esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The bit error rate (BER) in relation to the signal-to-noise ratio (SNR) serves as a widely recognized metric for assessing the performance of communication systems. The concept of SNR is so integral that many existing studies presume its definition to be understood, often omitting the specifics of its calculation in their simulations. Notably, the computation of SNR from the perspective of the transmitter yields distinct behaviors and outcomes compared to that from the receiver's side, particularly when the channel encompasses more than mere noise. Typically, research papers utilize the transmitter-side (or ensemble-average) SNR to benchmark the BER performance across various methodologies. Conversely, the receiver-side (or short-term) SNR becomes pertinent when prioritizing the receiver's performance. In the context of simulating the long-term evolution (LTE) downlink, applying both SNR calculation approaches reveals that the receiver-side SNR not only produces a significantly lower BER compared to the transmitter-side SNR but also alters the relative BER performance rankings among the channel models tested. It is deduced that while the transmitter-side SNR is apt for broad performance comparisons, it falls short in thoroughly examining the BER behavior of a receiver across varying SNR scenarios. Therefore, the transmitter-side SNR is useful when comparing the performance of the simulated system with other studies. Conversely, if the primary concern is the actual BER performance of the receiver, the receiver-side SNR could provide a more accurate performance assessment.  
long-term evolution; signal-to-noise ratio; ergodic channel +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Improved Performance of Li-ion Polymer Batteries Through Improved Pulse Charging Algorithm
## 1 Introduction
### CC-CV Charging Algorithm  
The CC-CV charging algorithm has three charging phases: (i) Trickle charging phase, (ii) constant current charging phase, and (iii) constant voltage charging phase. The trickle charging phase is initiated when the battery is significantly discharged and a small charge current, usually 1% to 10% of the full rated charge current, is used for charging. The constant current charge phase can have a charge current ranging from 50% to 320% of the full rated charge current to charge the battery [11]. During this charging phase, the voltage of the battery quickly rises, and measures must be taken to prevent overvoltage conditions. In the constant voltage charging phase, a decreasing charge current is used in this phase to prevent overcharge and overvoltage conditions, which can be detrimental to the LiPo battery. Charging time is prolonged in this phase due to the use of decreasing charge current. Figure 1 schematically illustrates the temporal CC-CV charging algorithm.  
The main drawbacks of the CC-CV charging algorithm are its longer charge time and decreased battery energy and charge efficiencies, which results in shorter battery runtimes. Increased battery runtime and shorter charging times, two important performance metrics electronic device consumers desire, can be addressed by the charging algorithm. Electrochemical-based modelling, where charging control algorithms are based on the movement of lithium ions, was developed [12; 13; 14] to address some of the shortcomings of the CC-CV charging algorithm. Other works [15; 16; 17; 18] have proposed the use of orthogonal arrays to search for the best charging patterns to increase charge efficiency and cycle life. The use of fuzzy logic and probabilistic techniques to adapt the battery charging based on the  
Figure 1: Schematic illustration of the constant current-constant voltage (CC–CV) charging algorithm.  
current state of charge (SoC) were also developed [6; 19; 20; 21; 22; 23]. Several authors [19; 24; 25] have proposed charging techniques based on the polarization of the battery, taking into account the temporarily evolving internal impedance of the battery. Other battery charging algorithms [18; 26; 27; 28; 29; 30; 31; 32; 33] use pulse and sinusoidal waveforms to charge the battery. Of these proposed charging algorithms, the most promising and simple is the pulse charging algorithm.
## 3 Simulation Results
### Taiwan Power (Taipower) System  
The proposed algorithm has been tested on a real power system, the Taipower system, which is a narrow and long power grid covering a distance of 400 km from north to south. Usually it is divided into three areas: north, center, and south. Different areas are connected together via a number of 345 kV transmission lines, as shown in Figure 11. This sample system is composed of 1821 buses and 3319 lines.  
