esa-sceva/satcom-chunk-collection huggingface dataset:
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 4 Analysis and Discussion
### Overstretching in the Color-Matching Process  
During color matching, this study is based on a histogram specification matching method, which generally has a good matching effect in images with rich ground object types and uniform gray distribution ranges. However, for the Antarctic region, the type of ground features is relatively simple, and the brightness difference is obvious in the optical images. As a result, a general histogram specification method will cause the color of the image to become unreal. In this research, to avoid unreal visual effects caused by such overstretching phenomena, we introduced a threshold value T to perform segmentation matching for different types of ground objects. Figure 10 shows the visual effect of the color-matching results for two landscape images with different T values. Below each image are the corresponding histogram distributions.  
Figure 9: _Cont._Figure 9: Detailed comparison between the images before and after image color matching: **(a)** original detail image-1, **(b)** study result of image-1, **(c)** original detail image-2, **(d)** study result of image-2, **(e)** original detail image-3, **(f)** study result of image-3, **(g)** original detail image-4, **(h)** study result of image-4, **(i)** original detail image-5, **(j)** study result of image-5, **(k)** original detail image-6, **(l)** study result of image-6, **(m)** original detail image-7, and **(n)** study result of image-7.  
Figure 10: Image matching comparison under different threshold values: (**a**) original image-1, (**b**) general method result of image-1 (T = 0), (**c**) study result of image-1 (T = 90), (**d–f**) are the corresponding histograms of (**a–c**), (**g**) original image-2, (**h**) general method result of image-2 (T = 0), (**i**) study result of image-2 (T = 90), and (**j–l**) are the corresponding histograms of (**g–i**).  
It can be seen from the matching results in Figure 10 that the general histogram specification method and the proposed method both obtain relatively consistent histograms. However, the proposed method is more consistent with polar visual effects than general methods; the general method has greater contrast in the glacier area, and this visual effect is also confirmed by the statistical values of the average gradient (Table 7). It can be seen from the statistical results that the average gradient value of the general method is significantly higher than that of the original image, which indicates that the general method has caused excessive texture information and contrast in the matching results of this area. The method used in this paper is slightly higher than the original image, indicating that the contrast of the original image has been enhanced to a certain extent, and the visual effect is more reasonable.
## 3 Methodology
### The DACR Generator
#### 3.1.3 CS Item Rating Generation  
Based on the trained DAE, item \(\mathbf{v}_{i}\) will be converted to \(\mathbf{y}_{i}=<y_{i,1},y_{i,2},\ldots,y_{i,d^{\prime}}>\) where \(y_{i,j}\) is the value of compact content feature \(j\) for item \(\mathbf{v}_{i}\). To generate ratings for a CS item, the similarity between the CS and non-CS items should be evaluated first. In this study, [PERSON]'s correlation coefficient is used to assess the item similarity. Let \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\) be the compact content feature vectors for non-CS item \(i\) and CS item \(j\), respectively. The similarity between items \(i\) and \(j\) is defined as:  
\[S_{ij}=\frac{\sum\limits_{k=1}^{d^{\prime}}{(y_{ik}-\mathbf{y}_{i})\cdot(y_{ jk}-\mathbf{\tilde{y}}_{j})}}{\sqrt{\sum\limits_{k=1}^{d^{\prime}}{(y_{ik}- \mathbf{y}_{i})}^{-2}\cdot\sum\limits_{k=1}^{d^{\prime}}{(y_{jk}-\mathbf{ \tilde{y}}_{j})}^{2}}} \tag{3}\]  
where \(\mathbf{y}_{i}\) and \(\mathbf{\tilde{y}}_{j}\) are the mean values of vectors \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\). Next, we can derive the ratings of the CS item from their \(\alpha\) most similar non-CS items. The predicted rating for user \(u\) on CS item \(j\) is formulated as:  
\[\hat{r}_{uj}=\frac{\sum\limits_{i\in\mathcal{S}^{\alpha}(u,j)}{r_{ui}S_{ij}}}{ \sum\limits_{i\in\mathcal{S}^{\alpha}(u,j)}{S_{ij}}} \tag{4}\]  
where \(r_{ui}\) is the real ratings for user \(u\) on non-CS item \(i\), \(S^{\alpha}(u,j)\) denotes the set of the \(\alpha\) most similar non-CS items to a CS item \(j\) and user \(u\).
