esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
According to the above theoretical derivation, both matrix \(\mathbf{M}\) and \(\mathbf{K}\) are of dimension \(2(\text{M}+4)\times 2(\text{M}+4)\), and vector A has dimension \(2(\text{M}+4)\). The expression of vector \(\mathbf{A}\) is shown as:  
\[\mathbf{A}=\{A_{0},A_{1},\ldots,A_{m},\ldots A_{M},A_{1}^{a},A_{2}^{a},A_{3}^ {a},A_{4}^{a},B_{0},B_{1},\ldots,B_{m},\ldots B_{M},B_{1}^{a},B_{2}^{a},B_{3} ^{a},B_{4}^{a}\} \tag{17}\]  
In Equation (17), the expressions of the mass matrix \(\mathbf{M}\) and the stiffness matrix \(\mathbf{K}\) for the lattice sandwich beam with arbitrary boundary conditions can be given separately as:  
\[\mathbf{K}=\mathbf{K}_{P}+\mathbf{K}_{\text{S}} \tag{18}\]  
\[\mathbf{K}_{P}=\left[ \begin{array}{cccc}K_{11}^{P}&K_{12}^{P}&K_{13}^{P}&K_ {14}^{P}\\ K_{21}^{P}&K_{22}^{P}&K_{23}^{P}&K_{24}^{P}\\ K_{31}^{P}&K_{32}^{P}&K_{33}^{P}&K_{34}^{P}\\ K_{41}^{P}&K_{42}^{P}&K_{43}^{P}&K_{44}^{P}\\ \end{array} \right] \tag{19}\]  
\[K_{11}^{P}=B(D_{\text{t}}+D_{b})\lambda_{r}^{2}\lambda_{i}^{2}\int_{0}^{L} \cos\lambda_{r}x\cos\lambda_{i}xdx \tag{20}\]  
\[K_{22}^{P}=B(D_{\text{t}}+D_{b})\lambda_{s}^{2}\lambda_{j}^{2}\int_{0}^{L} \sin\lambda_{s}x\sin\lambda_{j}xdx \tag{21}\]  
\[K_{33}^{P}=B(A_{\text{t}}+A_{b})\lambda_{r}\lambda_{i}\int_{0}^{L}\sin\lambda _{r}x\sin\lambda_{i}xdx+BA_{\text{c}}\int_{0}^{L}\cos\lambda_{r}x\cos\lambda_ {i}xdx \tag{22}\]  
\[K_{44}^{P}=B(A_{\text{t}}+A_{b})\lambda_{s}\lambda_{j}\int_{0}^{L}\cos\lambda _{s}x\cos\lambda_{j}xdx+BA_{\text{c}}\int_{0}^{L}\sin\lambda_{s}x\sin\lambda_ {j}xdx \tag{23}\]  
\[K_{12}^{P}=B(D_{\text{t}}+D_{b})\lambda_{r}^{2}\lambda_{i}^{2}\int_{0}^{L} \cos\lambda_{r}x\sin\lambda_{j}xdx \tag{24}\]  
\[K_{13}^{P}=B(-B_{\text{t}}+B_{b})\lambda_{r}^{2}(-\lambda_{i})\int_{0}^{L} \cos\lambda_{r}x\sin\lambda_{i}xdx \tag{25}\]  
\[K_{14}^{P}=B(-B_{\text{t}}+B_{b})\lambda_{r}^{2}\lambda_{j}\int_{0}^{L}\cos \lambda_{r}x\cos\lambda_{j}xdx \tag{26}\]\[K_{23}^{p}=B(-B_{\rm t}+B_{b})\lambda_{s}^{2}(-\lambda_{i})\int_{0}^{L}\sin\lambda _{s}x\sin\lambda_{i}xdx \tag{111}\]  
\[K_{24}^{p}=B(-B_{\rm t}+B_{b})\lambda_{s}^{2}\lambda_{j}\int_{0}^{L}\sin\lambda _{s}x\cos\lambda_{j}xdx \tag{112}\]  
\[K_{34}^{p}=B(A_{\rm t}+A_{b})(-\lambda_{r})\lambda_{j}\int_{0}^{L}\sin\lambda_{ r}x\cos\lambda_{j}xdx+B(A_{\rm c}+A_{\rm d}+A_{\rm e})\int_{0}^{L}\cos\lambda_{r}x\sin \lambda_{j}xdx \tag{113}\]  
\[K_{22}^{P}=B(D_{\text{t}}+D_{b})\lambda_{s}^{2}\lambda_{j}^{2}\int_{0}^{L} \sin\lambda_{s}x\sin\lambda_{j}xdx \tag{21}\]0  
\[{\bf K}_{5}=\left[ \begin{array}{cccc}K_{11}^{5}&0&0&0\\ 0&K_{22}^{5}&0&0\\ 0&0&K_{33}^{5}&0\\ 0&0&0&0\end{array} \right] \tag{115}\]  
\[K_{11}^{5}=B\left(k_{0}+(-1)^{r+i}k_{\rm L}\right) \tag{116}\]  
\[K_{22}^{5}=B\left(K_{0}\lambda_{s}\lambda_{j}+(-1)^{s+j}\lambda_{s}\lambda_{j }K_{L}\right) \tag{117}\]  
\[K_{33}^{5}=B\left(K_{0}+(-1)^{r+i}K_{L}\right) \tag{118}\]  
\[{\bf M}=\left[ \begin{array}{cccc}M_{11}&M_{12}&M_{13}&M_{14}\\ M_{21}&M_{22}&M_{23}&M_{24}\\ M_{31}&M_{32}&M_{33}&M_{34}\\ M_{41}&M_{42}&M_{43}&M_{44}\end{array}
