esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 1 Introduction
However, the existing study in [8] did not consider the number of selected hops, and it causes serious decrease in maximum data rate which is one of very important performance indicators in mobile communication systems. For solving the drawbacks of the existing study efficiently, the proposed scheme selects the number of minimum relays for obtaining target error performance. Generally, the error performance is improved when the number of used relays is increased since an effect of fading channel and error propagations are decreased (but total data rate is decreased). For better understanding, let the number of minimum relays for obtaining target error performance be \(N\). When the system selects the number of relays which is larger than \(N\) such as \(N+1\) or \(N+2\), the total data rate is decreased. On the other hand, when the system selects the number of relays which is less than \(N\) such as \(N-1\) or \(N-2\), the system does not obtain target error performance. Therefore, the main drawbacks for the existing study is solved by our proposed scheme  
Figure 1: The system model for multi-hop wireless network. and the proposed scheme can be used more practically since the loss of total data rate is less than the existing study in [8]. So, the number of relays must be reduced to solve this problem. Therefore, this paper proposes a scheme with fewer relays and the same BER performance compared to the conventional BHM scheme. This paper uses a Q-learning based relay selection scheme to select an optimal relay and reduce the latency in multi-hop wireless networks. Nowadays, there are many relay selection schemes using reinforcement learning in cooperative networks [9; 10; 11; 12; 13]. These papers are focused on using reinforcement learning to improve performance through selected relays. However, the purpose of this paper is to reduce the latency that occurs in the process of passing the relay with the same performance. The proposed scheme has a low latency and same reliability compared to the conventional BHM scheme. This paper is organized as follows. Section 2 represents the system model and conventional relay selection schemes. Section 3 describes a proposed relay selection scheme. Section 4 shows the simulation results. Finally, Section 5 concludes this paper.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 2 Background Knowledge
### Allan Variance  
Allan variance [10] is a famous method to characterize and identify various error sources in FOG, as well as their contribution to the statistical properties of the entire noise in detail. It is very easy to obtain types of error sources and the magnitude of each error source, by using the quantitative relationship between Allan variance and the power spectrum density (PSD). Here \(\omega(i),i=1,2,\cdots,N\), is the output angular rate of FOG, acquired at a fixed sampling period \(\tau_{0}\). Then, sample the N initial samples again, with sampling time \(\tau=M\times\tau_{0}\), where \(M=1,2,\cdots,N/2\). Thus, the number of data packets is \(K=N/M\), and the average value of each time length \(\tau\) is  
\[\omega_{j}(M)=\frac{1}{M}\sum_{i=1}^{M}\omega_{(j-1)(M+)}(\tau_{0}),j=1,2, \cdots,K. \tag{2}\]  
At this time, the Allan variance on the time domain is defined as:  
\[\sigma_{A}^{2}(\tau)=\frac{1}{2}\langle[\omega_{j+1}(M)-\omega_{j}(M)]^{2} \rangle\approx\frac{1}{2(K-1)}\sum_{j=1}^{K=1}\delta_{j}^{2}, \tag{3}\]  
where \(\delta_{j}=\omega_{j+1}(M)-\omega_{j}(M)\) is the difference between the average values of adjacent arrays, and \(\langle\rangle\) is used to acquire the population mean. By selecting different array lengths (i.e., correlation time), the corresponding Allan variance and Allan standard deviation can be obtained.  
In the frequency domain, the relationship of the bilateral power spectrum density between the Allan variance and the signal can be expressed by  
\[\sigma_{A}^{2}(\tau)=4\int_{0}^{\infty}S_{\omega}(f)\frac{\sin 4(\pi f\tau)}{( \pi f\tau)^{2}}df, \tag{4}\]  
where \(S_{\omega}(f)\) is PSD for random processes. The noise component in the FOG can be obtained quantitatively by means of Allan variance. These noise components mainly include angular random walk, zero offset stability, rate random walk, and speed ramp. It can be expressed as the following equation:  
Figure 2: The original gyro signal.  
