esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Model
### Outlet Chamber  
The flow in the outlet chamber can be divided into three parts: the input flow rate \(Q_{0}\), the flow rate \(Q_{01}\) to the control valve, and the flow rate \(Q_{04}\) to the load valve.  
The total flow rate comes from the downward moving pumping pistons in the discharge region. According to the kinematic relationship, the ideal flow of each pumping piston is given as follows:  
\[Q_{i}=\omega r_{p}A_{p}\tan\varphi\sin\theta_{i} \tag{2}\]  
Figure 2: Variable displacement mechanism. \(P_{0}\): the pressure of the discharge chamber; \(V_{0}\): the volume of the discharge chamber; \(Q_{0}\): the input flow rate; \(Q_{01}\): the flow rate to the control valve; \(Q_{04}\): the flow rate to the load valve; \(P_{1}\): the pressure of the chamber before the valve spool; \(V_{1}\): the volume of the chamber before the valve spool; \(P_{2}\): the pressure of the chamber behind the valve spool; \(V_{2}\): the volume of the chamber behind the valve spool; \(Q_{0}\): the flow rate of the control valve; \(Q_{2}\): the flow rate of the 2 th pipe inlet; \(P_{3}\): the pressure of the actuator chamber; \(V_{3}\): the volume of the actuator chamber; \(Q_{3}\): the flow rate of the 2 th pipe outlet; \(P_{4}\): the load pressure; \(Q_{4}\): the load flow rate; \(d_{i}\): the diameter of the \(i\)th pipe; \(l_{i}\): the length of the \(i\)th pipe; \(A_{p}\): the action area of the valve spool; \(A_{a}\): the action area of the actuator piston; \(m_{p}\): the mass of the valve spool; \(x_{a}\): the displacement of the actuator piston; \(k_{b}\): the stiffness of the valve spring; \(T_{a}\): the torque of the actuator piston. \(\varphi\): the swash plate angle.  
where \(\omega\) is the angular velocity of the cylinder block, \(r_{p}\) is the pitch radius of pumping piston and \(\theta_{i}\) is the angular displacement of the \(i\)th pumping piston.  
The kinematic flow fluctuation is the superposition of the flow ripples from different pumping pistons connected to the discharge port. Through Laplace transform, the flow rate to the load system can be expressed as follows:  
\[Q_{04}(s)=\frac{1}{Z_{04}}P_{0}(s) \tag{3}\]  
where \(Z_{04}\) is the equivalent impedance of the load valve and the pipe between the outlet chamber and load valve.  
Finally, the continuity equation of flow in the outlet chamber is given by the following:  
\[Q_{0}(s)-Q_{01}(s)-Q_{04}(s)=\frac{V_{0}}{K_{oil}}sP_{0}(s) \tag{4}\]  
Equation (4) is also can be written as a four-pole pattern as follows:  
\[\left[\begin{array}{c}P_{0}(s)\\ Q_{0}(s)\end{array}\right]=\mathbf{A}_{0}\left[\begin{array}{c}P_{0}(s)\\ Q_{01}(s)\end{array}\right],\ \ \ \text{where}\ \ \ \mathbf{A}_{0}=\left[\begin{array}{cc}1&0\\ \frac{V_{0}}{K_{oil}}s+\frac{1}{Z_{04}}&1\end{array}\right] \tag{5}\]
## 5 Transition from Bulk to Nanoscale
Bilayer exfoliated SnS has a thickness of 11 A [89], matching well to crystallographic data which give a value of 11.8 A [82], and structural models giving a value of 11.2 A for the bilayer which corresponds to the 8-atom unit cell [28]. The Brillouin zone has also been determined [90; 91]. Density functional theory calculations by [PERSON] et al. predict that interlayer spacing increases with decreasing layer number [92]. It was found that the \(a\)/\(b\) ratio moves to 1 as the 2D limit is approached; the Raman and reflectivity properties are also affected by thickness. It was also determined that the dielectric constant increases with the layer number [92]. Liquid-phase exfoliation and centrifugation of SnS has shown that because the SnS nanosheets are thinned and separated, a band gap of 1.65 eV is obtained, matching well with the HSE VASP calculations [1]. A theoretical study using first principle calculations with density