In this work, many test cases on Taipower system have been studied. These include various system conditions and loading conditions. Moreover, generator \(Q\) limits are taken into account in each simulation. The comparisons with the selected cases listed in Table 1 are shown in Table 4. This table indicates that the proposed approach is capable of offering accurate \(\lambda^{\text{max}}\) as well as being time efficient  
Figure 11: Simplified diagram of Taipower system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(\boldsymbol{\lambda^{\text{max}}}\)} & \multicolumn{2}{c}{**VSM (\%)**} & \multicolumn{2}{c}{**CPU Time (s)**} \\ \cline{2-7}  & **Proposed** & **CPFLOW** & **Proposed** & **CPFLOW** & **Proposed** & **CPFLOW** \\ \hline
6 & 1.7207 & 1.7207 & 14.81 & 14.81 & 3.18 & 12.25 \\
7 & 2.8146 & 2.8146 & 48.78 & 48.78 & 3.15 & 12.99 \\
8 & 2.6706 & 2.6706 & 44.97 & 44
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Results and Analysis  
This study performed two sets of tests to validate the accuracy of the Delay-Doppler domain features and the multi-input target recognition model (TF-DD-CNN). In experiment 1, signals' Time-Frequency domain features (TF), Delay-Doppler domain features (DD), and joint features (TF-DD) were respectively used as inputs. The joint features of signals were realized using Feature Fusion in Figure 12. The VGG16 model, ResNet model [30], and GoogleNet model [31] were used for target recognition to verify the effectiveness of Delay-Doppler domain features for target recognition. The objective of experiment 2 was to validate the efficacy of the developed recognition model by using the TF-DD-CNN model to identify the target and assess the accuracy of various models.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 5 Results and Discussion
### Multi-Core Performance Testing and Scaling on Low-Cost FPGA  
The KNN parameters in the experiment were 15 nearest neighbors, 3240 samples of training data, and 100,000 symbols modulated in 16-QAM. The Ultra96 performed the KNN-based demodulator with 1 to 8 cores. Figure 8 shows the total processing time on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**16-QAM** & **Training** & **Cluster-Block** & **Number of** & **\% Reduction** \\
**Labels** & **Data** & & **Operation** & **\% Reduction** \\ \hline
0 & 204 & 0, 1, 4, 5 & 798 & 75.37 \\
1 & 195 & 1, 0, 2, 4, 5, 6 & 1209 & 62.69 \\
2 & 210 & 2, 1, 3, 5, 6, 7 & 1227 & 62.13 \\
3 & 195 & 3, 2, 6, 7 & 811 & 74.97 \\
4 & 178 & 4, 0, 1, 5, 8, 9 & 1231 & 62.01 \\
5 & 221 & 5, 0, 1, 2, 4, 6, 8, 9, 10 & 1834 & 43.40 \\
6 & 201 & 6, 1, 2, 3, 5, 7, 9, 10, 11 & 1838 & 43.27 \\
7 & 205 & 7, 2, 3, 6, 10, 11 & 1211 & 62.62 \\
8 & 222 & 8, 4, 5, 9, 12, 13 & 1220 & 62.35 \\
9 & 211 & 9, 4, 5, 6, 8, 10, 12, 13, 14 & 1812 & 44.07 \\
10 & 192 & 10, 5, 6, 7, 9, 11, 13, 14, 15 & 1849 & 42.93 \\
11 & 208 & 11, 6, 7, 10, 14, 15 & 1216 & 62.47 \\
12 & 187 & 12, 8, 9, 13 & 821 & 74.66 \\
13 & 201 & 13, 8, 9, 10, 12, 14 & 1212 & 62.59 \\
14 & 199 & 14, 9, 10, 11, 13, 15 & 1222 & 62.28 \\
15 & 211 & 15, 10, 11, 14 & 810 & 75.00 \\ \hline Total & 3240 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reduction of operations using the modified KNN algorithm. The table shows the application of the cluster-blocks on the real data of the experimental setup.  
Figure 7: Comparison of results between Matlab using 64-bits and the FPGA using 32-bits.  
the Ultra96 performing the implementations with a clock of 187.5 MHz. We achieved a speed-up of six times when using 8 instead of 1 core.  