\[\sigma_{A}^{2}(\tau)=\sigma_{N}^{2}(\tau)+\sigma_{B}^{2}(\tau)+\sigma_{K}^{2}( \tau)+\sigma_{R}^{2}(\tau)+\sigma_{Q}^{2}(\tau). \tag{5}\]  
According to the relationship between the Allan variance and the power spectrum density function, the following equation can be obtained:  
\[\sigma_{A}^{2}(\tau)=\frac{3Q^{2}}{\tau^{2}}+\frac{N^{2}}{\tau}+(0.6643B)^{2}+ \frac{K^{2}\tau}{3}+\frac{R^{2}\tau^{2}}{2}=\sum_{n=-2}^{n=2}A_{n}\tau^{n}. \tag{6}\]  
According to the gyro output data, the least-squares fitting method can be used to obtain the fitting coefficients, and the various noise systems can then be obtained by combining the equations. The Allan variance of the original gyro signal has been shown in Figure 3.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 2 Main Power Stage
### Boundary Current Condition for Output inductor \(L_{o}\)  
The operation of the boundary mode of the output inductor \(L_{o}\) is analyzed as in a conventional buck converter. The current ripple \(\Delta i_{Lo}\) flowing through the output inductor \(L_{o}\) can be expressed as follows:  
\[\Delta i_{Lo}=\frac{v_{Lo}\Delta t}{L_{o}}=\frac{V_{o}(1-D)T_{s}}{L_{o}} \tag{22}\]  
According to Kirchhoff's Current Law (KCL), the average current on the capacitor is zero; it can be obtained that  
\[I_{o}=i_{Lr}(t)+i_{Lo}(t)-i_{Co}(t)=I_{Lr}+I_{Lo}-I_{Co}=I_{Lr}+I_{Lo} \tag{23}\]  
Therefore, the expression for calculating the DC component of the resonant inductor current \(I_{Lr}\) is first derived, i.e.,  
\[\begin{array}{ll}I_{Lr}&=\frac{1}{T_{s}}\int_{l_{0}}^{t_{0}+T_{s}}i_{Lr}(t) dt=\frac{1}{T_{s}}\bigg{[}\int_{l_{0}}^{t_{0}+\Delta t_{10}}i_{Lr}(t)dt+\frac{I_{Lr 1}\Delta t_{21}}{2}\bigg{]}\\ &=\frac{1}{T_{s}}\Bigg{[}\int_{l_{0}}^{t_{0}+\frac{1}{\omega_{a}}\cos^{-1}( \frac{-V_{o}}{V_{in}-V_{o}})}\frac{V_{in}-V_{o}}{Z_{a}}\sin\omega_{a}(t-t_{0} )dt+\,L_{r}\frac{V_{in}^{2}-2V_{in}V_{o}}{2V_{o}Z_{a}^{2}}\Bigg{]}\Bigg{]} \end{array} \tag{24}\]  
As \(I_{Lo}>0.5\Delta i_{Lo}\), \(L_{o}\) will be operated in the current conduction mode (CCM), i.e.,  
\[I_{Lo}>0.5\Delta i_{Lo}=\frac{V_{o}(1-D)T_{s}}{2L_{o}} \tag{25}\]  
\[L_{o}>\frac{V_{o}(1-D)T_{s}}{2I_{Lo}}=\frac{V_{o}(1-D)T_{s}}{2(I_{o}-I_{Lr})} \tag{26}\]  
Figure 9: Current flow in state 5.