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### Path Loss Components of the 5G System and Link Budgets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & _Path Loss in dB_ & \
\begin{tabular}{c} **Shadow Fading** \\ **Std (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Default Values for the Applicability Range and** \\ **Antenna Height** \\ \end{tabular} \\ \hline \multicolumn{4}{c}{InH} \\ \hline LoS & \(PL=16.9\log_{10}(d)+32.8+20\log_{10}(f_{c})\) & \(\sigma=3\) & \(h_{\text{BS}}=3-6\,m\); \(h_{\text{UT}}=1-2.5\,m\) \\ NLoS & \(PL=43.3\log_{10}(d)+11.5+20\log_{10}(f_{c})\) & \(\sigma=3\) & \(h_{\text{BS}}=3-6\,m\); \(h_{\text{UT}}=1-2.5\,m\) \\ \hline \multicolumn{4}{c}{UMi} \\ \hline LoS & \(PL=22.0\log_{10}(d)+28.0+20\log_{10}(f_{c})\) & \(\sigma=3\) & \(10\,m<d<d_{\text{ap}}^{(1)}\) \\ \(PL=40\log_{10}(d)+7.8-18\log_{10}(h_{\text{US}}^{\prime})-18\log_{10}(h_{ \text{UT}}^{\prime})\) & \(\sigma=3\) & \(d_{\text{BS}}=10\,m^{(1)}\); \(h_{\text{UT}}=1.5\,m^{(1)}\) \\ LoS & \(+2\log_{10}(f_{c})\) & & \(10\,m<d_{1}+d_{2}<5000\,m\), \\ Manhattan grid layout & & & \(\pi<\min(d_{1},d_{2})^{(2)}\) \\  & & & \(\pi=20\,m\) (_streat width_) \\ NLoS & \(PL(d_{1},d_{1})=PL_{LoS}(d_{1})+17.9-12.5m_{i}\) & \(\sigma=4\) & \(h_{\text{BS}}=10\,m\); \(h_{\text{UT}}=1.5\,m\) \\  & \(+10\mu_{\text{log}_{10}}(d_{1})+30\log_{10}(f_{c})\) & & When \\  & \(PL_{LoS}\) & \(PL_{LoS}\) & \(PL_{LoS}\) + \(\pi\), \\  & \(PL_{LoS}\) : path loss of scenario \(\text{ULoS}\) and \(\text{L},l\in\{1,2\}\) & & The LoSFi is applied. \\ NLoS & \(PL=36.7\log_{10}(d)+22.7+26\log_{10}(f_{c})\) & \(\sigma=4\) & \(10\,m<d<2000\,m\), \\  & \(PL=PL_{LoS}+PL_{thr}+PL_{thr}+PL_{thr}\) & & \\ Manhattan grid layout (\(\theta\) known): & & & \\ O-to-I & \(\left\{\begin{array}{ll}P_{L_{0}}=PL_{10}(d_{out},d_{in})\\ P_{L_{thr}}=14+15(1-\cos\theta)^{2}\\ P_{L_{thr}}=0.5d_{in}\end{array}\right.\) & \(\sigma=7\) & \(10\,m<d_{out}+d_{in}<1000\,m\), \\  & \(PL_{thr}=10\,m,h_{UT}=3(n_{H}-1)+1.5\,m\) \\ \hline \hline \end{tabular} For hexagonal layout (\(\theta\) unknown) \(PL_{thr}=20\) _other values remain same_  
\end{table} 
Table 3: 5G system path loss model summary table [13].