## 3 Results and Discussion
### Classification Discussion Based on Overall Results
#### 3.3.4 Different Fireproof Materials  
In the experiment, the three kinds of materials that met the fire protection requirements were brand B IFC (W), brand C NFC, and brand D FC. In order to compare the fire protection effect of the three materials, we counted the failure time data of some cases, as shown in Table 9.  
Figure 16: Curve of coating thickness with heating time.  
By comparing case 3 and case 12, we found that although the material thickness of case 3 was much less than that of case 12, the failure time was later than that of case 12. Considering that the fire protection effect of the three materials was positively related to the thickness, so it could be inferred that the fire protection effect of the Brand B IFC (W) was better than that of the brand D FC for the same thickness condition.  
By comparing case 8, case 9, and case 11, it was found that the material thicknesses of cases 8 and 9 were less than that of case 11, but the failure times were later than that of case 11. It could be inferred that the fire protection effect of the brand C NFC was better than that of brand D FC for the same thickness condition. This result was consistent with the thermal conductivity of this two materials in Table 2.  
By comparing case 3 and case 10, it was found that although the failure time of case 3 was earlier than that of case 10, the difference was not significant. Additionally, considering the fact that the material thickness of case 3 was far less than that of case 10, we could speculate that the fire protection effect of the brand B IFC (W) was better than that of the brand C NFC for the same thickness condition.  
For the selection of these three materials, there were the following considerations:  
1. The IFC (W) had thin thickness, a good fire protection effect, and a beautiful appearance, but the construction was more complex, and the material price and the construction cost were larger than those of NFC and NFC. The water resistance and the durability of the coating were worse than those of the NFC, but the water resistance and the durability could be improved by brushing the topcoat.
2. The NFC had strong durability, the material price per square meter was the lowest among the three materials, and the fire-retardant effect was good, but the good fire-retardant effect was obtained with the thickness. The coating was thick, heavy, rough, and not beautiful enough, but it could also be brushed with a topcoat to make it more beautiful.
3. The construction of FC was relatively convenient, and the price was moderate. It could be wrapped with high-temperature glue or iron wire winding. The thickness was large and the fire protection effect was general. It could easily be soaked by rain outside, so the surface could be waterproof and beautiful with a tarpaulin or aluminum sheet.  
It could be seen that the three schemes had their own advantages and disadvantages, and they needed to be selected according to the actual needs.
