esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The probability that there are no customers in the system is given by the following:  
\[P_{0}=(1+\sum_{i=1}^{s}\frac{\rho^{i}}{i!}\frac{\rho^{s}}{s!}\sum_{j=s+1}^{N}( \frac{\rho}{s})^{j-s})^{-1} \tag{2}\]  
where  
* P\({}_{0}\) is the probability that there are no customers in the system;
* \(\rho\) is the traffic intensity, defined as \(\rho=\frac{\lambda}{\mu}\);
* \(\lambda\) is the arrival rate of customers;
* \(\mu\) is the service rate per server;
* s is the number of servers;
* N is the maximum system capacity (including waiting and service). Probability of \(n\) customers in the system (Pn). The probability of having exactly \(n\) customers in the system is given by the following:  
\[\mathrm{Pn}=\left\{ \begin{array}{ll}\frac{\mathrm{p}^{n}}{\mathrm{n}^{2}}\; \mathrm{P}_{0}&\text{ if }1\leq\mathrm{n}\leq\mathrm{s}\\ \frac{\mathrm{p}^{n}}{\mathrm{s}^{\mathrm{s}}\mathrm{p}^{n-1}}\;\mathrm{P}_{0}& \text{ if }\mathrm{s}+1\leq\mathrm{n}\leq\mathrm{N}\\ 0&\text{ if }\mathrm{n}>\mathrm{N}\end{array} \right. \tag{3}\]  
where  
\(\bullet\) \(\mathrm{Pn}\) is the probability of having exactly \(n\) customers in the system. \(\bullet\) \(\mathrm{Average\) waiting time in the queue (Wq). The average time a customer spends in the waiting line waiting for a service is given by the following:  
\[\mathrm{Wq}=\frac{\mathrm{L_{q}}}{\lambda(1-\mathrm{P_{n}})} \tag{4}\]  
where  
\(\bullet\) \(\mathrm{Wq}\) is the average waiting time in the queue;  
\(\bullet\) \(\mathrm{L_{q}}\) is the average number of customers in the waiting line;  
\(\bullet\) \(\mathrm{\lambda}\) is the arrival rate of customers;  
\(\bullet\) \(\mathrm{P_{n}}\) is the probability that the system is full. \(\bullet\) \(\mathrm{Average\) time spent in the system (W). The average time a customer spends in the system (including waiting and service time) is given by the following:  
\[\mathrm{W}=\mathrm{Wq}+\frac{1}{\mathrm{\mu}} \tag{5}\]  
where  
\(\bullet\) \(\mathrm{W}\) is the average time a customer spends in the system;  
\(\bullet\) \(\mathrm{Wq}\) is the average time spent waiting in the queue;  
\(\bullet\) \(\mathrm{\mu}\) is the average service rate per server. \(\bullet\) \(\mathrm{Average\) number of customers waiting in line for a service (Lq). The average number of customers waiting for a service is given by the following:  
\[\mathrm{Lq}=\sum_{n}^{\mathrm{N}}\mathrm{(n-s)P_{n}} \tag{6}\]  
where  
\(\bullet\) \(\mathrm{Lq}\) is the average number of customers in the waiting line;  
\(\bullet\) \(\mathrm{P_{n}}\) is the probability of having exactly \(n\) customers in the system. The data generated was organized in Microsoft Excel, detailing both input parameters and output performance metrics, including queue intensity, system utilization, queue length, delays, and the probability of an idled server. This data was then imported into MATLAB 2018 for ANN model development. In an ANN-PSO model, the PSO algorithm optimizes ANN weights and biases by iteratively refining the network parameters based on minimizing the prediction error.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Water Storage Estimation Method  
Lakes are one of the most important natural ecosystems, being highly valued for a wide range of ecosystem services. According to the ecosystem services classification proposed by the Millennium Ecosystem Assessment (MA), a lake ecosystem provides provisioning services, regulating services, cultural services, and supporting services (Figure 6). We focused on the water provisioning ecosystem services of lakes in this study, and water storage is an essential assessment component.  
Figure 5: The trend of population and gross domestic product (GDP) growth in the state of Minnesota.  
