.75 \\ CW 50\% & 0.50 & — & — & 0.50 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of the binary mixtures (%, m/m).  
direct beam position (at room temperature). From the total scattering intensity, the parasitic scattering produced was subtracted.
## 3 Results  
Leg press (pre: 258.6 \(\pm\) 53.7 kg, post: 355.9 \(\pm\) 57.1 kg) and half-squat (pre: 149.5 \(\pm\) 28.7 kg, post: 191.8 \(\pm\) 25.4 kg) 1-RM were significantly increased after training (\(p\) < 0.001). The T CSA was significantly increased after the training intervention (pre: 63.4 \(\pm\) 12.8 cm\({}^{2}\); post: 72.4 \(\pm\) 18.1 cm\({}^{2}\); \(p\) < 0.001). Considering the four heads of quadriceps, significant increases were observed for CSAs of VL (pre: 18.3 \(\pm\) 3.6 cm\({}^{2}\); post: 22.3 \(\pm\) 5.4 cm\({}^{2}\); \(p\) < 0.001), VM (pre: 11.1 \(\pm\) 3.5 cm\({}^{2}\); post: 12.1 \(\pm\) 3.8 cm\({}^{2}\); \(p\) = 0.037) and VI (pre: 23.7 \(\pm\) 5.1 cm\({}^{2}\); post: 27.8 \(\pm\) 6.0 cm\({}^{2}\); \(p\) < 0.001). No significant changes were observed between pre- and post-training values for RF CSAs (\(p\) = 0.485). Significant deferences were observed between the percentage changes in RF CSA and that of VM, VM, VI and T (\(p\) < 0.05; Figure 1).  
Figure 1: Pre- to post-training percentage changes (%) of Vastus Lateralis, Rectus Femoris, Vastus Medialis, Vastus Intermedious and Total Quadriceps Femoris cross-sectional areas. With (*) denoted the significant differences between pre and post-training cross-sectional areas absolute values for each muscle separately as well as for the Total Quadriceps Femoris (\(p\) < 0.05). Small letters denote statistically significant differences between the marked groups (where VL = Vastus Lateralis, RF = Rectus Femoris, VM = Vastus Medialis, VI = Vastus Intermedious, T = Total Quadriceps Femoris).  
Figure 2: Correlation plots (**A**) and Bland-Altman 95% limits of agreements plots (**B**) for the comparisons between the training induced changes (%) of quadriceps femoris cross-sectional area, and those of Vastus Lateralis (**I**), Rectus Femoris (**2**), Vastus Medialis (**3**) and Vastus Intermedious (**4**).
## 2 Materials and Methods
### Electrochemical Measurements  
The determination platform of this experiment was performed the CHI-660C electrochemical workstation. The carbon paste electrode modified by the ion imprinted membrane was used as the working electrode, the calomel electrode was used as the reference electrode, and the platinum electrode was used as the counter electrode. Electrochemical experiments were implemented on the basis of the optimized conditions. Prior to the experiment, the working electrode was recombined in 5.0 mL UO\({}_{2}\)\({}^{2+}\) standard solution for 20 min, followed by washing with doubly distilled water to remove the surface impurities and immersed in 5.0 mL 1 mol L\({}^{-1}\) (pH = 6.0) 2-morpholino-ethanesulfonic acid (MES) buffer solution and 0.1 mol L\({}^{-1}\) KCl supporting electrolyte. Then, DPV was executed in the potential range from 0.0 V to \(-\)0.5 V with a potential increment of 20 mV, the pulse width of 170 ms, the pulse amplitude of 50 mV and the scanning rate of 25 mV s\({}^{-1}\).  
Electrochemical impedance spectroscopy (EIS) was employed to characterize the charge transfer characteristics and electrochemical performance of different modified sensors, which were gauged using 1.0 mmol L\({}^{-1}\) K\({}_{3}\)Fe(CN)\({}_{6}\)/K\({}_{4}\)Fe(CN)\({}_{6}\) (1:1) as an electroactive probe in 0.1 mol L\({}^{-1}\) KCl electrolyte with a bias potential of 0.25 V and a frequency range of 0.1-10\({}^{5}\) Hz with a signal amplitude of 5 mV.  