## 4 Results and Discussions
### Comparison with Pre-Trained Models
This results in an overall accuracy of 97.1%. Table 7 depicts the number of layers, input size, and number of learnable parameters in different CNN models considered in this work. EnsemConvNet-18 and EnsemConvNet-24 represents the ensemble of ConvNet-18 and ConvNet-24, respectively. It is evident that the total number of learnable parameters in the suggested models is less compared with other state-of-the-art architectures. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Performance** & & & & **Training** & **Testing** \\
**Metrics/** & _Acc_ & _Sen\({}_{M}\)_ & _Pre\({}_{M}\)_ & _F1-Score\({}_{M}\)_ & **Time** & **Time** \\  & & & & & **in s** & **in s** \\ \hline Alexnet & 96 & 96.4 & 96 & 96.2 & 935.45 & 4.14 \\ Mobilenet-V2 & 94.7 & 95 & 95 & 95 & 2945.67 & 8.17 \\ Resnet-50 & 96.7 & 97 & 96.6 & 96.8 & 5991.25 & 9.88 \\ VGG-16 & 95.8 & 96 & 95.7 & 95.8 & 8706.6 & 34.92 \\ VGG-19 & 97.1 & 97.2 & 96.9 & 97 & 11,239.8 & 36.17 \\ Densenet-201 & 94.5 & 95 & 94.5 & 94.7 & 20,832.4 & 20.28 \\ Efficientnet-b0 & 96.8 & 97 & 96.7 & 96.8 & 5506.23 & 11.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics obtained by various pre-trained models on MEAT3C database. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Performance** & & & & & **Training** & **Testing** \\
**Metrics/** & _Acc_ & _Sen_ & _Spe_ & _Pre_ & _F1-Score_ & **Time** & **Time** \\
**Network** & & & & & & **in s** & **in s** \\ \hline Alexnet & 98.8 & 98.9 & 98.7 & 98.7 & 98.8 & 7571.08 & 54.13 \\ Mobilenet-V2 & 99.3 & 99.7 & 98.4 & 98.9 & 99.3 & 12,907.56 & 86.7 \\ Resnet-50 & 99.8 & 99.9 & 99.7 & 99.7 & 99.7 & 14,951.2 & 98.76 \\ VGG-16 & 99.2 & 99.7 & 98.8 & 98.8 & 99.2 & 16,950.6 & 357.92 \\ VGG-19 & 99.8 & 100 & 99.6 & 99.6 & 99.8 & 18,804.4 & 361.25 \\ Densenet-201 & 99.6 & 100 & 99.1 & 99.1 & 99.5 & 199,074.8 & 230.78 \\ Efficientnet-b0 & 99.4 & 99.7 & 99 & 99 & 99.3 & 14,192.24 & 123.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics obtained by various pre-trained models on MEAT2C database.
## 2 State of the Art  
### Geolocation in Mobile Applications  
Geolocation is essential for applications that rely on user location [16]. The Haversine formula is commonly used to calculate the distance between two points on the Earth's surface, which is crucial for determining whether a user is within the proximity range of a shopping center. Previous studies have employed this formula to optimize location-based services and enhance the accuracy of location-based recommendation systems [17; 18].  
[PERSON] et al. [4] emphasize the significance of marketing strategies that leverage geolocation and contextual advertising in mobile applications. Their study underscores how these technologies enable businesses to offer highly personalized promotions based on the user's location.  
[PERSON] et al. [5] introduced an efficient approach for mobile location tracking and data reduction in proximity detection applications. Their work demonstrates how location tracking algorithms can significantly improve the accuracy of proximity detection and reduce data redundancy, which is crucial for systems such as the one proposed in our study.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets
### RA Methods in OFDMA Systems  
Most modern cellular networks utilize the orthogonal frequency division multiple access (OFDMA) scheme, and therefore, substantial research efforts have focused on optimizing its efficiency for RA. The authors of [19] propose a solution which assigns users spatial channels for all OFDMA symbols in a downlink (DL) frame according to each user's particular bit error rate and throughput requirements. A single-cell network serving a variable number of users, with a fixed number of channels, is considered. The channel state is assumed as constant (stationary/slow moving UEs) and (\(N_{BS}\times N_{UE}\)) multiple-input-multiple-output (MIMO) is considered, where \(N_{BS}\) and \(N_{UE}\) are the number of antennas for the BS and UE, respectively. The power and channel allocation problem is described through
# Damping of Beam Vibrations Using Tuned Particles Impact Damper
###### Abstract  
The presented paper reveals an innovative device which is the Tuned Particle Impact Damper (TPID). The damper enables the user change the dynamical features of the vibrating system thanks to rapidly tuning the volume of the container where the grains are locked. The effectiveness of proposed semi-active damping methodology was confirmed in experiments on vibrations of a cantilever beam excited by kinematic rule. Various damping characteristics captured for different volumes of the grains container and mass of granular material are presented. It is confirmed that the proposed TPID device allowed for efficient attenuation of the beam's vibration amplitude in the range of its resonant frequency vibrations.  