[PERSON] et al. [75] proposed a new approach for estimating the above-water volume of wetland depressions in the Prairie Pothole Region (PPR) using a contour-tree method with DEMs derived from light detection and ranging (LiDAR) data. The above-water volume (\(V_{AW}\)) of each depression was calculated based on a statistical analysis of the LiDAR DEM cells enclosed by the depression boundary contour:  
\[V_{AW}=(Z\times C-S)\times R^{2} \tag{1}\]  
where \(Z\) is the elevation of the depression boundary contour; \(C\) is the number of cells enclosed by the depression boundary contour; \(S\) is the summation of elevation values of all cells enclosed by the contour; \(R\) is the spatial resolution of the LiDAR DEM.  
We adopted the same method to calculate lake water storage based on the bathymetric data cells with a 5 m spatial resolution. As shown in Figure 7, the lake water was divided into cuboids based on pixels of DEM, and the above-water volume was calculated by the summation of the volumes of these cuboids. The total lake water storage can be calculated as follows:  
\[V_{lk}=\sum_{i=1}^{m}(EC_{i}\times N_{i}-S_{i})\times R^{2} \tag{2}\]  
where \(V_{lk}\) is the total lake water storage; \(m\) is the number of lakes, which is equal to 1290 in this study; \(EC_{i}\) is the contour elevation of lake \(i\); \(N_{i}\) is the number of cells enclosed by the contour of lake \(i\); \(S_{i}\) is the summation of bathymetric elevation values enclosed by the contour of lake \(i\); \(R^{2}\) is the pixel area of bathymetry cells, which is equal to 25 m\({}^{2}\) in this paper.  
Figure 6: Lake ecosystem services defined by the Millennium Ecosystem Assessment, including provisioning, regulating, supporting, and cultural services.
## 3 Physical Layer Design  
The physical layer of the proposed wireless communication architecture consists of the mm-wave communication channel, the antennas, and the transceiver. In this section, we attempt to present the advancements in understanding and design along the various aspects of the physical layer for inter-chip communication in multi-chip systems.
## 4 Load Sharing and Link-Failover Systems  
The study focuses on the load-sharing capabilities and link-failover performance of EIGRP and OSPF, two dynamic routing protocols essential for optimizing network efficiency and reliability.
## 2 Materials and Methods
### Description of Cellular Beams and Selection of Variables for Training ML Models
The flanges were discretized by 12 elements, whereas the discretization of the web along the span were performed with element size of about H/20 (Figure 2). In addition, a pinned condition was adopted for discretization of the web-flange connection to form a safe lower bound which was independent of the flange size and type of weld, as recommended by [PERSON] and [PERSON] [11]. The critical buckling load, or the output of the ML models, varied from 26.40 N/m to 1361.70 N/m (mean value of 225.68 N/m and standard deviation of 80.87 N/m). It should be pointed out that initial imperfections corresponding to geometry or fluctuations of material and residual stresses were not studied [36]. The data used in this work were scaled into the range [0, 1] in order to minimize bias within the dataset between variables. For the \(j\)th variable, the expression for scaling is defined as follow:  
\[V_{j}^{scaled}=\frac{V_{j}^{raw}-n_{j}}{m_{j}-n_{j}}, \tag{1}\]  
Figure 2: Visualization of the FE mesh showing the approximate element size along the span and at the flange surface. where scaling parameters \(m\) and \(n\) are also indicated in Table 1. In general, ML related problems often use this technique to transform the dataset into a uniform range to reduce numerical bias [9; 37]. It should be noted that a reserve transformation could be deduced for converting data from the scaling space to the raw one. The relationship between input and output variables is plotted in Figure 3. It is observed that no particular correlations are found between two inputs or between inputs and the output.
