esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Objective Function of Developed Channel Estimation  
The implemented channel estimation model using deep learning greatly reduces the BER, RMSE, and MSE. The HSCN network with a hybrid RP-WHEVO algorithm is proposed for estimating the variables to predict the channel coefficient in the MIMO system at the transmitter side by reducing SNR. Variables such as \"hidden neuron count and count of the epoch\" of the autoencoder and LSTM network are optimized using the RP-WHEVO algorithm in order to improve the spectral efficiency by reducing RMSE, MSE, NMSE, and BER. The main intention of the proposed channel estimation scheme aided by a heuristic-based deep learning approach is given in Equation (24).  
\[K=\underset{\{VL_{l}^{AE},BS^{AE},VL_{2}^{LASTMN}BS_{d}^{LASTM}\}}{\text{ argmin}}\left(\begin{array}{c}0.5*(RMSE+MSE)\\ +0.5*BER+NMSE+\frac{1}{5E}\end{array}\right) \tag{24}\]  
In Equation (24), the terms \(VL_{l}^{AE}\) and \(VL_{t}^{LASTMN}\) denote the tuned neuron counts that are optimized in the range \([5-225]\) in both the autoencoder and LSTM network, and the terms \(BS_{d}^{AE}\), \(BS_{d}^{LASTM}\) denote the tuned count of epochs that are optimized in the range \([5-50]\) in both the autoencoder and LSTM network and the term \(SE\) denotes the spectral efficiency. The formula for computing the values of RMSE, MSE, NMSE, and BER are given by Equation (25), Equation (26), Equation (27) and Equation (28), respectively.  
\[RMSE=\sqrt{\sum\limits_{l=1}^{m}\frac{\left(XZ-VC\right)^{2}}{G}} \tag{25}\]  
\[MSE=\frac{1}{G}\sum\limits_{l=1}^{G}(XZ-VC)^{2} \tag{26}\]  
\[NMSE=\frac{\sum\limits_{l=1}^{m}\left(XZ-VC\right)^{2}}{\sum\limits_{l=1}^{m} \left(VC\right)^{2}} \tag{27}\]  
\[BER=\frac{\text{Re}_{bit}}{To_{bit}} \tag{28}\]  
Spectral efficiency is defined as the total amount of information transmitted per unit bandwidth. Here, the terms \(XZ\) and \(VC\) represent the predicted value and actual value, respectively. The term \(\text{Re}_{bit}\) denotes the number of bits at the receiver side and \(To_{bit}\) denotes the total number of bits.
# Dual-Band Light Absorption Enhancement in Hyperbolic Rectangular Array
###### Abstract  
The effect of dual-band light absorption enhancement in a hyperbolic rectangular array (HRA) is presented. The enhanced light absorption of the HRA results from the propagating surface plasmon (PSP) resonance, and a dual-band absorption with low and flat sideband level can be realized. The impedance theory is used to evaluate the absorption properties of the HRA, and shows that the input impedances of the HRA varied abruptly around the absorption bands to meet the impedance matching. The absorption spectra of the HRA can be estimated using the effective medium theory (EMT), and its accuracy can be improved as the number of film stacks is increased. The dual-band absorptions of the HRA are very robust to the variations of the width and the number of film stack. Potential application in refractive index sensing can be achieved by utilizing the two absorption bands.  
dual-band absorption enhancement; hyperbolic metamaterials; hyperbolic rectangular arrays; input impedances; refractive index sensing Article
## 2 Formulation of the Problem
### Governing Equations
#### 2.2.2 Particle Phase  
For each particle suspended in the liquid, a differential form of force balance equation is utilized to obtain the trajectory of solid phase. The interaction forces between fluid and solid phases contain the drag, Brownian, gravity, and thermophoresis forces. The dynamic equations of solid phase are  
\[\frac{dX_{p}}{dt}=V_{p} \tag{7}\]  
\[\begin{array}{c}\frac{dV_{p}}{dt}=\frac{18\mu_{f}}{\frac{d^{2} \rho_{p}c_{c}}{\text{Drag force}}}+\frac{g(\rho_{p}-\rho_{f})}{\frac{g(\rho_{p}- \rho_{f})}{\text{P}_{p}}}+\frac{\xi\sqrt{\frac{\pi S_{0}}{\Delta t}}}{\text{ Brownian force}}\\ -\frac{36\mu_{f}^{2}C_{b}(k_{f}/k_{p}+C_{t}+Kn)}{\frac{\rho_{f}P_{p}d^{2}_{p}(1 +3C_{m}Kn)(1+2k_{f}/k_{p}+2C_{t}Kn)}{\text{Therprophooretic force}}}\end{array} \tag{8}\]  
where subscripts of \"f\" and \"p\" demonstrate the liquid and solid phases, respectively. \(X\) and \(V\) indicate the location and velocity of the particles, respectively. Moreover, \(g\), \(k\), \(t\), \(\rho\), and \(\mu\) are gravitational acceleration, heat conductivity, time, density, and viscosity, respectively.  