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 2 Materials and Methods
### Surveying Equipment
#### 2.2.3 Digital Aerial Photogrammetric Camera Microsoft UltraCamLp, Aircraft Tecam MMA  
Photogrammetric data collection was performed by digital aerial photogrammetric camera Microsoft UltraCamLp (Figure 2a). Image size is 11,704 \(\times\) 7920 pixels, and output format is jpeg or tiff. Pixel size is 6 \(\upmu\)m, and CCD chip area is 70.22 mm \(\times\) 47.52 mm. Panchromatic lens has focal length of 70 mm. Field of view from vertical is 52\({}^{\circ}\) at the cross-track direction and 37\({}^{\circ}\) at along-track direction. Maximal image acquisition speed is one image per 2 s. Weight of the camera is about 55 kg [55]. The photogrammetric equipment carrier was a twin-propeller aircraft Tecam MMA (Multi Mission Aircraft) (Figure 2b). Its length is 8.7 m, wingspan is 11.4 m, maximum takeoff weight is 1230 kg, and top speed is 145 knots [56]. The camera was mounted on an aircraft in a gyro-stabilizing basement, which significantly eliminates tilts of the aircraft. Aircraft technology was supplemented by GNSS and inertial measurement unit (IMU) in the product Applanar POSTrack TM, which stores the position and tilt of the camera at the time of exposure and provides input data for the calculation of the analytical aerotriangulation [57].
## 2 Methodology
### Direct Model
#### 2.2.3 Surface Characteristics
The MS effects may impact the structural parameters retrieved from NIR lidar measurements and the associated uncertainties have to be assessed from the simulations. **Land-cover type** is obtained with the MODIS product [46]. This parameter helps us choose the study areas. Two MODIS land-cover type yearly products (chosen arbitrary for 2011) are used to identify the forest area: MCD12C1 (horizontal resolution of 0.05\({}^{\circ}\)) and MCD12Q1 (horizontal resolution of 500 m). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{_P\({}_{\pi}\) in “Mixed Forest”_} & \multicolumn{2}{c}{_P\({}_{\pi}\) in “Evergreen Needle Leaf”_} & \multicolumn{2}{c}{**Absorption Coefficient (1 – \(\omega_{0}\))**} \\ \cline{2-7}  & **490 nm** & **865 nm** & **490 nm** & **865 nm** & **355 nm** & **1064 nm** \\ \hline January & \(1.79\pm 0.32\) & \(1.52\pm 0.15\) & \(1.62\pm 0.48\) & \(1.57\pm 0.15\) & & \\ July & \(1.36\pm 0.42\) & \(1.36\pm 0.16\) & \(1.65\pm 0.44\) & \(1.53\pm 0.21\) & \(\sim\)0.95 & \(\sim\)0.35 \\ \hline \end{tabular}
\end{table} 
Table 2: Backscatter scattering phase function (\(P_{\pi}\)) derived from the multi-angular satellite POLDER data. The absorption coefficient is also given. The classification scheme (\"_Land Cover Type 1_\"), defined by the International Geosphere Biosphere Programme (more information are available online [51]), is used in our simulations.
## 4 Performance Comparison between Protocols  
We now extend the analysis of the errors arising from the perturbations described in the previous sections. The aim is to obtain a final expression of the error rate due to these factors, which can later be introduced into a key rate. With this, we will compare the performance of the BBM92 protocol in various cases: with full perturbations, with only phase perturbations, and with a compensated phase perturbation. In addition, we will see how this relates to the proposed BSEP protocol, which already has a built-in autocompensation mechanism.  
To make the expressions of \(p_{err}^{Z}\) and \(p_{err}^{X}\) useful for a key rate analysis, we need to take into consideration that we are considering many rounds of detection. In fact, we will consider the ideal scenario of infinite signals being sent. That is, we need to take the _error probabilities_\(p_{err}^{Z}\) and \(p_{err}^{X}\) and translate them into actual _error rates_\(e_{Z}\) and \(e_{X}\). Thus, we need to compute statistical average of the above error probabilities.
# Evaluation of the Coupling of a Hybrid Power Plant with a Water Generation System
## 4 Results
### Sensitivity Analysis  
The final acceptable price of water that constitutes out plant economically viable depends on the treated water capacity of the reclamation plant and the resulting energy required to carry out the whole purification process.  
We seek to supply with potable water the same amount of people we provide electricity to, i.e., 27,500 homes. At present, the average daily water consumption in Spain is 132 liters/inhabitant/day. In the view of the above, a total treated water capacity of 3,630 m\({}^{3}\)/day is assumed at the reclamation plant. In accordance to the aforementioned specific treatment train and the total water capacity, a final value of 1.84 MWh/day is required. Another significant data point for the correct calculation of this analysis is the price of electricity. Here, we assume that of Spain, with a value of 0.13 \(\upmu\)KWh. If part of the generated electricity in the HPP is used to run the water reclamation plant, the selling price of drinking water is finally found to be 14.61 \(\upmu\)m\({}^{3}\).  