## 2 Numerical Simulation and Parameter Selection
### Strip Footing  
Simulating strip footing was possible using a beam element [23; 24; 25] that possessed both bending stiffness (EI) and axial stiffness (EA). The material model for this element was linear elastic, and its characteristics were dependent on specific parameters. These parameters included the modulus of elasticity for concrete (E), the cross-sectional area (A), the moment of inertia (I), and the characteristic strength (Fcu) of 25 N/mm\({}^{2}\). Additionally, the footing was subjected to a distributed load of 100 kN/m\({}^{2}\), which was transmitted to the soil. Table 3 presents the material properties associated with the strip footing.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Name** & **Value** & **Unit** \\ \hline Soil dry unit weight & \(\gamma\)d & 18 & kN/m\({}^{3}\) \\ \hline Young’s modulus & E & 30,000 & kN/m\({}^{2}\) \\ \hline Poisson’s ratio & \(\upsilon\) & 0.33 & — \\ \hline Cohesion & c & 36 & kN/m\({}^{2}\) \\ \hline Friction angle & \(\varphi\) & 0 & degree \\ \hline Dilatancy angle & \(\psi\) & 0 & degree \\ \hline Interface reduction factor & R & 1 & – \\ \hline \end{tabular}
\end{table}
Table 1: The properties of the medium clay.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Name** & **Value** & **Unit** \\ \hline Normal stiffness & EA & \(5\times 10^{6}\) & kN/m \\ \hline Flexural rigidity & EI & \(2\times 10^{4}\) & kN/m\({}^{2}\)/m \\ \hline Equivalent thickness & d & 0.208 & m \\ \hline Weight & w & 1.2 & kN/m\({}^{2}\) \\ \hline Poisson’s ratio & \(\upsilon\) & 0.3 & – \\ \hline \end{tabular}
\end{table}
Table 2: The material properties of the pile.
## 2 Passive Wake-Up Radio
### Discussion of the Passive Wake-Up Radio Parameters
#### 2.4.2 Input and Output Voltage Requirements  
The \(V_{in}\) column describes the minimum voltage required to power up the wake-up radio circuit. It is a critical design factor that needs to be considered for varying RF environments. Low turn-on voltages can be designed for weak RF environments while only high \(V_{in}\) voltage values can work well in strong RF environments. The \(V_{out}\) is the output DC voltage obtained from the harvester circuit. This voltage charges the energy saver so that sufficient power is available to trigger the interrupt, hence activate the main radio. The ratio of the minimum \(V_{in}\) to V\({}_{out}\) is generally lower in RFID designs than the CMOS, meaning that lower input voltage can produce a higher output voltage in the RFID designs than in the CMOS designs. Lower wake-up time is preferred, as the designs with high wake-up time, such as [21], require 726 ms, which increases latency in the network.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 3 Analysis of Electric Field Noise Characteristics
### Simulation of the Motion State of a Wave Glider  
The connecting cables between the surface floating body and the underwater towing body of the wave glider are in tension and relaxation states, and there are different motion states between the surface floating body and the underwater gliding body under the two different states. To analyze the electric field characteristics of the wave glider under different motion states, a crane was used to fix the wave glider in the experiment to control the depth of entry into the water. The bow and stern ends of the surface floating body were connected by towing ropes, and its motion direction was controlled to simulate the motion state of the wave glider's surface floating body, as shown in Figure 4.  
Figure 4: Simulation of the motion state of a wave glider. (**a**) Tensioning the suspension rope, the traction rope is not under force, and the floating body on the water surface is in a semi-free state. (**b**) The suspension rope is loose, the traction rope is tensioned, and the floating body on the water surface shakes parallel to the dock. (**c**) The suspension rope is loose, the traction rope is tensioned, and the floating body on the water surface shakes perpendicular to the dock. (**d**) Loose suspension rope, loose traction rope, and floating body on the water surface shaking parallel to the dock. (**e**) Loose suspension rope, loose traction rope, and floating body on the water surface shaking perpendicular to the dock. (**f**) Floating body on the water surface shaking the water.  
As shown in Figure 4, the motion states (b), (c), (d), and (e) simulate the free movement of a floating body on the water surface. As a control group, by tightening the suspension rope on the surface of the floating body, (a) and (f) simulate the floating body in a semi-in-water and out-of-water state, control the relative motion between the surface floating body and the underwater towing body, and analyze the impact of the underwater towing body's rudder operation on the analysis of the electromagnetic characteristics.