\(C_{c}\) in drag force term denotes the Cunningham correction and is determined by  
\[C_{c}=1+\frac{2\lambda}{d_{p}}\Big{[}1.257+0.4e^{-(1.1d/2\lambda)}\Big{]} \tag{9}\]  
where \(d_{p}\) and \(\lambda\) are the particle diameter and fluid mean free path, respectively. Moreover, \(Kn\) in thermophoresis force terms is the Knudsen number. The constant values of \(C_{m}\), \(C_{t}\), and \(C_{s}\), are respectively allocated as 1.14, 2.18, and 1.17 [29]. \(\xi\) in Brownian force is the zero-mean, unit-variance independent Gaussian random numbers. Finally, \(S_{0}\) is the spectral intensity of Brownian force and is calculated as follows [30; 31]:  
\[S_{0}=\frac{216\
u K_{B}T_{f}}{\pi^{2}\rho_{f}d^{5}_{p}\Big{(}\frac{\rho_{p}}{ \rho_{f}}\Big{)}^{2}C_{c}} \tag{10}\]  
where \(K_{B}\) is the Boltzmann constant (=1.38 \(\times\) 10\({}^{-23}\) J K\({}^{-1}\)).  
The energy equation for the solid phase is presented as  
\[m_{p}C_{p}\left(\frac{dT_{p}}{dt}\right)=hA_{p}(T_{f}-T_{p}) \tag{11}\]  
where \(A_{p}\) is the particle surface area. Moreover, \(h\) is the heat transfer coefficient and is defined by [32]:\[h=\frac{k_{f}}{d_{p}}\Big{(}2+0.6 Re_{p}^{0.5}Pr_{f}^{0.3}\Big{)} \tag{12}\]  
where \(Pr_{f}\) and \(Re_{p}\) are the Prandtl number of the liquid phase and particle Reynolds number, respectively.
## 2 Experimental Setup  
The experimental setup for signal transmission and noise characterization is shown in Figure 1. For transmission, a pseudo-random bit sequence with word length of \(2^{15}-1\) and its negated pattern are generated by a pulse pattern generator (PPG) operating at 28 Gbaud. Decorrelation is performed by delaying the negated pattern by 84 bits. A two-bit digital to analog converter (DAC) with 19 GHz analog bandwidth is employed to convert the binary sequences into four-level pulse amplitude modulation signals, where an additional 77-symbol delay is introduced between the two outputs to drive an IQ modulator with 25 GHz 3 dB bandwidth to generate an optical 28 Gbaud 16 QAM. The transmitter external cavity laser (ECL) at 1550 nm has a linewidth of ~40 kHz. Polarization multiplexing (PolMux) emulation is performed after the IQ modulator to achieve 224 Gbit/s DP-16 QAM. An optical booster, an optical bandpass filter (OBPF) with bandwidth of 1.2 nm and a variable optical attenuator (VOA) are placed before the fiber link for OSNR adjustment.  
The transmission link is comprised of 200 km SSMF. First order pump at 1455 nm (PYL-1-1455-R, IPG Photonics, Oxford, MA, USA) is used to Raman amplify the signal which is divided into two parts 95% (backward) and 5% (forward) for distributed Raman amplification. An automatic gain controlled pre-amplifier EDFA with fixed output power is placed after the link. A coherent receiver with a ~40 kHz linewidth local oscillator (LO) and an 80 GSa/s digital storage oscilloscope (DSO) are used for detection. Signal demodulation is performed off-line with a DSP chain consisting of low pass filtering; chromatic dispersion (CD) compensation, timing recovery, polarization de-multiplexing and three different CPR methods before a bit-error-rate counter.  
For a better understanding of the signal impairments induced by the distributed Raman amplification, we also characterized the amplitude and frequency noise of the received signal with the use of the digital coherent receiver based on the method described in [28]. In this measurement the transmitter laser is unmodulated and transmitted over the Raman amplified link with different pump configurations.