## 3 Co-Channel Coexistence Mitigation Methods
### M\({}_{\rm B}\)-E-Signals  
Brief descriptionIn M\({}_{\rm B}\)-E-signals, as shown in Figure 3, only V\({}_{\rm NR}\) know the superframe structure, and they can only transmit packets in their own slot S\({}_{\rm NR}\). To make V\({}_{\rm 1 lp}\) aware of the slot reserved for NR, V\({}_{\rm NR}\) transmit three types of energy signals (ESs) that do not carry any data:  
1. ES-1 is sent during a TTI in S\({}_{\rm NR}\) that is not used by any V\({}_{\rm NR}\); the signal is transmitted by all V\({}_{\rm NR}\) that do not transmit data and sense the TTI as idle;
2. ES-2 is sent just before the slot S\({}_{\rm NR}\) to avoid V\({}_{\rm 1 lp}\) initiating a transmission at the end of the last part of S\({}_{\rm 1 lp/bd}\), which would then partially overlap with the following S\({}_{\rm NR}\); the signal is transmitted by all V\({}_{\rm NR}\) that sense the channel idle during that interval;
3. ES-3 is sent during the last OFDM symbol of each TTI in S\({}_{\rm NR}\) by all V\({}_{\rm NR}\) that transmitted during that TTI, with the scope to avoid V\({}_{\rm 1 lp}\), assuming that the channel is idle during that gap.  
Figure 2: Illustration of M\({}_{\rm A}\)-time-split, both vehicles V\({}_{\rm NR}\) and V\({}_{\rm 1 lp}\) are synchronized and know the structure of the superframe. Each type of vehicle could only transmit packets on their own slot S\({}_{\rm NR}\) or S\({}_{\rm 1 lp/bd}\). The subfigure (**a**) is from the V\({}_{\rm NR}\) perspective, and the subfigure (**b**) is from the V\({}_{\rm 1 lp}\) perspective. In this and the following figures, a vertical dashed red line is added to separate the NR-V2X slot from the \(\rm 1 lp/bd\) slot.  
When V11p sense the ESs, they assume that the channel is busy and defer any access to the channel; this is automatically performed by CSMA/CA, without the need to change the protocol.  
ConfigurationsThe energy signals can be implemented with variable slot settings, but this appears possible only if they are controlled, because all NR stations should have the same view of the slots.  
Modifications requiredThe NR stations need to transmit the ESs (which are new, as reported in Table 2). The only modification required in IEEE 802.11p in order to correctly detect the ESs, is to reduce the clear channel assessment (CCA) threshold of V11p (above which the channel is assumed busy) from \(-65\) dBm to \(-85\) dBm; otherwise, the probability that an ES is ignored is too high. This modification would require further studies, since it may also increase the probability of channel busy detection due to spurious emissions, normally ignored with the current threshold.  
Implications of NR instead of LTEThe use of NR-V2X instead of LTE-V2X implies that when the numerology is higher than 0, which means that the the duration of the TTI and the OFDM symbols are reduced (please refer to the first three columns of Table 3), the time duration of both ES-1 and ES-3 is shorter.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 5 System Architecture and Design Scheme
### Similarity Calculation  
Since user load data are not exchanged, federated learning cannot directly cluster the data at the nodes; instead, it clusters the model parameters of the clients. Similar clients are identified by analyzing their model parameters. To evaluate the similarity of the clients' model parameters while ensuring privacy, all clients first perform differential privacy federated learning until convergence. Subsequently, the server calculates the similarity of the local update vectors from the clients for the current round. The specific steps are as follows:  