To be regarded as a viable proposal under current conditions, either the necessary energy for water production or the price of electricity should be reduced, since the actual price of water in Spain is 1.66 \(\upmu\)m\({}^{3}\)[60]. Despite this, future water scarcity and climate change are expected to considerably increase the value of water, especially in arid areas with low reserves of water resources. Consequently, although this calculated price seems high today, it might become more realistic in the future.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### Training Process  
Using the same notation adopted in [12], the set of trainable parameters is denoted as \(\mathbf{\Theta}=\{\mathbf{\Theta}_{en},\mathbf{\Theta}_{de}\}\). The truncated input and output of the NR-CsiNet model for the \(i\)-th patch are denoted as \(\mathbf{H}_{i}^{\text{Csi-RSi}}\) and \(\hat{\mathbf{H}}_{i}^{\text{f}}=f(\mathbf{H}_{i}^{\text{Csi-RSi}};\mathbf{\Theta}) =f_{de}(f_{en}(\mathbf{H}_{i}^{\text{Csi-RSi}};\mathbf{\Theta}_{en});\mathbf{\Theta}_{de})\), respectively.  
The loss function is defined as  
\[L(\mathbf{\Theta})=\frac{1}{S}\sum_{i=1}^{S}||f(\mathbf{H}_{i}^{\text{Csi-RSi}};\bm {\Theta})-\mathbf{H}_{i}^{\text{i}d,t}||_{2}^{2} \tag{7}\]  
where \(S\) denotes the number of training samples and \(||.||_{2}\) is the Euclidean norm operator.
## 3 Methods
### Leaf Area Index (LAI) Estimation  
The theoretical basis of LAI estimation is the Beer-Lambert law, which was originally used to depict the light attenuation in uniform mediums (e.g., liquid) and later extended to the light interception of homogeneous canopies with continuously random distribution of leaves, known as the gap fraction theory [5]. For vegetation canopy, the canopy transmittance is equivalent to gap fraction in a specific direction [34]. Accordingly, the gap fraction \(P_{0}(\theta_{v},\ \phi_{v})\) in the direction \((\theta_{v},\ \phi_{v})\) is related to the LAI by Equation (2). Readers are recommended to refer to the review articles and book chapters about the indirect LAI measurements for more detail about the derivation [1; 5; 22].  
\[P_{0}(\theta_{v},\ \phi_{v})=\exp\biggl{(}-\frac{G(\theta_{v},\ \phi_{v}) \cdot LAI}{\cos(\theta_{v})}\biggr{)}, \tag{2}\]  
where \(G(\theta_{v},\ \phi_{v})\) is defined as the mean projection of a unit leaf area on the plane perpendicular to the observation direction. Based on the assumption that leaf inclination anglesare independent of azimuth, \(G(\theta_{v},\ \phi_{v})\) is rewritten as \(G(\theta_{v})\) that can be approximated by an ellipsoidal function of zenith angle and leaf inclination distribution.  
\[G(\theta_{v})=\frac{\left(\chi^{2}+\left(tan\theta\right)^{2}\right)^{0.5}\cdot tan \theta}{\chi+1.774\cdot\left(\chi+1.182\right)^{-0.733}}, \tag{3}\]  
where \(\chi\) is a shape parameter representing the ratio of vertical to horizontal projections of canopy elements. In this study, the value of \(\chi\) is set as 2, i.e., close to the planofile type.  