## 2 Methodology
### Dynamic Allocation of Satellite Resources for Emergency Scheduling
#### 2.4.1 Initial Scheduling Plan of Satellite Resources
\begin{table}
\begin{tabular}{l|l} \hline \multicolumn{2}{l}{**Hybrid Genetic Tabu Search Algorithm**} \\ \hline \multirow{6}{*}{Input} & (1) & the maximum iteration number of genetic algorithm \(i_{\text{sp-max}}\); \\  & (2) & the maximum iteration number of tabu search \(i_{\text{ts-max}}\); \\  & (3) & the population size \(n\); \\  & (4) & the crossover probability \(p_{c}\); \\  & (5) & the mutation probability \(p_{m}\); \\  & (6) & the length of tabu table \(l\); \\  & (7) & the neighborhood size \(n\); \\  & (8) & the neighborhood function \(F(x)\). \\ \hline \multirow{6}{*}{Processing chain} & (1) Determine the chromosome coding method. Chromosome coding is a series of segments, each of which represents the mission scheduling for a satellite payload type. In each segment, the execution window of each mission is arranged by number to form the chromosome coding of this segment. The chromosome coding template is defined as \(u_{i}=\left\{t_{i},w_{i}^{q},st_{i}\right\}\), where \(w_{i}^{q}\) is the visible window of mission \(t_{i}\), \(st_{i}\) is the start time of execution, and the coding template is represented as a vector \(\
u\), where \(\
u=\left\{u_{1},u_{2},\ldots,u_{n}\right\}\). \\  & (2) Initialize the population. An initial population of size \(N\) is randomly generated. The initial generation \(t\) is set to \(0\), \(m\) chromosomes are randomly generated as \(x_{1}^{1},x_{1}^{2},\ldots,x_{t}^{m}\), and the optimal value is set as \(X_{best}\). \\  & (3) Calculate the value of the fitness function. The objective function is taken as the fitness function of chromosome, and the fitness function value \(f(x_{1}^{1}),f(x_{2}^{2}),\ldots,f(x_{t}^{m})\) of each chromosome is calculated in the population. If \(f(X_{best})<f(x_{1}^{i})\), \(i=1,2,\ldots,m\), then \(X_{best}=x_{i}^{i}\). \\  & (4) If \(t<i_{max}\), then \(t=t+1\), otherwise, the iteration ends. \\  & (5) Perform selection operation on the population. The route wheel selection method is used to select \(m\) chromosomes from the population as the parent of the next generation, and the probability of each chromosome being selected is \(P_{i}=f\left(x_{i}^{i}\right)\)\(\int\limits_{j=1}^{m}f\left(x_{1}^{j}\right)\), \(i=1,2,\ldots,m\). In the selection process, the best member of the previous generation is retained in the next generation. \\ \hline \end{tabular}
\end{table} 
Table 4: Processing chain for the hybrid genetic tabu search algorithm.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 6 Experimental Simulation Results
### Cable Type Sweep and Bandwidth Sweep  
For calculation of the primary and secondary parameters of the CYKY cable, the geometrical properties were used, i.e., the radius of the conductor and the distance between the centres of the cores available from the manufacturer. For the simulation, a lower background noise value was selected at \(-100\) dBm/Hz. In addition, the throughput is monitored only at a distance of 300 m. The rest of the input parameters coincide with the previous simulation as described in Table 10. The simulation output is once again the dependence of the band rate on the distance in Table 10. The next simulation is focused on the influence of the bandwidth on the application rate. The G.hn PLC module defines 25, 50 a 100 MHz bands. Table 10 also shows the PSD values defined for these bands. The maximum transmit power is defined in the simulator by ITU-T G.9964 and the OFDM parameters satisfy the ITU-T G.9960 recommendation.  
Throughput differences of the LV and MV cables is shown in Figure 9a.The point-to-point transmission rate for different frequency bands is shown in Figure 9b. According  
Figure 8: (**a**) Comparison of throughput with transmit PSD sweep; (**b**) Comparison of throughput with cable cross-section sweep.  
to the figure, the largest bandwidth reaches a transmission speed up to 500 Mbps, and for the 50 MHz band the speed is up to 330 Mbps. However, with increasing distance, the speed decreases rapidly, and at a distance of 200 m, the difference is less than 80 Mbps and gradually approaches the lowest bandwidth. This decrease is mainly due to the strong frequency dependence of the primary parameters on the transmission line. The results were reached by the experimental setup for the trend verification of the throughput. Because of this, it is difficult to compare these results with real equipment, especially due to inaccuracies in the consideration of attenuation on the simulated route.