The influence of the supporting electrolyte such as HAc-NaAc, sodium citrate, Tris-HCl, MES(2-(N-Morpholino) ethanesulfonic acid hydrate) and phosphate acid-sodium phosphate on the peak current intensity of the sensor surface was investigated. The effect of pH value on the electrochemical determination of 1.5 \(\times\) 10\({}^{-7}\) mol L\({}^{-1}\) UO\({}_{2}\)\({}^{2+}\) solution was studied in the range of 4.0-9.0. To that end, the prepared electrodes were inserted into the solutions with different pH values where they were incubated for 10 min at a constant stirring speed and then measured.  
Repeatability: Six tests were performed using the same electrodes MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE for 0.2, 0.15, and 0.1 \(\mu\)mol L\({}^{-1}\) UO\({}_{2}\)\({}^{2+}\) solutions, respectively; the reproducibility of the sensors was evaluated by measuring the response signals of the electrochemistry of the six sensors in the above uranyl solutions under the same time conditions.  
Stability: The prepared sensors were stored at dry room temperature for 30 days and the signals were detected at five-day intervals.
## 4 Data-Driven CIM Construction
### Data-Driven CIM Construction Process
#### 4.2.1 Data Acquisition  
In the construction process of CIM, data collection represents a critical initial phase. Leveraging the diverse technological approaches, a wide range of urban data types can be gathered, including spatial data, demographic data, transportation data, and environmental data. These datasets form the essential informational foundation for the development of CIM systems. During the data collection process, it is imperative to first clarify the specific data requirements of the city and align the objectives of CIM with these needs [16; 55]. For instance, the specific demands of transportation, energy, and environmental sectors in smart cities should constitute the core focus of data collection to ensure that the data can effectively support the subsequent applications.  
In the inventory stage of existing data, the initial step involves conducting a comprehensive evaluation of the city's current data sources, encompassing multiple channels such as government agencies, enterprises, sensor networks, and satellite imagery. By assessing the data quality, completeness, and accessibility, gaps and deficiencies in the data can be identified, enabling the formulation of targeted data collection strategies. Additionally, it is essential to establish unified data standards and interoperability protocols to ensure the seamless integration and effective utilization of data from heterogeneous sources. Multiple technologies are frequently employed in the data collection process, and the integration of BIM and GIS concepts often serves as a pivotal approach to advancing the urban model development [28; 60; 61].
## 3 Antenna Design
### Analysis of MIMO Antenna Parameters
\[CCL=-\log_{2}[\det(\Phi_{k})], \tag{19}\]  
\[\Phi_{k}= \begin{bmatrix}\rho_{11}&\rho_{12}\\ \rho_{21}&\rho_{22}\end{bmatrix}  \tag{20}\]  
\[\rho_{ii}=\Big{(}1-\Big{(}\big{[}S_{ii}|^{2}+\big{|}S_{ij}\big{|}^{2}\Big{)} \Big{)},\quad\text{and} \tag{21}\]  
\[\rho_{ij}=-\Big{(}S_{ii}^{*}S_{ij}+S_{ij}^{*}S_{ij}\Big{)}, \tag{22}\]  
where \(\Phi_{k}\) indicates the correlation matrix at the receiving antenna. Figure 21c depicts the simulated CCL of the proposed MIMO antenna. It recorded the specified limit for the proposed MIMO configuration. A comparison between the CCL curves with and without introducing the MTM unit cell. So, it has a value of 0.006 and 0.840, respectively. It can be confirmed that the proposed design of graphene plasmonic MIMO configuration delivers a higher transmission data rate in any scattering environment. Figure 22 illustrates the representation of the far-field radiation pattern component of the E and H plane at 3.5 THz. The E and H plane radiation patterns are represented in terms their components. The proposed graphene plasmonic MIMO antenna radiation patterns are illustrated in the presence and the absence of the MTM unit cell. The proposed MIMO configuration exhibits a little change in the E and H planes as shown in Figure 22. At last, the E and H patterns indicate that no significant difference between the main lobe pattern with and without the Dumbbell-structure MTM unit cell. Figure 22: Far-field radiation of Ant. C with and without MTM unit cell **(a)** E-plane and **(b)** H-Plane.
