esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Therapeutically effective treatments of cancer are limited. To calibrate the efficiency of the novel technique we recently discovered to modulate cancer cell viability using tuned electromagnetic fields; H1299 human lung cancer cells were irradiated in a sweeping regime of W-band (75-105 GHz) millimeter waves (MMW) at 0.2 mW/cm\({}^{2}\) (2 W/m\({}^{2}\)). Effects on cell morphology, cell death and senescence were examined and compared to that of non-tumorigenic MCF-10A human epithelial cells. MMW irradiation led to alterations of cell and nucleus morphology of H1299 cells, significantly increasing mortality and senescence over 14 days of observation. Extended irradiation of 10 min duration resulted in complete death of exposed H1299 cell population within two days, while healthy MCF-10A cells remained unaffected even after 16 min of irradiation under the same conditions. Irradiation effects were observed to be specific to MMW treated H1299 cells and absent in the control group of non-irradiated cells. MMW irradiation affected nuclear morphology of H1299 cells only and not of the immortalized MCF-10A cells. Irradiation with low intensity MMW shows an antitumor effect on H1299 lung cancer cells. This method provides a novel treatment modality enabling targeted specificity for various types of cancers.  
W-band (75-105 GHz) MMW; H1299 human lung cancer cells; non-tumorigenic MCF-10A human epithelial cells; in vitro
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 3 Theoretical Analysis of Prestress Loss of BFRP Tendons
### Results and Discussions
#### 3.2.2 Prestress Loss during the In-Service Stage  
It can be found from Equations (3)-(6) that the length of the prestressed tendon had no impact on the prestress loss during the in-service stage, and as a result, the prestress loss did not vary with the change of the slab width. Given the same properties of prestressed tendon and concrete, the prestress loss during the in-service stage was a fixed value. The prestress loss of the BFRP tendon during the in-service stage is summarized in Table 6. It can be found that the total prestress loss during the in-service stage (5.2%) was much smaller than that in the prestressing stage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distribution Angle (\({}^{\circ}\))**} & \multirow{2}{*}{**Length of BFRP Tendom (mm)**} & \multicolumn{4}{c}{**Prestress Loss (\%)**} & \multirow{2}{*}{\(\frac{\sigma_{\rm 11}}{\sigma_{\rm 12}}\) (\%)**} \\ \cline{3-3} \cline{5-6}  & & \(\sigma_{\rm 11}\) & & \(\sigma_{\rm 12}\) & \(\sigma_{\rm 11}\)+\(\sigma_{\rm 12}\) \\ \hline
20 & 37,279 & 1.0 & 13.9 & 14.9 & 6.8 \\
25 & 30,169 & 1.3 & 11.4 & 12.6 & 9.9 \\
30 & 25,500 & 1.5 & 9.7 & 11.2 & 13.3 \\
35 & 22,229 & 1.7 & 8.5 & 10.2 & 16.7 \\
40 & 19,835 & 1.9 & 7.6 & 9.5 & 20.0 \\
45 & 18,031 & 2.1 & 7.0 & 9.1 & 23.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Theoretical prestress loss of BFRP tendons given the slab width of 12.75m.
## 3 Type-2 Fuzzy Logic
### Combining Information about Possibilities and Probabilities  
Considers a variable \(X\) taking values from \(X=\left(x_{1},\ x_{2},\ \ldots,\ x_{n}\right)\), \(\left(p_{1},\ p_{2},\ \ldots,\ p_{n}\right)\) provides probabilistic information, and \(\left(\pi_{1},\ \pi_{2},\ \ldots,\ \pi_{n}\right)\) provides information on the distribution of options.  
Author [32] originally proposed a probability for a fuzzy subset of \(F\). Suppose that \(F\) is a fuzzy subset of \(X\) for \(x_{i}\in X\), then the probability for fuzzy subset \(F\) is defined as:  
\[P(F)=\sum_{i=1}^{n}F(x_{i})p_{i}=\sum_{i=1}^{n}\pi_{i}p_{i}, \tag{14}\]  
where \(\pi_{i}\) is the value of the uncertainty of the possibilities with respect to the fuzzy set theory, \(p_{i}\) is the probability value for the fuzzy event \(x_{i}\).  