## 4 Numerical and Anechoic Chamber Experiments
### Accuracy Verification  
To assess the effectiveness of deep learning models, the primary measure employed is the mean Average Precision (mAP). This measure is commonly applied in object detection research to depict the accuracy and recall patterns across different trained models [43]. The AP is computed through the difference-based AP evaluation technique, which represents the region below the precision-recall curve. The calculation formula is:  
\[\text{AP}=\left(\frac{1}{n}\sum_{(r\frac{1}{2},\frac{2}{r-r},\frac{n-1}{n},1)}P _{(r)}\right) \tag{32}\]  
\[\text{mAP}=\frac{1}{N}\sum_{k=1}^{k=N}\text{AP}_{k} \tag{33}\]  
where \(n\) means the entire number of detection instances. \(P(r)\) signifies the accuracy level at a specific recall rate of \(r\). \(N\) refers to the complete count of categories, and \(AP_{k}\) denotes the AP for category \(k\). \(mAP_{50}\) refers to IoU thresholds set at 50%. And mAP is short for \(mAP_{50}\) in this paper in the absence of ambiguity. Furthermore, auxiliary assessment metrics such as precision, recall, and the F1 measure are represented mathematically in the following manner:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{34}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{35}\]  
\[F1=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{36}\]  
where TP stands for true positives, indicating the case where an object is accurately identified as a positive example. FP denotes false positives, involving the erroneous identification of an object that is not actually positive. FN refers to false negatives, where a legitimate object is mistakenly classified as negative.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 3 Wind Farms Contribution in AGC  
The AGC is responsible for minimization of power system cost, due to operation of different power plants, removing the steady-state and transient frequency deviations and smoothing the tie-line power between areas. As stated earlier, this study investigates the last items--which are known as secondary frequency control or LFC.  
There is a vast body of literature on control strategies for wind farms to contribute in the LFC. Because of simplicity in structure and industry application of proportional integral (PI) controller, most of the studies have used the PI controller in VSWT structure to contribute to the secondary frequency control. However, a range of other control approaches can be examined for such contribution. Generally, over and undershoots, settling time, steady-state error and some other control indices are investigated to evaluate the performance of controllers in the LFC. A simple PI controller tuned by try and error for wind turbine was proposed for secondary frequency control in Reference [42], which was able to restore the frequency to its nominal value.  
Applied control strategies for wind farms can be classified into optimization methods, intelligent methods, and model predictive control methods for PI controllers in wind turbine structure. Furthermore, contribution to the AGC issues through the wind farm by the integration of energy storage systems and FACTS devices are investigated.
## 2 Diagnostics
### Diamond UV Sensor  
The UV diamond detector is a chemical vapour deposition (CVD) single crystal diamond (SCD) detector in a metal/intrinsic/boron doped configuration, placed outside the biological shielding wall, at 19 m from the plasma vessel. Its line of sight crosses the core of the plasma on the midplane at an angle, as shown in Figure 3. The sampling rate is 200 kHz, i.e., 20 times faster than spectroscopy. The emission of ELMs in the UV region and the high speed of these detectors make them suitable for accurate spectrogram analyses, as it will be discussed in the next sections, allowing better time-resolved information [36; 37]. The diamond sensitivity ranges from about 10 eV (225 nm) to a few keV and, since the actual line of sight of the diagnostic runs through the main plasma, this diagnostic can also detect phenomena characterizing the core of the plasma. The signal of UV in the pulse 92238 is described by the red line in the top plots of Figure 2.  
Figure 1: Pellet injection schematics (configuration post-2015).  
Figure 2: Shot 92238. In the first column, the ELM signals (**top** plot, beryllium in blue and diamond UV in red) and the pellet signal (**bottom** plot) are shown along the discharge (then, plasma disrupts). Second column shows a detail of the same signals for 48.0 s < t < 48.1 s.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network
### QKD-Based Data Disaster Recovery System  
Data disaster recovery refers to the establishment of a remote multi-point backup of data through the establishment of a disaster recovery center to improve resistance to operations during a disaster. According to the security level protection and grading standards of power companies, the security  
Figure 8: Electricity information collection based on QKD.  
Figure 9: Video conference system based on QKD.  
protection level of disaster recovery and backup services is S2A2G2 in the second level. The protection measure of traditional system is to deploy servers with different risk levels in different physical areas, and each area is safely isolated by a firewall. The protective measures are limited, and the level of protection is insufficient. Since the disaster recovery data involves sensitive information of most of the core power business, ensuring the safe transmission of core data of the power grid business is a point worth paying attention to in disaster recovery backup. In order to improve the security of disaster recovery services and rely on the secure distribution mechanism of quantum keys, this paper designs a disaster recovery service system based on the encryption and decryption of quantum key. Figure 10 shows a QKD-based disaster recovery backup system.