## 2 Related Work
### AE Methods for Anomaly Detection  
Due to the fact that defects are rare, the auto-encoder, which is an unsupervised learning method, has lately been widely employed to identify them. Since it is difficult to identify the defect features in the vibration signal of a rotating machine in real time, a deep learning approach with a deep auto-encoder (DAE) is proposed in [7]. The approach is used to diagnose faults in electric locomotives. In addition, a technique for detecting wind turbine (WT) faults is presented in [8]. As a result of the uncertainty and temporal dependency of the measurement noise, the WT employs a denoising auto-encoder (DAE) to detect defects. Therefore, the auto-encoder is utilized to detect faults in a variety of areas.  
Failure detection and diagnosis are critical components in ensuring the safety of industrial processes. In [9], they use an auto-encoder to detect rare faults and long short-term memory (LSTM) to identify various types of faults. The auto-encoder is trained with normal data before being used to detect anomalies, and the predicted defect data are utilized to determine the defect type using LSTM. In [10], a simulation using electrical impulses to determine normality and defects is used. In addition, in [11], a LSTM model is used to detect cavities in impact-echo time series data acquired by non-destructive testing.
## 5 Experimental Validation
### Test Bed  
Figure 11 shows the test bed used in this paper. Two squirrel cage IMs whose main characteristics are given in Appendix A were tested, one in healthy conditions the other with a rotor broken bar fault. The broken bar fault was created by drilling a hole in one bar, in the junction of the bar with the end ring, as can be seen in Figure 12. This way to produce the bar breakage avoids damaging the magnetic circuit of the rotor and enables an easy verification of the complete disconnection between the bar and the end ring. To cover a wide scenario of industrial situation, the motor under test has been feed, alternatively, through variable speed drives (VSDs) of two different brands (ABB model ACS800 and Siemens model M440) with up to four different control strategies (scalar, scalar with slip compensation, field oriented control (FOC) and direct torque control (DTC)) and direct on-line (DOL) through an autotransformer. A permanent magnet synchronous machine (PMSM) is used as a mechanical load controlled by a drive ABB model ACSM1-04 AS-024A-4. The test bed is controlled using a programmable logic controller (PLC) and a system control and data acquisition (SCADA) system which allows to perform the test in an automatic way and to repeat accurately the same conditions with different motors under test. A digital oscilloscope model Yokogawa DL750 has been used to acquire the currents during the different performed tests.  
In this case, the test bed has been used for detecting broken bars fault, as in [1; 3; 22], but it can be easily adapted to detect other faults such as inter-turn short circuits, bearing damages or eccentricities. Indeed, other IMs with different faults can be coupled in the test bed and the tests set can be performed automatically.  
Figure 11: Test Bed used to obtain the experimental signals.
## 5 Protection Against Vampire Attacks in Routing
### Route Discovery Based on AHP  
Once security information has been distributed around the network, the step that comes next is to exploit this information to discover the optimal routes with the help of the analytic hierarchy process (AHP) [49]. AHP is one of the many choices of multi-criteria decision analysis (MCDA) methods, which are initially developed to help make optimal decisions (in this paper, this decision is about picking the best route) while taking multiple concerns (for example, energy efficiency and security) into consideration. There are many candidates other than AHP, but none of them, even AHP itself, are perfect and cannot be applied to every problem.  