A few years later, [33] generalized the probability theory and constructed a mathematical notation for calculating the probability of fuzzy event \(A\), which is shown as:  
\[P(A)=\left\{\begin{array}{cc}\int\mu_{A}(x)f(x)dx,&\mbox{if }\ x\mbox{ is }\ continuous\\ \sum_{i}\mu_{A}(x_{i})f(x_{i}),&\mbox{if }\ x\mbox{ is }\ discrete\end{array}\right., \tag{15}\]  
where \(\mu_{A}(x)\) represents the probability distribution of \(X\), \(f(x_{i})\) indicates the distribution function of the probability \(X\).  
Suppose that \(x\) is a variable that takes values in space \(X\), the probability of uncertainty is modeled by \(P=\left(p_{1},\ p_{2},\ \ldots,\ p_{n}\right)\), \(F\) is a fuzzy subset of \(X\) with represented possibility of distribution \(\Pi=\left(\pi_{1},\ \pi_{2},\ \ldots,\ \pi_{n}\right)\). For each \(x_{i}\epsilon X\) the conditional probability distribution \(\widetilde{P_{i}}\) based on conditioning \(P\) with \(\Pi\) can be denoted as:  
\[\widetilde{P_{i}}=P(x_{i}|F)=\frac{P(\left(x_{i}\right)\cap F)}{P(F)}=\frac{p_ {i}\pi_{i}}{\sum_{j=1}^{n}p_{j}\pi_{j}}, \tag{16}\]  
where \(p_{i}\) indicates probability that \(x\) is taken of the \(V\), and satisfying requirements \(\sum_{i=1}^{n}p_{i}=1\).
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 4 Adaptive Estimation and Compensation for the CFO Scheme  
### Effects of CFO to MR-OFDM  
The OFDM technology is a common technique that has several advantages in regards to digital transmissions over frequency-selective fading channels. However, the principal weakness of OFDM is the sensitivity of frequency offsets because of motion-induced Doppler shifts and the LO frequency mismatch. In this paper, we assume that the system has been synchronized in time and in oscillator frequency. The OFDM signal of the baseband in the frequency domain is given as follows:  
\[x[n]=\sum_{k=0}^{N-1}X[k].e^{\beta\Pi\mathds{I}k/N} \tag{6}\]  
Here, \(X[k]\) is the symbol transmitted by the \(k\)-th subcarrier.  
As mentioned in Section 2, we focus on the fractional CFO, and the received baseband signal under the presence of an FFO of \(\varepsilon\) as follows:  
\[y_{l}[n]=\frac{1}{N}\sum_{k=0}^{N-1}H_{l}[k]X_{l}[k]e^{\beta\Pi(k+\varepsilon )/N}+z_{l}[n] \tag{7}\]  
Here, \(N\) is the FFT size; \(l\) is the sequence number of the symbol in the packet; and \(z\) is the Gaussian noise.  
The frequency domain of the received signal with an FFO of \(\varepsilon\) is as follows [17]:  
\[Y_{l}=FFT[y_{l}[n]]=\sum_{n=0}^{N-1}y_{l}[n]e^{-\beta\Pi\mathds{I}n/N}=(\sum_ {n=0}^{N-1}\frac{1}{N}\sum_{m=0}^{M-1}H[m]X_{l}[m]e^{\beta\Pi(m+\varepsilon)n /N}+z_{l}[n])e^{-\beta\Pi\mathds{I}n/N} \tag{8}\]  
\[Y_{l}[k]=e^{\beta\Pi\mathds{I}c(N-1)/N}\bigg{(}\frac{\sin(\[PERSON] \varepsilon)}{N\sin(\[PERSON]\varepsilon/N)}\bigg{)}H_{l}[k]X_{l}[k] \tag{9}\] \[+e^{\beta\Pi\varepsilon(N-1)/N}\sum_{m=0,m\
eq k}^{N-1}\frac{ \sin(\Pi(m-k+\varepsilon))}{N\sin(\Pi(m-k+\varepsilon)/N)}H[m]X_{l}[m]e^{\beta \Pi(m-k)(N-1)/N}+Z_{l}[k]\] \[Y_{l}[k]=e^{\beta\Pi\varepsilon(N-1)/N}\bigg{\{}\frac{\sin(\Pi \varepsilon)}{N\sin(\Pi\varepsilon/N)}\bigg{\}}H_{l}[k]X_{l}[k]+I_{l}[k]+Z_{l}[k] \tag{10}\]  
\[I_{l}[k]=e^{\beta\Pi\varepsilon(N-1)/N}\sum_{m=0,m\
eq k}^{N-1}\frac{\sin(\Pi (m-k+\varepsilon))}{N\sin(\Pi(m-k+\varepsilon)/N)}H[m]X_{l}[m]e^{\beta\Pi(m-k) (N-1)/N} \tag{11}\]  
where \(I_{l}[k]\) represents the ICI from other subcarriers to the \(k\)-th subcarrier with an FFO of \(\varepsilon\).  