functional theory (DFT) was conducted by [PERSON] et al. to determine the optoelectronic properties of single layer, double layer, and bulk SnS. It was found that the optoelectronic properties of SnS vary significantly with respect to the number of layers and the separation between them: the calculated band gaps were found to be 2.72 eV, 1.57 eV, and 1.07 eV for single layer, double layer, and bulk SnS, respectively, with values increasing with tensile strain along the layer stacking direction [28]. The minimum direct band gap was 2.72 eV, located at \(\Gamma\) (along the \(\Gamma\)-Y and \(\Gamma\)-Z directions the direct band gaps were 2.93 eV and 2.77 eV, respectively). These large band gaps suggest that single layer SnS is transparent for the longer wavelengths in the visible region of the electromagnetic spectrum. The electron effective masses were found to reduce as the number of layers increased from single to double layer to bulk. Tensile strain also increases electron effective mass for double layer and bulk SnS [28]. Figure 7 shows the odd-even quantum confinement effects for SnS at different layer thicknesses. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **E\({}_{\text{KS}}\)** & **\(\Delta_{\text{xc}}\)** & **E\({}_{\text{g}}\)(\({}^{\text{d}}\))** & **E\({}_{\text{g}}\)(\({}^{\text{d}}\))** & **\(\overline{\text{m}}  
Figure 6: **(I)** Band structure of monochalcogenides at bulk, monolayer and bilayer [80]; **(II)** band structure of single layer, double layer and bulk SnS **(a–c)** with corresponding Brillouin zones (**d–f**) [28]. **(I)** Adapted with permission from _Phys. Rev. B_, **2015**, \(8\), 085,406; **(II)** adapted with permission from _J. Appl. Phys._, **2013**, _23_, 233507. Figure 7: Odd–even quantum confinement effects for SnS at different layer thicknesses [93]. Adapted with permission from _J. Phys. Chem. C_, **2016**, _39_, 22663–22669.
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
## 1 Introduction
Owing to the complex small-scale turbulence structures in the supersonic boundary layer, the deflection angle and correspondingly drifted offset of the beam varied obviously and randomly, and the distribution probability of photons is redistributed. The result shows that the subsonic and supersonic boundary layer would decrease \(\sim\)35.8% and \(\sim\)62.5% of the secure key rate respectively. With the increase of speed \(v\), the key-rate curve is obviously jittering, and the QKD performance is continuously reduced. Our work provides a theoretical guidance towards a possible realization of high-speed airborne QKD.
## 4 Experimental Results  
The CO and ICO algorithms are evaluated in this section to show their performance for the parameter estimation of three types of PV models: SDM, DDM, and PVMM. The SDM and DDM tests were conducted on silicon solar cells with 57 mm diameters (RTC France) to collect current-voltage data [41]. Moreover, under 1000 W/m2 irradiance, a PV module (Photo Watt-PWP 201) with 36 polycrystalline PV cells was used [41]. Experimental data were used to estimate the parameters of the PV models using a variety of algorithms. The maximum and minimum limits for each parameter of the PV model are given in Table 2[16].  
Moreover, two recently developed algorithms, PGJAYA [6] and SEDE [14], as well as eight well-known original algorithms (i.e., DE [33], PSO [34], GA [35], TLBO [36], JAYA [37], SSA [38], WSO [13], and GWO [39]) were chosen to validate and verify the effectiveness of the CO and ICO for identifying the PV parameters. A maximum number of 50,000 function evaluations was assumed for all case studies. As in the original literature, the other parameters of the applied algorithms were maintained. The statistical analysis was performed by running each algorithm 30 times independently in MATLAB 2021b.