1. Clients use the differential privacy federated averaging (DP-FedAvg) algorithm [29; 30] to train until convergence.
2. Equation (11) is used to calculate the cosine similarity between any two clients' local updates. The cosine similarity evaluates the similarity of two vectors by calculating the cosine of the angle between them. The closer the value is to 1, the more similar the two vectors are: \[e_{ij}=e_{ji}=\frac{\left\langle\Delta\theta_{i},\Delta\theta_{j}\right\rangle} {\left\|\Delta\theta_{i}\right\|\left\|\Delta\theta_{j}\right\|}\] (11)
## 2 Materials and Methods  
### Materials  
The monomers triethylene glycol dimethacrylate (TEGDMA), 95%, 2,2-Bis[p-(2\({}^{\prime}\)-hydroxy-3\({}^{\prime}\)-methacryloxypropoxy)phenylene]propane (Bis-GMA), and the solvent cyclohexane, \(\geq\)99.5%, were all provided by SIGMA-ALDRICH CHEMIE GmbH (Steinheim, Germany). The co-initiator 2-(dimethylamino)ethyl methacrylate (DMAEMA), 99%, and initiator camphorquinone, 98%, were purchased from J&K Scientific GmbH (Pforzheimer, Germany). A commercially available flumed silica nanopowder, AEROSIL OX 50, specific surface area (BET) 35-65 m\({}^{2}\cdot\)g\({}^{-1}\) and purity \(\geq\) 99.8%, was supplied by EVONIK GmbH (Hanau-Wolfgang, Germany). The 3-(trimethoxysil)propyl methacrylate (\(\gamma\)-MPS), 98%, used as a conventional methacrylate functionalized silane coupling agent, was purchased from J&K Scientific GmbH (Pforzheimer, Germany). The octyltrimethoxysilane (OTMS), 96%, and 3-(trimethoxysil)propyl acrylate (ACPS), 92%, were provided by SIGMA-ALDRICH CHEMIE GmbH (Steinheim, Germany). The catalyst propylamine, \(\geq\)99.0%, was supplied from Merck KGaA (Darmstadt, Germany). All other chemicals used were of reagent grade.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 7 Positioning Simulation Results
### Example 3
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\overline{N}_{\text{BS}}\) & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{10} & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{10} \\ \cline{2-9} \(N\) & 1 & 1 & 1 & 8 & 8 & 8 \\ \cline{2-9} \(d_{\text{BS}}\) & \multicolumn{2}{c}{390} & \multicolumn{2}{c}{276} & \multicolumn{2}{c}{565} & \multicolumn{2}{c}{399} \\ \cline{2-9}  & 50\% & 90\% & 50\% & 90\% & 50\% & 90\% & 50\% & 90\% \\ \hline OLS-OTP & 168 & 434 & 104 & 273 & 139 & 392 & 73 & 250 \\ \hline MLE-OTP & 114 & 265 & 73 & 162 & 86 & 240 & 57 & 156 \\ \hline OLS-MLE & 114 & 279 & 73 & 158 & 76 & 184 & 50 & 119 \\ \hline MLE-MLE & 110 & 259 & 70 & 150 & 77 & 188 & 53 & 132 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Positioning error, in meters, 50 th and 90 th percentiles of Example 3 (2 GHz). Base stations have omnidirectional (\(N=1\)) or directional antennas (\(N=8\)) and the average number of contacted BSs is \(\overline{N}_{\text{BS}}=5\) or 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\overline{N}_{\text{BS}}\) & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{10} & \multicolumn{2}{c}{5} & \multicolumn{2}{c}{10} \\ \cline{2-9} \(N\) & 1 & 1 & 8 & 8 & 8 \\ \cline{2-9} \(d_{\text{BS}}\) & \multicolumn{2}{c}{390} & \multicolumn{2}{c}{276} & \multicolumn{2}{c}{565} & \multicolumn{2}{c}{399} \\ \cline{2-9}  & 50\% & 90\% & 50\% & 90\% & 50\% & 90\% & 50\% & 90\% \\ \hline OLS-OTP & 158 & 394 & 97 & 250 & 107 & 295 & 54 & 198 \\ \hline MLE-OTP & 110 & 258 & 68 & 160 & 66 & 195 & 42 & 117 \\ \hline OLS-MLE & 109 & 275 & 66 & 146 & 59 & 144 & 38 & 92 \\ \hline MLE-MLE & 107 & 253 & 64 & 144 & 58 & 154 & 40 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Positioning error, in meters, 50 th and 90 th percentiles of Example 3 (28 GHz). Base stations have omnidirectional (\(N=1\)) or directional antennas (\(N=8\)) and the average number of contacted BSs is \(\overline{N}_{\text{BS}}=5\) or 10.