The \"utility function\" (see more details in [50]) of each route, defined in this paper, is hard to construct since vampire attackers still deliver the packets eventually. Hence, in a sense, the energy consumed by attackers cannot be treated as \"entirely\" wasted. Furthermore, as earlier mentioned in Section 3.1, the extra energy consumed by vampire attacks is associated with a series of random parameters, its volume varies a lot and the exact number is difficult to determine, making it even more difficult for us to construct the utility function. The authors of [49] suggest that AHP is particularly helpful when a decision maker is having problems in constructing a utility function.  
As shown in Figure 5, we can then set a goal of figuring out the optimal route based on multiple criteria. The top-level in the figure is the goal of the decision, the second level of the hierarchy addresses the criteria under consideration, the lowest level shows the available choices (in this paper, they are all the possible routes). Afterward, the scores(or so-called priorities) of different possible routes can be determined based on pairwise comparisons between different criteria preset by a decision maker.
## 3 Small-Signal Stability Analysis  
The dynamic model of droop control has been widely discussed [27]. In this section, a small-signal model is derived for the proposed controller in which the total virtual impedance \(X_{V}\) is considered. By considering small disturbances around the equilibrium point defined by \((\Delta\delta e,\ E,\ V)\), the active power equation can be linearized around this point; thus, the expression shown in Equation (4) is obtained:  
\[\Delta P(S)=\left(\frac{\partial p}{\partial X_{V}}\right)\Delta X_{V}(S)+ \left(\frac{\partial p}{\partial\delta}\right)\Delta\delta(S). \tag{4}\]  
This last equation can be expressed in terms of \(k_{px}\) and \(k_{p\delta}\) as presented in Equation (5):  
\[\Delta P(S)=k_{px}\Delta X_{V}(S)+k_{p\delta}\Delta\delta(S). \tag{5}\]  
Similarly, the small disturbances around the equilibrium point are defined by \((\Delta\delta e,\ E,\ V)\), and the reactive power equation can be linearized around this point as expressed in Equation (6):  
\[\Delta Q(S)=\left(\frac{\partial Q}{\partial X_{V}}\right)\Delta X_{V}(S)+ \left(\frac{\partial Q}{\partial\delta}\right)\Delta\delta(S). \tag{6}\]  
This last equation can be expressed in terms of \(k_{qx}\) and \(k_{q\delta}\), as shown in Equation (7):  
\[\Delta Q(S)=k_{qx}\Delta X_{V}(S)+k_{q\delta}\Delta\delta(S), \tag{7}\]  
where the term \(\Delta\) indicates the small-signal deviation of the variable around the equilibrium point, and theses variations are expressed as shown in Equations (8)-(11). Furthermore, the variations of total virtual impedance \(\Delta X_{V}\) in Equation (9) include the changes in the variable virtual impedance \(\Delta X_{m}\) and the changes in the fixed virtual impedance \(\Delta X_{Vf}\) (which is considered in this study as zero during the steady-state operation):  
\[\Delta W=-k_{p}\Delta p, \tag{8}\]  
\[\Delta X_{V}=\Delta X_{Vf}\ +\Delta X_{m}, \tag{9}\]  
\[\Delta P=k_{px}\Delta X_{V}+k_{p\delta}\Delta\delta, \tag{10}\]  
\[\Delta Q=k_{qx}\Delta X_{V}+k_{q\delta}\Delta\delta. \tag{11}\]The four terms--\(k_{px}\), \(k_{p\beta}\), \(K_{qx}\), and \(k_{q\beta}\)--are calculated around the equilibrium point, which represents the sensitivity of the power flow with the virtual impedance. Thus, the partial derivative of the active power based on virtual impedance is obtained as shown in Equation (12).