## 2 Materials and Methods
### Application of Morphological Operators  
Outdoor operations under natural light with complex backgrounds are particularly sensitive, as unpredictable noise and effects can make it difficult to achieve the desired goal. One of the most important methods for removing these noise and unpredicted factors is the use of morphological operators. These operators include a wide range of operators, such as opening, closing, filling holes, deleting border pixels, removing objects with pixels less than threshold values, thinning, thickening, and others. In the proposed segmentation algorithm, opening, closing, filling holes and deleting objects with a number of pixels less than 100 were used at different stages. This threshold value was selected with trial and error and with the consideration of not removing the apple pixels.  
The process of mathematical morphology in computational terms, consists of moving all the pixels of the image from left to right and from top to bottom in order to find isolated pixels, which are considered noise [12]. This noise is eliminated by applying erosion and dilation with the following equations:  
\[\textit{Open}=\ (B\ \otimes E)\ \oplus E \tag{1}\]  
\[\textit{Close}=\ (B\ \oplus E)\ \otimes E \tag{2}\]  
The operation open, disappears the fine points or fine structures and the operation close, fill the black holes of a certain size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Color Model** & **Number** & **Color Model** & **Number** & **Color Model** \\ \hline
1 & RGB & 7 & HSI & 13 & YPbPr \\
2 & HSV & 8 & Improved YCbCr & 14 & YUV \\
3 & YIQ & 9 & L*a*b* & 15 & HSL \\
4 & YCbCr & 10 & JPEG-YCbCr & 16 & XYZ \\
5 & CMY & 11 & YDbDr & 17 & Luv \\
6 & LCH & 12 & CAT02 LMS & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Various color models examined.
## 3 Design and Implementation Results
### Implementation Environments  
On the basis of the proposed architecture, the dual plane IoT system is constructed in this paper. The proposed architecture is applied to the MN-CSE. The hardware and software specifications are listed in Table 3.  
Since the proposed system is designed and implemented based on the Intel DPDK, it has a similar hardware dependency to the DPDK. The DPDK is designed to run on Intel x86-based, IBM Power PC and ARM processors and a wide range of NICs by multiple vendors such as Intel (Santa Clara, CA, USA), Emulex (Costa Mesa, CA, USA), Mellanox (Yorneam Illit, Israel), and Cisco (San Jose, CA, USA). That is, the proposed system is designed to run on commodity hardware instead of highly specialized hardware, and is independent of any hardware type. For generality and ease of maintenance, the proposed system is implemented in C language under a Linux CentOS in an x86 machine, and uses Intel CPU and common NIC, Intel I217-V (Intel, Santa Clara, CA, USA).  
On the other hand, the proposed system requires huge pages with more than 2 GB of memory for the large memory pool allocation used for packet buffers. Furthermore, in the proposed dual plane architecture, a CPU core (or thread) assigned to the data plane dedicates its CPU resource to busy-wait polling to avoid NIC interrupts and system calls. For this reason, an additional CPU core for the control plane is needed to handle control messages. However, these hardware requirements are acceptable for recent general computers. For high scalability, it is running under the VMware hypervisor (VMware, Palo Alto, CA, USA) so that the deployment or cloning of the system is easy.
## 1 Introduction
For example, a strong RFI source in the center of Spain was turned off on March 16 thanks to the efforts of the Direccion General de Telecomunicaciones (belonging to the Ministerio de Fomento, Spain), CDTI (Centro para el Desarrollo Tecnologico e Industrial), INSA (Ingenieria y Servicios Aerospaciales), and ESA (European Space Agency). In Section 2, a number of typical RFI scenarios are first presented to understand the nature of RFI, and the constraints the RFI detection and mitigation algorithms must face. In Section 3, a RFI detection and mitigation algorithm implemented to operate directly on L1b SMOS dual-polarization or full-polarimetric data is described. Finally, Section 4 presents the statistical analysis performed on more than 13,000 snap-shots.