Table 2 shows the hardware resources consumed. We can see the LookUp Tables LUTs, Flip flops, Block RAM, and DSPs in the table. In this experiment, the most used hardware resource was Block RAM, see Figure 9. In the design space exploration, we applied the optimization directives to reach the highest number of cores in the low-cost Ultra96.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N-Cores** & **BRAM** & **FF** & **LUT** & **DSP** \\ \hline
1 & 33.5 & 18,465 & 14,985 & 30 \\
2 & 53.5 & 23,238 & 19,229 & 59 \\
3 & 73.5 & 27,640 & 22,959 & 71 \\
4 & 93.5 & 31,586 & 27,004 & 93 \\
5 & 114.5 & 36,084 & 30,614 & 108 \\
6 & 134.5 & 40,250 & 34,422 & 127 \\
7 & 154.5 & 44,592 & 38,454 & 142 \\
8 & 174.5 & 48,116 & 41,794 & 157 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware resource consumption using a different number of cores on Ultra96 board.  
Figure 8: Total computation time for 100k input symbols using a different number of cores.  
Figure 9: The graph shows the percentage of resources consumed when increasing the number of cores on the Ultra96 board.
# Wavenumber-Frequency Analysis of Internal Aerodynamic Noise in Constriction-Expansion Pipe
## 2 Theoretical Background
### Acoustic Power Level  
The formula for the acoustic power of a fluid in a pipe is briefly introduced, because most of the existing empirical approaches consider the acoustic power level to be a critical parameter. The acoustic particle velocity in a duct with a circular cross-section can be described as follows [14]:  
\[u_{z}(r,\theta,z,t)=\sum_{m=0}^{\infty}\sum_{n=0}^{\infty}\frac{k^{+}z,m,n}{k _{0}\rho_{0}c_{0}}J_{m}(k_{r,m,n}r)\cos(m\theta)\,C_{+,m,n}e^{i\left(\omega t- k_{z,m,n}^{+}\right)} \tag{12}\]  
From Equations (6) and (12), the acoustic power is defined as follows:  
\[\begin{gathered} dW=I_{z}\,dA=pu_{z}dA\\ W=\int\mathcal{I}\,dA\ \ where\ \ \mathcal{\bar{I}}=\frac{1}{ \mathcal{\bar{I}}}\int\limits_{0}^{\mathcal{\bar{I}}}I_{z}dt\end{gathered} \tag{13}\]  
where \(I_{z}\) is the intensity of the propagated acoustic wave, \(A\) is the cross-sectional area, and \(\mathcal{\bar{I}}\) is the time-averaged acoustic intensity.  
After some algebra, the formula for the acoustic power can be written as follows:  
\[W=\sum_{m=0}^{\infty}\sum_{n=0}^{\infty}\frac{k^{+}z,m,n}{k_{0}\rho_{0}c_{0}} C_{+,m,n}^{2}\frac{1}{2}\int_{0}^{2\pi}\cos^{2}(m\theta)d\theta\frac{R^{2}}{2} \Big{\{}J_{m}(k_{r,m,n}R)^{2}-J_{m-1}(k_{r,m,n}R)J_{m+1}(k_{r,m,n}R)\Big{\}} \tag{14}\]  
Note that this is valid when the effects of the mean flow are negligible.
## 3 Epileptic Seizure Detection
### Proposed Epileptic Seizure Detection Scheme Based on Random Neural Network
Each frequency sub-band extracted from the raw data using DWT has been used separately to train the RNN-based epilepsy detection model. The number of neurons in the input, hidden, and output layers in all five experiments is 92, 10, and 1, respectively. The results from scenario 1 indicate which frequency sub-band contains the most discriminating features for efficient detection of epileptic seizures. In scenarios 2 and 3, as stated in Table 3, the BONN dataset has been used for classification using DWT and FFT, respectively. In scenario 2, the RNN-based classification model has 460 input  
Figure 3: EEG data pre-processing and feature extraction for RNN-based epileptic seizure detection. neurons, whereas in scenario 3, the input neurons are 92. In both instances, the number of neurons in the hidden and output layers is 20 and 1, respectively. Similarly, in scenarios 4 and 5, as illustrated in Table 3, the CHB-MIT dataset was used for classification using DWT and FFT in pre-processing step. The number of input neurons in scenarios 4 and 5 is 460 and 92, respectively. The number of hidden neurons is 20, with one output neuron.