As known, if the system has an FFO of \(\varepsilon=0\), the ICI value as shown in Equation (11) equals 0, which illustrates that the orthogonality of subcarriers was guaranteed. Orthogonality is very important with the OFDM system, as it causes a reduction in the system performance. The signal is distorted because of FFO with amplitude and phase distortions, shown in the first term of Equation (9).
## 3 Results
### Regression Models with All Parameters
#### 3.3.2 PlanetScope Regression Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & _p-Value\({}^{1}\)_ & **RMSE (g/m\({}^{2}\))** & **(\(n\))** \\ \hline
5 May & RF & 0.44 & 0.001 & 0.92 & 15 \\  & SVR & 0.51 & 0.001 & 0.90 & 15 \\ \hline
12 May & RF & 0.45 & 0.001 & 0.62 & 15 \\  & SVR & 0.78 & <0.001 & 0.53 & 15 \\ \hline
20 May & RF & 0.05 & NS & 2.24 & 15 \\  & SVR & 0.38 & 0.001 & 1.90 & 15 \\ \hline
27 May & RF & 0.63 & <0.001 & 2.84 & 15 \\  & SVR & 0.50 & 0.001 & 3.40 & 15 \\ \hline
4 June & RF & 0.07 & NS & 6.24 & 15 \\  & SVR & 0.05 & NS & 5.54 & 15 \\ \hline
5 and 12 May & RF & 0.44 & <0.001 & 1.06 & 29 \\  & SVR & 0.29 & 0.001 & 1.08 & 29 \\ \hline
12 and 20 May & RF & 0.79 & <0.001 & 1.37 & 29 \\  & SVR & 0.71 & <0.001 & 1.03 & 29 \\ \hline
20 and 27 May & RF & 0.63 & <0.001 & 3.24 & 29 \\  & SVR & 0.50 & <0.001 & 3.79 & 29 \\ \hline
27 May, 4 June & RF & 0.63 & <0.001 & 4.77 & 29 \\  & SVR & 0.63 & <0.001 & 4.94 & 29 \\ \hline
5, 12, and 20 May & RF & 0.50 & <0.001 & 1.73 & 44 \\  & SVR & 0.68 & <0.001 & 1.43 & 44 \\ \hline
12, 20, and 27 May & RF & 0.83 & <0.001 & 1.77 & 44 \\  & SVR & 0.67 & <0.001 & 3.16 & 44 \\ \hline
20 and 27 May, 4 June & RF & 0.75 & <0.001 & 3.64 & 44 \\  & SVR & 0.74 & <0.001 & 4.59 & 44 \\ \hline \end{tabular} \({}^{1}\) NS = non-significant.  
\end{table} 
Table 8: Statistics for the validation of the PlanetScope canopy nitrogen models with 24 variables as a function of the date and the modelling approach (RF or SVR).
# Testbed for Experimental Characterization of Indoor Visible Light Communication Channels
## 3 Experimental Testbed
### Mockup Description  
Figure 2 shows the design of a 0.6 m \(\times\) 0.6 m \(\times\) 0.3 m (length \(\times\) width \(\times\) height) mockup by using AutoDesk(r) Fusion 360. In this first version, the ceiling was equipped with four WLED lamps as it can be observed in the general scheme and the zoom of the ceiling of the mockup design. However, the ceiling can be easily modified to have different lamp arrangements. Configuring receiver location and orientation is enabled by using precise servo and stepping motor systems managed by an Arduino microcontroller. In addition, the interior of walls is painted with white plastic paint to emulate the effect of light reflection. Most of plastic pieces of this mockup were manufactured using 3D printers. Walls are fabricated with methacrylate by using laser cutter, which was also used to cut the metal bars to support the structure. Nylon filament was used to manufacture the pieces holding the LED electronic boards so as to dissipate heat during operation.  