# Fast Implementation of NIST P-256 Elliptic Curve Cryptography on 8-Bit AVR Processor
## 2 Preliminaries  
### Elliptic Curve Cryptography  
ECC has been the most popular public key cryptosystem since [PERSON] [17] and [PERSON] [18] first introduced EEC in 1985, due to the short key sizes and low computational cost as compared to RSA, DSA, or Diffie-Hellman. Its security is based on Elliptic Curve Discrete Logarithm Problem (ECDLP), which is hard to be solved by general purpose subexponential algorithms. An elliptic curve \(E\) over finite field \(\mathbb{F}_{P}\) can be defined by a short Weierstrass equation, as follows:  
\[y^{2}=x^{3}+ax+b,\]  
where \(a,b\in\mathbb{F}_{P}\) and \(4a^{3}+27b^{2}\
eq 0\). It is common practice to choose the curve parameter \(a\) as \(-3\) in order to improve the performance of point arithmetic in scalar multiplication.  
In 1999, NIST proposed five prime-field curves for standardization [19], which adopts this approach. Hence, the NIST curves \(E\) can be defined by a short Weierstrass equation as \(y^{2}=x^{3}-3x+b\). The NIST P-256 curve use prime field \(\mathbb{F}_{P_{256}}\), defined by prime \(P_{256}=2^{256}-2^{224}+2^{192}+2^{96}-1\). This prime has a specific form, such that the prime is represented with addition or subtraction of several number of power of 2 and all of the exponents are multiples of 8, which are the advantages of manipulating on 8-bit devices. With this special form, modular reduction operation can be easily conducted by some additions while using the congruence \(2^{256}\equiv 2^{224}-2^{192}-2^{96}+1\pmod{P_{256}}\).
## 4 Results and Discussion
### Challenges
#### 4.2.2 Technical Challenges  
Compared to other challenges, more studies have covered technical challenges associated with emerging digital payment technologies. Thus, 32 studies out of 58 (see Table 6) included technical issues, such as privacy, security, access, fraud, lack of infrastructure, design and communication issues, speed, reliability, user anonymity, disruptive technology, operation, data management and instability (power outage). Overall, the technical challenges relate to '_safety and reliability issues_' and '_privacy issues_'. The studies highlighted security breaches and lack of protection against fraud and cyberattacks as reasons for reducing consumers' use of digital payment technologies. Similarly, in terms of 'privacy', the interception of confidential information, incomplete and interrupted transactions and frequent service denial faced by consumers lowered their trust and engagement towards digital payment technologies. It was indicated that the scope and magnitude of privacy risk had substantial effects on consumers' confidence and negatively impacted the global economy in general. Furthermore, it was found that although there are other negative impacts imposed by the privacy risks, the significant effect is the loss of customer trust [8]. Digital payment technologies have their own share of technical glitches. The payments do not always run smoothly. For example, UPI-based payment technology in India called BHIM (Bharath Interface for Money) reported having problems when installing and operating [14]. This shows that although consumers are ready to use digital payment technologies, there are challenges regarding infrastructure and availability. Furthermore, network failures negatively impacted system popularity [14]. Similarly, [PERSON] and [PERSON] [13] emphasized improving infrastructure facilities in the rural areas of India where poor network connectivity disrupts a transaction to result in incomplete transactions.  
Studies have also spotlighted issues related to cryptocurrencies, e.g., it was found that the existing privacy measures of Bitcoin are not sufficient to safeguard user privacy. Studies confirmed that Bitcoin users' privacy is compromised, even when the users are in strict compliance with the Bitcoin security requirements [76]. Subsequently, it was discovered that in the process of the fast-paced development of Bitcoin, it has been frequently exposed to various security threats where numerous accidents occur, such as Bitcoin trading platforms being stolen by hackers. This has made the future development of Bitcoin unforeseeable [82].