[PERSON] (1971) indicated that the exponential relationship between gap fraction and LAI is effective even though the continuously random distribution of leaves is not satisfied ([PERSON], 1971). Following this statement, scholars proposed the clumping index (\(\Omega_{0}\)) to correct the cases of regular and clumped leaf arrangement ([PERSON] and [PERSON], 2018). Naturally, Equation (1) is written as Equation (3). Note that the LAI estimate based on Equation (1) is considered as effective LAI whereas that based on Equation (4) is considered as true LAI.  
\[P_{0}(\theta_{v})=\exp\left(-\frac{\Omega_{0}\cdot G(\theta_{v})\cdot LAI}{ \cos(\theta_{v})}\right) \tag{4}\]  
To eliminate the clumping effect, [PERSON] and [PERSON] (1986) proposed to average the logarithms of gap fractions over segments of finite length ([PERSON], 1971). The \(\Omega_{0}\) can be expressed by  
\[\Omega_{0}(\theta_{v})=\frac{ln[\overline{P_{0}(\theta_{v})}\ ]}{ln[P_{0}( \theta_{v})]}. \tag{5}\]
## 6 Implementation and Discussion of Results
### Evaluation Metrics  
* Accumulated wealth rate (AWR) is a strategy in which the investor buys as much as stock based on the first-day price. Every day, calculate the total stock value based on the current price by multiplying the number of shares by the current price, then add it to the cash to get the investor's total wealth on that day. Finally, calculate the profit or loss achieved and divide it by the total cash available at the start of the trading period. The following equation describes how the AWR can be calculated. \[\text{AWR}_{T}=\frac{\sum_{t=1}^{T}P_{t}\text{ or }L_{t}}{Cash_{t=0}}\] (5)
* Average Max drawdown As an intermediate step, the max drawdown (MDD) is calculated, and then the average is calculated on top of it. MMD compares the current wealth with the peak wealth to determine the maximum loss during the trading period. Consequently, the average max drawdown is the average value for the MDD during the trading period. \[\text{MDD}_{t}=\frac{(\text{{Peak}\,\text{{Welalth}}-\text{{Welalth}}_{t}})}{ \text{{Peak}\,\text{{Welalth}}}}\] (6) \[\text{AMDD}_{T}=\frac{\sum_{t=1}^{T}(\text{MDD}_{t})}{T}\] (7)
* Calmar ratio This calculates the mean value for the accumulated wealth rate with respect to the max of the max drawdown value. The following equation can calculate it. \[\text{AMDD}_{T}=\frac{\sum_{t=1}^{T}(\text{MDD}_{t})}{T}\] (8)
* Average profit return The profit return calculates the difference between the current and previous prices and normalizes the result to the previous price. The average profit return is the average of the previous value within the trading period, as shown in Equations (9) and (10). \[\text{PR}_{T}=\frac{\sum_{t=1}^{T}(Price_{t}-Price_{t-1})}{Price_{t-1}}\] (9) \[\text{APR}_{T}=\text{Avg}(\text{PR}_{T})\] (10)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Best Stock** & **Buy and Hold** & **(CRP)** & **Proposed Algorithm** \\ \hline average\_profit\_return & 0.0011 & 0.0011 & 0.0011 & 0.01 \\ Sharpe ratio & 2.5 & 2.4 & 2.4 & 3 \\ Average maximum drawdown & 0.0031 & 0.02 & 0.01 & 0.03 \\ Calmar ratio & 6.1 & 2.6 & 3 & 3 \\ Annualized return rate (ARR) & 0.5 & 0.5 & 0.5 & 1.1 \\ Annualized Sharpe ratio (ANSR) & 47.8 & 47 & 47 & 57.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of model performance.  
* Average Sharpe ratio The Sharpe ratio is defined as the meaning of the accumulated wealth rate divided by the standard deviation of the accumulated wealth rate. Moreover, the average Sharpe ratio is the average of this value during the trading period. \[\text{SR}_{T}=\frac{mean(\text{AWR}_{T})}{Std(\text{AWR}_{T})}\] (11) \[AS\text{R}_{T}=\frac{\sum_{t=1}^{T}(SR_{t})}{T}\] (12)
* Annualized Return Rate and Annualized Sharpe Ratio The annualized terms mean calculating the values with respect to a full year. They are calculated with the same equations, but the trading periods will be 365.