Particle Impact Damper; adaptive-passive damping; damping of vibrations; experiments +
Footnote †: journal: Applied Sciences
## 3 Results
### The Frequency of Antibiotic Resistance Genes among E. coli and S. aureus Isolates  
Using the PCR test results obtained in Table 4 and Figure 4, the incidence levels of resistance genes were determined for isolates of _E. coli_ and _S. aureus_. Among the various isolates of _E. coli_ isolated from both samples of shawarma and chicken burgers, the proportions of the _blazar_ and _tet(A)_ genes were 55.89% and 45.1%, respectively. _S. aureus_ strains, on the other hand, do not possess either of these genes. There were 21.5% and 47.31% of _blaz_ and _mecA_ genes present among the various isolates of _S. aureus_ isolated from shawarma and chicken burgers, respectively. In contrast, _E. coli_ strains did not possess either of these genes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Foodborne Pathogen**} & \multicolumn{6}{c}{**Antibiotic Resistance Genes**} \\ \cline{3-10}  & \multicolumn{2}{c}{_blazar_} & \multicolumn{2}{c}{_tet(A)_} & \multicolumn{2}{c}{_blaz_} & \multicolumn{2}{c}{_mecA_} \\ \cline{3-10}  & & **No.** & \% & **No.** & \% & **No.** & \% & **No.** & \% \\ \hline \multirow{6}{*}{_E. coli_} & Total & \multirow{2}{*}{57} & \multirow{2}{*}{55.89} & \multirow{2}{*}{46} & \multirow{2}{*}{45.1} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} \\  & (N = 102) & & & & & & & & \\ \cline{1-1} \cline{5-10}  & Shawarma & & & & & & & & \\ \cline{1-1} \cline{5-10}  & (N = 58) & & & & & & & & \\ \cline{1-1} \cline{5-10}  & Chick & & & & & & & & \\ \cline{1-1} \cline{5-10}  & burger & & & & & & & & \\ \cline{1-1} \cline{5-10}  & (N = 44) & & & & & & & & \\ \hline \multirow{6}{*}{_S. aureus_} & Total & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{20} & \multirow{2}{*}{21.5} & \multirow{2}{*}{44} & \multirow{2}{*}{47.31} \\  & (N = 93) & & & & & & & & \\ \cline{1-1} \cline{5-10}  & Shawarma & & & & & & & & \\ \cline{1-1} \cline{5-10}  & (N = 48) & & & & & & & & \\ \cline{1-1} \cline{5-10}  & Chick & & & & & & & & \\ \cline{1-1} \cline{5-10}  & burger & & & & & & & & \\ \cline{1-1} \cline{5-10}  & (N = 45) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The frequency of antimicrobial resistance genes in _E. coli_ and _S. aureus_ isolates recovered from shawarma and chicken burgers samples.  
Figure 3: Antibiotic resistance pattern of _E. coli_ (**A**); and _S. aureus_ (**B**) isolates.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Valid Inequalities
#### 3.4.3 Odd Path-Free Inequalities  
Let \(S\subseteq V\setminus\{s\}\) be a set of vertices of the underlying graph, \(V^{\mathcal{T}}(S)\coloneqq\{u_{\theta}\in V^{\mathcal{T}}\mid u\in S\}\) the corresponding vertices in \(V^{\mathcal{T}}\), and \(A^{\mathcal{T}}(S)\) the arcs of the subgraph of \(D^{\mathcal{T}}\) induced by \(V^{\mathcal{T}}(S)\). The SEC corresponding to \(S\) implies that the set \(A^{\mathcal{T}}(S)\) can contain, at most, \(|S|-1\) arcs.  