# Value of Information Based Data Retrieval in UWSNs
## 4 Path Planning Algorithms  
The path planning algorithms provide us with the sequence of nodes which the AUV will traverse for data collection. As suggested earlier, the sequence of node visitation by the AUV will affect the overall accumulated Vol. We propose a few different path planning algorithms for experimentation and analysis. The first algorithm is the Lawn-Mower path planner (LPP) which is based on minimizing total tour time. Then we have the Greedy path planner (GPP) which is based on greedily accumulating VoI. Greedy with Intermediate-Node-Visitation (GIPP) is a variant which is based on greedily accumulating Vol while also trying to minimize tour time. Hybrid path planner (HPP) is a combination of LPP and GPP. Hybrid with Intermediate-Node-Visitation (HIPP) is a combination of LPP and GIPP. The Random path planner (RPP) is used as a baseline algorithm for analysis purposes.
## 3 Methodology
### Field Investigation and Laboratory Sieving  
Numerous studies have demonstrated that the stability of landslide dams is closely related to their structural configurations. To investigate the relationship between the dam structure and the formation mechanisms of the Conaxue Co landslide dam, a comprehensive field investigation was conducted. Based on the particle size distribution characteristics of the dam materials, this study focused on a typical profile exposed by highway excavation on the dam body. An approach that integrates the sampling-area frequency method and traditional laboratory sieving method was employed to conduct a quantitative analysis of the dam structure.  
The sampling-area frequency method was utilized for rock blocks with particle sizes greater than 100 mm. The specific steps are as follows: First, the profile was divided based on the overall particle size of typical landslide dam profiles. The selected typical profile in this study was divided into two layers: the upper layer (Layer 1) and the lower layer (Layer 2; Figure 4A). Second, two 1.0 m \(\times\) 1.0 m square areas were identified on the Layer 1 and Layer 2 profiles, respectively. A grid with a side length of 0.1 m was used to statistically analyze the area of rock fragments larger than 100 mm within the square areas. This operation allowed for the determination of the area percentage of particles greater than 100 mm (Figure 4B).  
The traditional laboratory sieving method was employed for gravel and rock fragments with particle sizes less than 100 mm. The specific steps are as follows: First, fine particles smaller than 100 mm were sampled from Layer 1 and Layer 2, with a minimum sampling quantity of 8 kg for each layer. Second, the samples were sieved using screens with aperture sizes of 60 mm, 40 mm, 20 mm, 10 mm, 5 mm, 0.25 mm, and 0.075 mm (Figure 4C). The mass of the samples retained on each sieve was measured to determine the percentage of particles smaller than 100 mm in each layer. Finally, the results obtained from both methods were converted using the relationship between the area-frequency method and the weight-frequency method [20]. The area percentages of each particle group in the vertical profile layers were converted into weight percentages. This yielded the cumulative vertical particle grading distribution for the typical profile of the Conaxue Co landslide dam.  
Figure 3: The flowchart of the entire methodology.
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 7 A Comparative Analysis of Metric Access Methods  
This section presents an overview of the advantages and disadvantages of metric access methods. Based on both approaches, partitioning and non-partitioning, a short taxonomy can be introduced [222; 267; 268] divided in two categories.  
The first category does not use space partitioning, therefore, the family of the M-tree [53] generates a balanced incremental index. However, it suffers from the problem of overlap. An optimized version was proposed in [269]; this approach is the slim tree, which is based on the reorganization of the index to reduce overlap. Its disadvantage is the need to reinsert objects, which is costly.  
As for the second category, it is based on the partitioning of space and two sub-approaches are provided: the first uses ball partitioning, such as VP tree [2], MVP tree [263], etc.; the other approach uses hyper plane partitioning, such as GH tree [219], GNAT [246], etc. The VP tree is based on sharing using balls. The MVP tree is a generalization of the VP tree. The nodes of the MVP tree are divided into quantiles. The CD tree [237] is a type of index based on hyperplane partitioning. It has proven its effectiveness for a limited number of dimensions.  
Recently, a new technique has emerged MM tree [270; 271], that also utilizes the partitioning by balls. An extension of this technique has been extended: the onion tree [3]. The objective is to separate the last region to generate successive enlargements, however, the problem is not fully resolved.  
For more details, recall Figure 9 which presents a taxonomy, Tables 11-17 which provide an in-depth analysis, summarize the advantages and disadvantages, and application areas of metric indexing techniques.  
Figure 8: Taxonomy of tree-based indexing techniques.