## 2 Stability of the Equilibrium Point
### Stability Analysis by Linearizing around an Equilibrium Point
\right]\), and the trace of the Jacobian matrix is \(0\) and the determinant of the Jacobian matrix is \(g\), which is larger than \(0\). This indicates that we have a linear centre, and this linear centre indicates that the original nonlinear system is indeed also a centre because when \(b=0\) the mass-spring system is a conservative system, i.e., there is no energy being dissipated. The following vector field is plot as shown in Figure 8 to demonstrate the centre. The origin represents the angular position and angular velocity of the pendulum both equal to \(0\). The next circle that surrounds the origin represents the small-angle swing, and the one after the next circle represents the swing with a large angle, and so on and so forth, and this makes sense because there is no damping. With damping present, i.e., \(b\
eq 0\), the Jacobian matrix has become the following: \(J=\left[ \begin{array}{cc}0&1\\ -g&-\frac{b}{m}\end{array} \right]\), and the trace of the Jacobian matrix is \(-\frac{b}{m}\) and the determinant of the Jacobian matrix is \(g\), which is greater than \(0\). Based on the trace and the determinant information, we can conclude that the equilibrium point is stable. From above analysis, we can see that after linearizing around the equilibrium point, and based on the linearized result, we can conclude whether the equilibrium point of the original nonlinear system is stable or not. The linearization method works for the above case because the equilibrium point of the pendulum with damping is a spiral and without the damping case it is centre but the energy of the system is conserved. However, the linearization method does not work for the cases where the equilibrium point is centre, degenerated node, star, or non-isolated.
## 6 Discussion
### Cost-Effectiveness and Life-Cycle Considerations
[175], certain advanced damping systems can be recalibrated or upgraded without necessitating extensive structural alterations. This flexibility can prolong the effective lifespan of the damping system and enhance its long-term cost efficiency. In summary, the considerations surrounding the cost-effectiveness and life-cycle of supplementary dampers are complex and necessitate thorough analysis within the framework of specific project needs and regional seismic threats. Although the initial financial outlay may be considerable, the advantages of diminished damage, enhanced performance, and reduced long-term expenses frequently validate their use, especially in areas prone to significant seismic activity. As seismic design methodologies continue to advance, ongoing research and evaluations following implementation will further elucidate the long-term economic and environmental advantages of these cutting-edge damping systems.
## 5 Comparison  
In this section, we make a comparison with the conventional MFFP [1,2] in terms of the computational efficiency by comparing (1) system complexity and (2) time complexity. Here, \(|P_{A}|_{\text{MFFP}}\) represents the number of activity places needed by the MFFP method, \(|P_{PIP}|\) represents the number of \(P_{PIP}\) identified in one PN-based system, and \(|P_{A}|_{\text{IMFFP}}\) represents the number of activity places needed by the IMFFP-2 method. Further, O(n?)\({}_{\text{MFFP}}\) and O(n?)\({}_{\text{IMFFP}}\) represent time complexity under MFFP and IMFFP-2, respectively. Firstly, based on Table 7 one can realize that there are 6, 11, and 16 variables (i.e., activity places) in Examples 1, 2, and 3, respectively, that need to be considered under the conventional MFFP control policy. However, there are just 4, 9, and 12 variables to consider since there are 2, 2, and 4 \(P_{PIP}\) identified in Examples 1, 2, and 3, respectively, under IMFFP-2.  
Figure 8: Controlled system of the Petri model in Figure 7.  
Figure 9: The third Petri net model of FMS.  
Firstly, from the viewpoint of system complexity, the computational efficiency of IMFFP-2 must be better than conventional MFFP since the number of variables of IMFFP-2 is much less than in the conventional MFFP method. On the other hand, according to the book _Introduction to the theory of computation_ (second edition) [55] and Wikipedia [56], the definition of time complexity for computer science is as follows: the time complexity of an algorithm can be expressed commonly by using big O notation \"O\", which excludes coefficients and lower-order terms. When expressed this way, time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For instance, if the time required by an algorithm on all inputs of size \(n\) is at most \(6n^{3}+2n^{2}+20n+45\) for any \(n\) (\(n\) is a variable), then obviously, there are four terms in total and the highest order term is \(6n^{3}\). Thus, the asymptotic time complexity is O(\(n^{3}\)). Therefore, the time complexity of [PERSON]'s MFFP method in Example 1 could be viewed as O(\(n^{6}\)). The time complexity of our IMFFP-2 is viewed as O(\(n^{4}\)) based on Table 7. Similarly, the time complexity of [PERSON]'s MFFP method in Example 2 is viewed as O(\(n^{11}\)), and our IMFFP-2 is O(\(n^{9}\)); the time complexity of [PERSON]'s MFFP method in Example 3 is O(\(n^{16}\)), and our IMFFP-2 is O(\(n^{13}\)). Accordingly, in Examples 1 and 2, the computational efficiency of the proposed IMFFP-2 is 100 times better than that of the conventional MFFP control policy. In addition, in Example 3, the computational efficiency of the proposed IMFFP-2 is 1000 times better than that of the conventional MFFP method.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 4 Experiments  
In this section, we evaluate the proposed UAS geopositioning approach using two variations of landmark matching approaches--ORB and SuperGlue. We use the pre-trained SuperGlue model without additional training. The experiments section is organized into several subsections. First, we discuss the in-house generated datasets and UAS geopositioning accuracy under several scenarios. This is followed by an analysis of the effectiveness of the Geographic Information System (GIS) [20] filter generated using OpenStreetMap [21]. We compare two matching schemes and validate our pipeline running in real-time. All experiments are coded and tested with a 12 GB NVIDIA TITAN X GPU. Finally, the geopositioning accuracy of the proposed approach is shown to outperform a common SLAM approach.