We obtain another class of inequalities by restricting ourselves to subsets with odd cardinality, i.e., \(|S|=2k+1\). A subset \(A^{\prime}\subseteq A^{\mathcal{T}}\) is path-free with respect to \(S\) if it contains no nontrivial paths in \(D^{\mathcal{T}}\), a nontrivial path consisting of more than one arc. If a set is path-free, than any arc in the intersection of \(A^{\prime}\) with a tour through \(D^{\mathcal{T}}\) connects two distinct vertices. As a result, the intersection can contain, at most, \(k\) arcs, yielding the following odd path-free inequality (see Figure 2):  
\[\sum_{(n,\varnothing,\theta)\in A^{\prime}}x_{uv,\theta}\leq k.\]  
We separate these inequalities heuristically by first determining promising subsets \(S\), and then computing the optimal set \(A^{\prime}\) for each promising subset \(S\). In order to determine a promising subset \(S\), we note that the odd path-free inequality is strongest for small values of \(k\). We therefore restrict ourselves to the case of \(k=1\), determining several promising sets based on the amount of induced flow \(y^{*}(S)\). To avoid very similar inequalities, we only consider the best promising 3-set containing each vertex \(v\in D\setminus\{s\}\). For each candidate set \(S\), we compute an optimal set \(A^{\prime}\) using an integer program.
## 2 Measurement System Design for Field Application  
The design of measurement systems for impedance spectroscopy, as portable measurement systems or in embedded solutions, is important for field application; realizing portable solutions includes several challenges, e.g., due to the required frequency range, which varies considerably according to the type of application. Especially for the measurement of impedance spectra at very-low-frequency values down to a few mHz or even below, many challenges should be solved. In this context, [PERSON] et al. [1] address the design, realization, and testing of a low-frequency impedance measurement platform. The realized system is portable, low-cost, and highly versatile. Signal generation and analysis are realized based on a personal computer and a soundboard. The implemented platform is adaptable to different applications with minimal effort.  
For embedded impedance spectroscopy, the main challenges are due to limited processing capability and memory availability. To calculate impedance, an AC analysis of both currents and voltages has to be carried out. This can be realized with various methods. In [2], several methods are analyzed in terms of speed, memory requirements, amplitude measurement accuracy, and phase measurement accuracy. [PERSON] et al. show that the linear sine fitting method reaches the highest accuracy in amplitude and phase determination but needs a high processing time and more memory capacity. Other methods, such as the Goertzel algorithm or fast Fourier transform with barycenter correction also have their merits under reduced calculation and storage resources.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Predictor Settings
#### 4.2.3 Weight Resolution  
Figure 9c shows the compression ratio as a function of the weight resolution parameter, \(\Omega\). The compression performance suffers significantly when the value of \(\Omega\) is close to the minimum allowed value. Increasing the value of \(\Omega\) means that more bits are used to represent the weight component; thus, the prediction is more accurate, which results in  
Figure 9: Compression ratio for different predictor parameters: (**a**) number of prediction bands, compression mode, and local sum type; (**b**) weight update scaling exponent final value, weight update scaling exponent initial value, and weight update scaling exponent change interval; (**c**) weight resolution; (**d**) register size.  
improved compression effectiveness and higher implementation complexity. However, the weight increment will always be a multiple of two when \(\Omega\geq v_{max}+D+2\) according to the definition of the next weight vector defined in the standard [23]. Essentially, the weight value's least significant bit remains unchanged with each update, which means that the available weight resolution is not being fully utilized. As a result, the compression ratio curve tends to flatten beyond a certain point. Simultaneously, a larger value of \(\Omega\) implies a need to use a greater number of bits for encoding. As indicated by the results of this experiment, \(\Omega=13\) produced a good compromise.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 4 Evaluations and Results
### Discussions  
The TARCS routing scheme proposed in this paper is more suitable for complex scenarios with highly dynamic changes in network topology. The advantages are:  
1. Node cognition of its surroundings. Moving nodes can sense the topology environment between itself and neighboring nodes accurately and in a timely manner, thereby improving the node's cognitive ability in regards to its environment.