## 3 EMS Selection and Description
### Selected Energy Management Strategies
#### 3.2.1 Conventional PFCS  
This control strategy involves adjusting the output power of the FC to follow the power demanded by the vehicle while adding or subtracting a charge proportion based on the deviation of the Battery State of Charge (SOC) from a target value. This target value is chosen based on battery performance and durability, and it relies on manufacturer data. The power that is demanded from the FC (\(P_{FC}\)) is described in the following expression:  
\[P_{FC}=P_{D,Veh}+\alpha_{chrg,disch}\cdot\Delta SOC \tag{1}\]  
\[\Delta SOC=SOC_{target}-SOC \tag{2}\]  
\[\alpha_{chrg,disch}>0 \tag{3}\]  
where  
* \(P_{D,Veh}\): power demand from the vehicle.
* \(\alpha_{chrg,disch}\): charge proportion.
* \(\Delta SOC\): SOC deviation.
* \(SOC_{target}\): target SOC for the battery pack.  
Attempting to match the instantaneous power demand from the vehicle while adding/subtracting a certain charge for the batteries makes this strategy less suitable for Fuel Cell Electric Vehicles (FCEVs), as fuel cells have restrictive high-efficiency zones and slow response times, making it difficult to regulate the system in an efficient and durability-compliant manner.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 5 Procedures to Calculate Modularity, Complexity and MLT
### Procedures for M(G) and PCI  
Firstly, let us have a model of manufacturing process, in which parts P1, P2, and P3 are processed by machines M1, M2, M3, M4, M5, M6, and M7, and scheduled as depicted in Figure 2.  
When applying \(M(G)\) indicator, at first, it is needed to create a visibility matrix of the given process model (see in Figure 3) to obtain \(Nj\) values.  
Figure 2: A model of manufacturing process producing parts P1, P2 and P3 by machines M1–M7.  
Then, process modularity is quantified using Equation (4) as follows:  
\[M(G)=\frac{7}{2\cdot\ln 2+3\cdot\ln 3+5\cdot\ln 5+4\cdot\ln 4+5\cdot\ln 5 +8\cdot\ln 8+8\cdot\ln 8}=0.118\]  
The procedure is repeated for all alternative structure models. Then, the model with the highest \(M(G)\) value represents the optimal manufacturing process structure among concurrent ones, from the modularity point of view.  
In order to apply the _PCI_ indicator, at first, it is necessary to enumerate partial complexities of the machines. Then, their sum represents the overall complexity of the process. Let us demonstrate enumeration of partial complexity for machine M1 by the applying Formula (8). This machine is processing two parts (P1 and P3), and therefore the partial complexity of M1 has to be enumerate in this way:  
\[PCI_{M1}=p_{11}\log_{2}p_{11}+p_{13}\log_{2}p_{13}=\frac{1}{5}\log_{2}\frac{1}{5 }+\frac{1}{5}\log_{2}\frac{1}{5}=0.928\;bits\]  
where, e.g., \(p_{11}\) is the probability that part P1 is just being proceeded by machine M1. It must be recognized that when this part is processed on machines in serial manner, then \(p_{11}\) equals \(1/5\), because the number of machines in serial connection equals five.  
The partial complexities can be enumerated in the two ways shown in Figure 4. Due to this fact, it is possible to create a graph of the complexity distributions, where the resulting cumulated complexities are the same for both the views. It can easily be proved that _PCI_ value for the given process model is 7.04 bits.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
###### Abstract  
Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.  