## 2 System Model
### Limited Feedback Method with a Two-Stage Precoder
Based on the quantized CDI of the dimension-reduced effective channel of the \(g\)-th group, i.e., \(\hat{\mathbf{H}}_{g}^{\text{eff}}=\left[\hat{\mathbf{h}}_{g,1}^{\text{eff}},...,\hat{\mathbf{h}}_{g,K_{g}}^{\text{eff}}\right]\in\mathbb{C}^{s_{g}\times K _{g}}\), the BS constructs the inner precoder of the \(g\)-th group, i.e., \(\mathbf{V}_{g}=\left[\mathbf{v}_{g,1},...,\mathbf{v}_{g,K_{g}}\right]\), where a ZF beamforming vector of user \(k\) in the \(g\)-th group, i.e., \(\mathbf{v}_{g,k}\) is obtained as  
\[\mathbf{v}_{g,k}=\frac{\mathbf{\mathcal{A}}_{g,k}\hat{\mathbf{h}}_{g,k}^{\text {eff}}}{\left\|\mathbf{\mathcal{A}}_{g,k}\hat{\mathbf{h}}_{g,k}^{\text{eff}} \right\|}, \tag{21}\]where \(\mathbf{\mathcal{A}}_{g,k}\triangleq\mathbf{\Lambda}_{g,k}^{\perp}\left(\mathbf{\Lambda}_{g,k}^{ \perp}\right)^{\dagger}\in\mathbb{C}^{s_{g}\times s_{g}}\) is the null-space of the quantized CDI of the effective channels of all other users in group \(g\), where \(\mathbf{\Lambda}_{g,k}^{\perp}\) is a \(s_{g}\times(s_{g}-K_{g}+1)\) submatrix consisted by orthonormal column vectors, which is obtained from the SVD of \(\hat{\mathbf{\mathrm{H}}}_{g,-k}^{\mathrm{eff}}\) such as  
\[\hat{\mathbf{\mathrm{H}}}_{g,-k}^{\mathrm{eff}}=\left[\mathbf{\Lambda}_{g,k},\ \mathbf{\Lambda}_{g,k}^{\perp}\right]\mathbf{\Xi}_{g,k}\mathbf{\mathrm{L}}_{g,k}^{\dagger}, \tag{22}\]  
where \(\hat{\mathbf{\mathrm{H}}}_{g,-k}^{\mathrm{eff}}\) is the quantized CDI of the effective channels of all other users in group \(g\), i.e., \(\hat{\mathbf{\mathrm{H}}}_{g,-k}^{\mathrm{eff}}=\left[\hat{\mathbf{\mathrm{h}}}_{g,1}^ {\mathrm{eff}},...,\hat{\mathbf{\mathrm{h}}}_{g,k-1}^{\mathrm{eff}},\hat{\mathbf{ \mathrm{h}}}_{g,k+1}^{\mathrm{eff}},...,\hat{\mathbf{\mathrm{h}}}_{g,K_{g}}^{ \mathrm{eff}}\right]\)[11; 29].
# Overview on Different Types of Solar Cells: An Update
## 5 Thin-Film Solar Cells
### Future Prospects and Commercialization of Thin-Film Technologies  
Based on the analysis, the world thin-film solar cell market size [265] was valued at $11.3 billion (in 2020) and is expected to rise to $25.3 billion (by 2030). Several factors such as the declination of solar price, high electricity demand, and increased solar-cell capacity have contributed to the growth of the market Cadmium telluride films dominate the solar cell market, followed by amorphous silicon and copper indium gallium selenide films. Thin films have been deposited on metal, glass, and plastic. The obtained materials (film thickness) were 20 times lighter when compared with crystalline silicon wafers. In the future, thin-film solar panels could be implemented in the residential industry due to lower installation costs.  
Polycrystalline thin-film solar cells could be the most promising candidates for solving the energy production problem due to their low cost in large-scale industrial production and their improved power conversion efficiency. The time-stability should be maintained (within 25 years) in hot, hard conditions. Thickness reduction is needed to optimize the internal resistance (solar cell device) and minimize electrical power loss. In the CuInSe\({}_{2}\)-based solar cells, efficiency could be improved by reducing the recombination current. Non-hazardous CZTS films could be used to replace CIGS films (indium and gallium are rare). The precise tuning of the band gap (CZTS films) could be carried out by adding some impurities (Ag, Na, Sb). In CdTe-based solar cells, the performance of solar cells could be improved by adding a buffer layer (reducing the shunt-resistance problem).