2. Adaptability. Nodes can adjust the routing protocol in real time according to the perceived result, and the adaptability of network is stronger.
3. Scalability. The TARCS adaptive routing mechanism enables a FANET to adapt to changing scenarios. By modifying the topology change degree reference threshold/interval and adding alternate routing protocols, the TARCS can be extended to more scenarios and is compatible with more routing protocols.  
In addition, the TARCS routing scheme proposed in this paper also proposes a new processing idea to deal with a highly dynamic MANET, namely, the topology-change-driven protocol selection  
Figure 12: Comparisons of \(TCD_{nturk}\) with changed node density and fixed node density: (**a**) the node density is variable; (**b**) the node density is fixed.  
framework. Compared with the proactive, reactive and hybrid routing protocols, TARCS is more adaptable. The specific description is shown in Table 5.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Multi-Scale Transformation
#### 2.1.2 Based on Wavelet Transform Methods  
* Discrete wavelet transform The wavelet transform is a multi-resolution image decomposition tool that provides multiple channels to represent the features of an image through different frequency sub-bands at multiple scales [9]. To create the image with the corresponding wavelet coefficient, the source image must first undergo a wavelet transformation. Then, the approximation coefficient and detail coefficient corresponding to the source picture at each level are fused, respectively, in accordance with the fusion principles. A basic weighted average or a weighted average based on PCA (principal component analysis) might be used for this criterion. The final single-output fusion image is reconstructed using the fusion approximation coefficient and detail coefficient of each layer, and this is done using the inverse wavelet transform.
* Displacement invariant discrete wavelet transform Because the standard discrete wavelet transform (DWT) is not shift-invariant, utilizing it in the fusion approach will provide unstable and flickering results. The fusion output for image sequences must be consistent and steady with the original input sequence. Even a small shift in the source image will cause the DWT output to alter. The Harr DWT approach, which employs a fresh set of filters in each decomposition layer, can be utilized for displacement-invariant fusion in order to preserve DWT translation. This method resolves the issue by skipping the down-sampling stage in the decomposition procedure.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 2 Preliminary Considerations
### Generalized Advantage Estimation  
To reduce variance in policy gradient methods, [PERSON] et al. [26] proposed the generalized advantage estimator (GAE) as follows:  
\[\begin{split}\widehat{A}_{t}^{\text{GAE}(\gamma,\lambda)}& =\sum_{l=1}^{\infty}(\gamma\lambda)^{l}\delta_{t+l}^{V}\\ &=\sum_{l=1}^{\infty}(\gamma\lambda)^{l}(r_{t}+\gamma V(s_{t+l+1 })-V(s_{t+l}))\end{split} \tag{6}\]  
GAE is a general form of the following two advantage estimators:  
\[\hat{A}_{t}:=\delta_{t}=r_{t}+\gamma V(s_{t+1})-V(s_{t}) \tag{7}\]  
\[\hat{A}_{t}:=\sum_{l=0}^{\infty}\gamma^{l}\delta_{t+l}=\sum_{l=0}^{\infty} \gamma^{l}r_{t+l}-V(s_{t}) \tag{8}\]  
Equation (7) is biased but has low variance, while Equation (8) is unbiased but has high variance, due to the sum of many terms. GAE unifies these two advantage estimators and balances between the bias and the variance for advantage estimation by parameter \(\lambda\in(0,1)\). Equations (7) and (8) are the specific cases of Equation (6) when \(\lambda=0\) and \(\lambda=1\), respectively.
# Coexistence in Wireless Networks: Challenges and Opportunities
## List of Abbreviations  
* 5G 5 th--generation NOMA Non-orthogonal multiple access RAN Radio access network LAN Local area network BLE Bluetooth low energy LoRaWAN Long range wide area network MIMO Multiple input multiple output mm Millimeter GEO Geostationary orbit MEO Medium Earth orbit LEO low Earth orbit FrFT Fractional Fourier transform IoT Internet of things CNN Convolutional neural network FMCW Frequency modulated continuous wave OFDM Orthogonal frequency-division multiplexing LFM Linear frequency modulated EESS Earth exploration satellite service RLS Recursive least square FSS Fixed satellite services