###### Abstract  
Orientation information of space debris is required to improve the orbital prediction accuracy for mitigation or elimination of a significant threat to not only human space activities but also operational satellites. Obtaining orientation information is currently achievable by applying photometry, adaptive optics (AO) and satellite laser ranging (SLR) technologies. In this study, a new method is proposed based on an echo laser pulse waveform (ELPW) for the orientation determination of space debris; its feasibility was also investigated by numerical simulations. Unlike the photometry and AO technologies available just under the sun-illumination condition and the SLR technology applicable only for cooperative targets, the ELPW is achievable by using a high power laser regardless of the above measurement constraints. A mathematical model is derived to generate the ELPW, and the beam broadening and spreading due to the atmospheric turbulence is taken into account. The Gaussian decomposition based on a genetic algorithm was employed to the ELPWs in order to analyze the orientation features. It is demonstrated from the numerical simulations that the ELPWs have distinctive shapes characterizing the orientation of space debris and therefore our approach was capable of providing orientation information.  
orientation determination; echo laser pulse waveform; space debris; debris laser ranging; Gaussian decomposition +
Footnote †: journal: Remote Sensing, 2020, 12, 1659; doi:10.3390/rs12101659
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 2 The Generation Mechanism of Wideband Harmonics
### Analysis of Resonance Generation Mechanism of LCL Filter  
The LCL filter can effectively eliminate high harmonic components in the inverter output current, but it is not an ideal low-pass filter. The current loop usually has a very high control bandwidth, so the current can be considered as following the reference, ideally, in the analysis of LCL frequency domain characteristics. Meanwhile, assuming that the voltage outer loop parameters are reasonable and therefore the DC side voltage is stable, Figure 3 describes the equivalent function between the inverter's output current and the voltage, where \(C\) denotes the filter capacitor, \(L_{1}\) and \(L_{2}\) represent the filter inductors on both sides of the capacitor, \(u\) is the output voltage, and \(i_{\text{g}}\) is the output current supplied to the grid by the inverter.  
The LCL filter can be formulated as:  
\[G_{\text{LCL}}(s)=\frac{1}{s^{3}L_{1}L_{2}C+s(L_{1}+L_{2})} \tag{2}\]  
where \(L_{1}\) and \(L_{2}\) are the filter inductances, and \(C\) is the filter capacitance.  
By setting \(L_{1}\) = 2.0 mH, \(L_{2}\) = 0.5 mH, and \(C\) = 100 uF, the frequency domain features of the LCL filter are shown in Figure 4. It can be seen that a resonant point exists at 1160 Hz, via which, even if the harmonic current amplitude of the corresponding frequency is small, the control stability of the current loop will be very poor due to the high gain from the LCL filter.  
Compared with Equation (2) and the ideal low-pass filter transfer function, it is not hard to find that the third-order pole caused by the filter capacitor \(C\) is responsible for the LCL filter's nonideal frequency domain characteristics. Therefore, reducing the effect of the high-order pole or constructing other closed-loop poles by changing the transfer function is an effective method for solving the resonance of the LCL filter. There are several locations in dual-loop control where additional control branches can be added. The method changing the location of the zero or pole in the characteristic equations of the control system is expressed in the next section.  
Figure 3: The closed-loop function of LCL.  
Figure 2: Inverter equivalent transfer function.