###### Abstract  
With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and efficiency of information acquisition. In this paper, focusing on the problem of centralized multi-satellite scheduling, we establish a mathematical model of satellite scheduling with complex constraints of load and platform operation. We also propose a real-coding Population Incremental Based Learning (PBIL) algorithm to solve the multi-satellite scheduling problem. The real-coding format can greatly shorten the coding length compared to the traditional PBIL algorithm with binary coding so that the computational efficiency is improved. Additionally, we design a value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. Finally, we take some numerical examples to verify the real-coding PBIL algorithm for multi-satellite scheduling. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is also analyzed.  
c 2022, 11, 1147. [[https://doi.org/10.3390/electronics11071147](https://doi.org/10.3390/electronics11071147)]([https://doi.org/10.3390/electronics11071147](https://doi.org/10.3390/electronics11071147))
## 2 Materials and Methods  
### Materials  
Polyaniline--emeraldine salt (PANI) (density 1.36 g/cm\({}^{3}\) at 25 \({}^{\circ}\)C) is commercially available (Sigma--Aldrich), and it was purchased in the form of black powder (428,329-25 G, M.W. > 15,000 powder, 3-100 \(\upmu\)m particle size, \(\leq\)1% water content). PANI was used without further purification.  
Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\) (BST) (density in pellet form: 6.825 g/cm\({}^{3}\)) was used as the inorganic thermoelectric additive in PANI-BST composites. BST was synthesized following a previously reported methodology developed by employing the ball milling process [28]. Briefly, pieces of Bi, Sb and Te of high purity and the proper stoichiometry were weighed under an Ar atmosphere, and the mixture was further processed via ball milling. The resulting BST was extensively studied with respect to its structural characteristics and thermoelectric properties [28].  
The composite PANI-Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\) powders were prepared via mechanical mixing of the polymer powder with the inorganic additive. More precisely, composite PANI-Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\) powders with 20 wt.% and 30 wt.% Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\) content were initially prepared. The two blending ratios were selected based on a previously reported comparable system appearing in the literature, consisting of PANI and Bi\({}_{2}\)Te\({}_{3}\) in a 70-30% wt. ratio [31]. Mechanical mixing was carried out for 15 min in a glovebox under an inert (argon) environment using a mortar.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
###### Abstract  
Free-space optical (FSO) communication systems have attracted considerable research interest owing to its high transmission rates and its efficient solution for the last mile problem. Atmospheric turbulence degrades the performance of FSO systems. To estimate the received signal, Channel State Information (CSI) or blind detection is used. The blind detection estimates the received signal without the need for pilot signals which reduce the throughput and increase the complexity of the signal. In this paper, we have proposed a relay technique which utilizes the blind detection method for FSO communications. This proposed technique would improve the performance of the FSO system compared to the direct link method. It is observed that the proposed method achieves a signal-to-noise ratio (SNR) close to the SNR reported by CSI method maintaining the same average bit error rate, provided that only a small observation window is employed. Moreover, Monte Carlo simulation results are further provided to demonstrate the validity of the derived approximated average bit error rate.  
free-space optical; intensity modulation with direct detection; channel state information; maximum likelihood sequence detection Article
# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 3 Model Structure of Semantic Segmentation
### Atrous Convolution  
Atrous convolution is an effective technique to enlarge the field-of-view of filters at any convolutional layer. This structure catches more features without adding to the computation [29]. Atrous convolution uses another parameter called the dilation rate, which defines the spacing placed between values to be used in a kernel. In an ordinary convolution where a kernel has no spacing, the dilation rate is defined to be one. When a \(3\times 3\) kernel has a dilation rate of two, its actual field of view becomes equivalent to that of a \(5\times 5\) kernel. In this way, a wider field of view is secured at the same computational cost.  
Atrous convolution delivers better performance than the downsampling-convolution-upsampling process. Still, if a fixed rate is applied when using the atrous convolution, the filter size is fixed and only limited features can be found among other various features. To address this challenge, different sizes of rates are used, which is called the atrous spatial pyramid pooling, or ASPP [30]. DeepLabV3+ is a model where those two structures are incorporated. The encoder properly uses the atrous convolution, and the resultant feature map is placed in the decoder.