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Dimensionality Reduction
#### 3.3.4 Uniform Manifold Approximation and Projection (UMAP)  
It is an embedding method for dimensionality reduction and a newly proposed multivariate learning method for adequately representing the local structure while better incorporating the global structure [137]. UMAP scales well with massive datasets. UMAP uses a high-dimensional graph representing the data points to generate the fuzzy topological structure. The created high-dimensional graph is a weighted graph, with edge weights indicating the probability that two points are related. UMAP computes the similarity between high-dimensional data points using an exponential probability distribution, as given in Equation (21) [126]:  
\[p_{i|j}=exp\left(-\frac{d\big{(}x_{i},x_{j}\big{)}-\rho_{i}}{\sigma_{i}}\right)\,, \tag{21}\]  
where \(d\big{(}x_{i},x_{j}\big{)}\) represents the distance between the \(i-\text{th}\) and \(j-\text{th}\) data points, and \(\rho_{i}\) is the distance between the \(i-\text{th}\) data point and its first nearest neighbor(s). When the weight of the graph between \(i\) and \(j\) nodes is greater than the weight between \(j\) and \(i\) nodes, UMAP employs a high-dimensional probability summarization, as shown in Equation (22):  
\[p_{ij}=p_{i|j}+p_{j|i}-p_{i|j}p_{j|i} \tag{22}\]  
UMPA in the graph must indicate \(k\), the number of nearest neighbours, where \(k\) is calculated by Equation (23):  
\[k=2^{\sum_{i}p_{ij}} \tag{23}\]  
UMAP uses a probability measure for modelling distance in few dimensions, as shown in Equation (24):  
\[q_{ij}=\left(1+a\big{(}y_{i}-y_{j}\big{)}^{2p}\right)^{-1} \tag{24}\]  
For default UMAP, a \(\approx\) 1.93 and b \(\approx\) 0.79. UMAP employs binary cross-entropy (\(CE\)) as a cost function due to its ability to capture the global data structure, as illustrated in Equation (25):  
\[CE(P,Q)=\sum_{i}\sum_{j}\Bigg{[}p_{ij}\log\left(\frac{p_{ij}}{q_{ij}}\right)+ \left(1-p_{ij}\right)\log\left(\frac{1-p_{ij}}{1-q_{ij}}\right)\Bigg{]}, \tag{25}\]  
where \(P\) represents the probability similarity of high-dimensional data points and \(Q\) represents low-dimensional data points.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 5 Conclusions and Future Work
**Abbreviations**  
AC Alternation Current  
APF Active Power Filter  
BSCFD Billion Standard Cubic Feet per Day  
CSC Current Source Converter  
DC Direct Current  
DFIG Doubly Fed Induction Generators  
DMS Distribution Management Systems  
DR Demand Response  
DVR Dynamic Voltage Restorer  
EMI Electromagnetic Interference  
EMT Electromagnetic Transient  
ESS Energy Storage System  
FACTs Flexible AC Transmission systems  
FQSTS Fast Quasi-static time-series  
FSIG Fixed Speed Induction Generator  
HTF Harmonic Trap Filter  
HVAC High Voltage Alternating Current  
HVDC High Voltage Direct Current  
LCC Line Commutated Current  
PCC Point of Common Coupling  
PCU Power Conditioning Unit  
PLC Power Line Communication  
PMS Root Mean Square  
PMSG Permanent Magnet Synchronous Generator  
PQ Power Quality  
PWM Pulse Width Modulation  
PWN Pulse Width Modulation  
RES Renewable Energy Systems  
RLC Combination of Resistance/Inductor/Capacitor Circuit  
STATCOM Static Synchronous Compensator  
TBFW Three-Bridge Four Wire Inverter  
TDD Total Demand Distortion  
TDD Total Harmonic Distortion  
VSC Voltage Source Converter  
WF Wind Farm  
WPP Wind Power Plant  
WTG Wind Turbine Generator  
**List of Symbols**  
\(c\) subscript refers to compensator  
\(f\) subscript refers to final time  
\(g\) subscript refers to generator  
\(I\) current  
\(I\) subscript refers to inverter capacity  
**I** nodal current vector  
\(i,j\) subscripts refers to counter serial
## 3 Artificial Intelligence Techniques for 6G Networks
### Intelligent Spectrum Management  
In order to dynamically assign and manage frequency spectrum resources in 6G networks, intelligent spectrum management leverages AI approaches. AI systems can improve spectrum allocation, minimizing interference and increasing network capacity, by continually monitoring spectrum utilization and user requests. This adaptive strategy allows a variety of services and applications with different bandwidth needs, while enabling effective usage of the spectrum that is already available [11].