## 3 Results and Discussion
### Microscopic Mechanism Analysis of PPA/SBS/SBR Composite-Modified Asphalt Cold Replenishment Liquid
#### 3.4.2 Fourier-Transform Infrared Spectroscopy  
FTIR spectroscopy was used to determine the functional groups and their changes in various materials, study the chemical reactions occurring during the modification process, and explore the modification mechanism. The FTIR spectra of the materials are shown in Figure 10[31]. Base asphalt, H-ACRL, and L-ACRL were taken as the research objects before and after TFOT. As shown by the FTIR spectra of different asphalt samples in Figure 10, the FTIR spectra of the two ACRLs were very similar to those of the base asphalt. After TFOT, there were no new absorption peaks generated, and some absorption peaks were only slightly different in intensity [32]. For example, the absorption peaks at 2923 cm\({}^{-1}\) and 2850 cm\({}^{-1}\) were caused by -CH\({}_{2}\) antisymmetric and symmetric stretching vibrations. There were weaker absorption peaks at 1600 cm\({}^{-1}\) and 1573 cm\({}^{-1}\), which were caused by the stretching vibration of the C=C aromatic ring. The absorption peaks at 1454 cm\({}^{-1}\) and 1457 cm\({}^{-1}\) were caused by -CH\({}_{2}\) bending vibrations. The peaks in the wavenumber range of 926-840 cm\({}^{-1}\) were due to the substitution pattern of the benzene ring of asphalt, and several weak absorption peaks appearing in this range were due to C-H vibrations on the benzene ring [33].  
In summary, there were no new absorption peaks for H-ACRL and L-ACRL compared with the spectrum of base asphalt. The process of preparing asphalt cold replenishment liquid by adding modifiers, surfactants, diluents, and reinforcing agent is only a physical modification.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 7 Summary and Prospects  
The structure of this paper is organized as follows: Section 2 introduces related work; Section 3 elaborates on the problem model; Section 4 provides a detailed description of the queuing search algorithm (QSA); Section 5 presents the experimental results and analysis; and finally, Section 6 summarizes this paper and outlines future research directions.  
This paper addresses the Edge Site Deployment Problem (ESDP) with the objective of minimizing user request latency to enhance user experience and provide effective solutions for service providers. The proposed solution, based on the queuing search algorithm (QSA), optimizes the deployment process by reducing the number of hops in the network, representing the latency of user requests. The QSA, inspired by human queuing behaviors, efficiently avoids local optima and demonstrates superior global search capabilities. Simulation experiments validate its effectiveness, showing improved performance in comparison to traditional methods. This work not only offers a novel approach for ESDP but also contributes to the broader field of edge computing by advancing optimization techniques for resource allocation and deployment strategies. Although this study has achieved certain results, factors such as user mobility and server heterogeneity have not yet been considered. Future research will focus on the following directions:  
User mobility modeling: Incorporate user dynamic mobility into the optimization framework to more realistically reflect actual application scenarios;  
Server heterogeneity analysis: Study the performance differences of different types of servers and their impact on deployment strategies;  
Load balancing: The number of jumps is random, and more realistic network models are studied to better achieve load balancing effects.  
Conceptualization, B.W., X.S. and Y.S.; Methodology, B.W. and X.S.; Software, B.W. and X.S.; Validation, B.W. and X.S.; Formal analysis, B.W.; Investigation, B.W. and X.S.; Resources, B.W.; Data curation, B.W. and X.S.; Writing--original draft, B.W. and X.S.; Writing--review & editing, B.W. and Y.S.; Visualization, B.W. and X.S.; Supervision, B.W. and Y.S.; Project administration, B.W. and Y.S.; Funding acquisition, B.W. and Y.S. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the key scientific and technological projects of Henan Province (Grant No. 252102211072, 242102210095, and 232102210078), National Natural Science Foundation of China (Grant No. 62102372 and 62072414), the Doctor Scientific Research Fund of Zhengzhou University of Light Industry under Grant No. 2021 BSJ029, and the School-Level Young Backbone Teacher Training Program of Zhengzhou University of Light Industry.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.