## 5 Major Equipment and Technologies
### Harvesting  
In the agriculture sector, harvesting is a crucial phase in the crop production process because this final stage determines crop yield and, eventually, the crop's performance. Regarding manpower, it is expected that the US faces USD 3.1 billion worth of crop losses each year due to manpower shortages [142]. Additionally, according to the United States Department of Agriculture (USDA), 14% of total agricultural costs are spent on wages and labor, and as high as 39% on some labor-intensive farms [143]. Considering the expense and manpower shortage issues at this stage, farm experts hope that the use of agricultural robotics technology will not only reduce manpower shortage but also provide flexible harvesting capabilities when required. Automatic fruit harvesting requires in-depth research on sophisticated sensors that can collect precise and unambiguous information about specific crops and fruits.  
For example, strawberries are highly consumed fruits and can be purchased all year round. However, manpower, particularly during the packaging and harvesting stages, is a major factor for the high-level price of this fruit [144]. Because this fruit is mostly cultivated in the greenhouses system, the harvesting robot can reduce the production cost. A robot developed by Agrobot is capable of collecting strawberries along the field's strawberry plant rows and enhancing packaging through the operator [145]. For example, Agrobot's SW 6010 is a semi-automatic robot for semi-automatic strawberry picking. Tektu T-100 is a rechargeable electric strawberry harvester that operates multiple channels with a net-zero emission [144].
# Polynomial Expressions for the Thermal Efficiency of the Parabolic Trough Solar Collector
## 2 Material and Methods
### Followed Methodology for the Approximation Methods
Generally, the solution procedure is very quick with solving time lower than 1 s per case. The literature review of this work indicates that the thermal efficiency of the PTC is expressed with polynomial expressions and thus, this strategy is investigated in this work in a detailed way. In this work, different polynomial formulas have been used in a systematic way. All these formulas can be expressed through the following general expression:  
\[\eta_{col}=a_{0}+a_{1}\cdot\frac{\Delta T}{G_{b}}+a_{2}\cdot\frac{\Delta T^{2 }}{G_{b}}+a_{3}\cdot\frac{\Delta T^{3}}{G_{b}}+a_{4}\cdot\frac{\Delta T^{4}}{ G_{b}}+b\cdot\Delta T \tag{30}\]  
In all the examined cases, the constant term \"\(a_{0}\)\" is included and all the proper combinations of the coefficients have been studied. In other words, every combination includes some parameters (e.g., \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\)) and the other parameters are set to be zero (e.g., \(a_{4}\), \(b\)). In all the cases, there are at least two coefficients, the \"\(a_{0}\)\" and another. The maximum number of the coefficients is six when all the coefficients are used in the approximation. The selection of the Formula (27) is in accordance with the existing literature. The term with \"\(\Delta T\)\" is an important one, especially for the LS-2 module, and it has been used also in Reference [18]. The other terms are used in various studies. The maximum power is the fourth because the thermal radiation losses include this power and also there is no study in the literature with a higher power to be used. So, there is no reason for using the fifth power for example in the approximation formula because it would be unrealistic due to the radiation thermal losses law. So, this work studies the conventional polynomial expressions with the terms \"\(\Delta T\)\"/\(G_{b}\)\" and for all the cases, the need for the extra term \"\(\Delta T\)\" is evaluated. Finally, the goal is to determine the simplest expression which can lead to accurate results. The approximation is conducted in every case with Microsoft Excel software and the command \"linset\". The R\({}^{2}\) is calculated as an effective index which has to be extremely close to 100%. Moreover, the mean absolute percentage error (MAPE) is an extra index that is calculated and it has to be close to zero. Its definition is given below:  
\[MAPE=\frac{1}{n}\cdot\sum_{i=1}^{i=n}\left|\frac{\eta_{th,aprx}}{\eta_{th,EES }}-1\right|\cdot 100\% \tag{31}\]  
where (\(n\)) is the total number of the dataset points which is equal to 120 in this work.