metallic surface; autoencoder; convolutional neural network; defect detection +
Footnote †: journal: Applied Sciences
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
## 6 Economic Evaluation
The SVC is slightly ahead of the considered grid, but in the case of a weak grid, its performance deteriorates, while the STATCOM solution guarantees better performance in terms of rotor angle stability in the case of a weak grid or a case characterized by a low percentage of synchronous generation (e.g., high penetration of renewable energy sources); the higher cost of STATCOM compared to SVC is justified by the widest power range and the ease of allocation and maintenance; the modernization of the excitation systems is also a very effective solution thanks to the direct impact on the generators, for which an economic incentive could be created if the provision of voltage regulation services were remunerated. Investing in the power grid by doubling important lines is not only crucial to avoid congestion problems, but also to significantly improve grid stability. This improvement would also allow for the installation of more RES generation units [10; 11; 22], which can have a significant impact on stability if they are not technologically predisposed to participate in regulation. This paper provides a preliminary examination of the impact of new inverter-based technologies on improving the security of modern power systems. Future developments will include the following topics: the impact of the considered technologies on transient stability and frequency stability in a case study IEEE with a large number of RES generators, especially wind and photovoltaic plants; the effects of load models (e.g., exponential model, ZIP model, etc.) are carefully studied. As the photovoltaic plants are mainly installed in the distribution networks, this event will change the behavior of mixed loads in the transmission networks; from an economic point of view, new market products, e.g., ancillary services, need to be developed to encourage not only utilities to provide services based on new technologies but also TSOs to procure cost-effective resources to manage power systems safely. Finally, an important step to confirm and generalize the results obtained in this work for the IEEE 9-bus network is to apply this analysis to real power systems. However, many characteristics of power systems can affect the final results (e.g., generation technologies, transmission network characteristics, etc. ), so it is important to perform a comparative analysis between two power systems in this case as well. For example, comparing the behavior of the power systems of two major Italian islands (i.e., Sicily and Sardinia) can be a useful test. This step is under development [23]. Conceptualization, G.M.T. and D.S. ; Data curation, G.M. and S.L. ; Investigation, G.M. and S.L. ; Methodology, G.M.T. ; Software, G.M. and S.L. ; Supervision, G.M.T. and D.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
# 3-D SRIM Simulation of Focused Ion Beam
## 4 Results  
### Simulation Results  
The SRIM program was improved for the application-oriented model by editing the script file with the spatial positions and landing angles of all ions at the surface of Si substrate. In order to research the sputtering performance systematically, the point-like incident beam, the Gaussian distribution beam with the same beam energy and beam spot and the application-oriented model were simulated with the same energy and dose. Three-dimensional scattering distributions in the substrate and the relative lateral range cross-sections (XOY) for 30 keV Ga\({}^{+}\) are shown in Figure 5. The point-like mono-energetic Ga\({}^{+}\) beam is clustered at the top of the substrate as shown in Figure 5a. While in Figure 5c,e, the beam distribution becomes tapered with the depth, which is consistent with the actual condition. The actual nanowires are \"V\" shape since the sputtered atoms are redeposited on the sidewalls in etching. Judging from Figure 5b,d,f, these three incident beams have the same penetration depth of 280 A for Ga\({}^{+}\) implantation concentration, which is consistent with the measured parameters in the experiment [32; 33].  
Quantitative behaviors for three incident beams in Figure 5 were listed in Table 1. The longitudinal range is keeping 280 A as mentioned above and the longitudinal straggle of the point-like incident beam is just 1 A smaller than the other models. But the lateral projection, the lateral projection straggle, the radial range and the radial straggle differ a lot for different models. The application-oriented model has the largest radial range and the main cause may be that the model itself has the largest FWHM value. In order to verify the above conclusion, the radial range (RR) and radial straggle (RS) under different FWHM values with the new model were simulated as shown in Figure 6. Judging from  
Figure 4: Application-oriented incident model at the surface of the sample with 50 pA beam current and 12 mm working distance.  
Figure 6, RR and RS increase with FWHM value and become stable at about 75.5 nm where the 20-80% rise distances and the 12-88% rise distances are much smaller than FWHM value. Since the radial range depends on the original spatial position of the incident beam, the model with aberrations and the Coulomb force interaction is more applicable. Furthermore, ion-solid interaction under different beam energy and beam currents can be obtained by using the application-oriented model.