The optical receiver consists of a \(2\times 2\) matrix of photodetectors (Hamamatsu S5107 P-i-N photodiodes) with a total active area of 100 mm\({}^{2}\) connected to a transimpedance amplifier for each of the four channels. Emitters are based on standard commercial off-the-shelf (COTS) white LEDs which are fed by voltage-controlled current sources designed with operational amplifiers and power transistors. Figure 3 shows a 3D view of the electronic board designs of receiver and emitters by using KiCAD.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 3 Proposed Algorithm
### Global Optimal Layer Selection via BayesOpt
#### 3.4.2 Probability of Improvement (PI)  
After the GP regression, the acquisition function determines which point in the input space should be evaluated next via proxy optimization:  
\[n_{k+1}=\arg\max_{n}f(n,n_{1:k}), \tag{8}\]  
where \(f(.)\) denotes the acquisition function.  
One intuitive approach is to maximize the probability of improving the best current point \(n_{k}^{*}\), which is called as \"probability of improvement (PI)\". Under the GP regression, the PI can be calculated as:  
\[PI(n,n_{1:k})=P(L(n)\leq L(n_{k}^{*}))=\Phi(\gamma(n)), \tag{9}\]where \(\gamma(n)\) is  
\[\frac{L(n_{k}^{*})-\mu_{k}(n)}{\sigma_{k}(n)}\]  
and \(\Phi\) denotes the cumulative distribution function of the standard normal distribution.  
The drawback of PI is that this strategy is based on pure exploitation. Points that have a high probability of being smaller than \(L(n_{k}^{*})\) will be drawn over points that offer larger gain but less certainty [28; 29; 30].
## 2 Design and Methods
### Motion Analysis  
Motion analysis for the RWTD is instrumental in deriving key design parameters to enhance its performance and safety. This method aids in identifying areas of instability during different phases of the walking training process, allowing designers to refine the device's structure for improved stability. Moreover, motion analysis aligns the device's motion with natural motion optimizing its effectiveness in facilitating a more natural and beneficial training experience for patients.  
The analysis also plays a crucial role in enhancing safety by identifying potential concerns like sudden movements or abnormal trajectories. Addressing these issues in the design phase ensures that the RWTD prioritizes patient safety, preventing injuries or discomfort during operation. Additionally, motion analysis helps assess the device's ergonomics and user-friendliness, allowing for refinement of design parameters related to the range of motion, joint angles, and user interface for an optimized user experience.  
Insights gained from motion analysis further contribute to load distribution optimization across the device during various motion phases. This informs adjustments to design parameters related to load-bearing components, material, and structural integrity, preventing excessive stress on specific parts of the device. Moreover, the customization of the RWTD to accommodate diverse patient needs, such as weight, height, and range of motion, is made possible through the insights garnered from motion analysis. In essence, motion analysis is integral in shaping an RWTD that aligns seamlessly with natural biomechanics, prioritizing patient well-being and ensuring an effective rehabilitation experience.
# The Long and Winding Road to Cardiac Regeneration
## 2 Stem Cell Therapy
### Adult Stem Cells
#### 2.1.3 Endothelial Progenitor Cells  
Endothelial progenitor cells (EPCs) are a promising cell type for cardiac regeneration due to their ability to promote angiogenesis and repair damaged blood vessels [105; 106]. EPCs are derived from various sources, including bone marrow, peripheral blood, and umbilical cord blood. These cells possess unique characteristics that make them attractive for therapeutic applications in cardiac regeneration [107; 108].  
The advantages of EPCs lie in their regenerative potential. EPCs have the ability to differentiate into mature endothelial cells and contribute to the formation of new blood vessels. They also secrete proangiogenic factors, such as vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF), which further enhance angiogenesis and tissue repair [109]. Additionally, EPCs possess immunomodulatory properties and can promote the recruitment of other regenerative cells to the injured cardiac tissue [110].  
However, EPC-based therapies for cardiac regeneration face certain limitations. The low number and limited lifespan of circulating EPCs in the peripheral blood pose challenges for their isolation and expansion in therapeutic quantities. Moreover, EPCs derived from older individuals or patients with cardiovascular diseases may exhibit reduced regenerative potential. Additionally, the engraftment efficiency of transplanted EPCs into the myocardium remains a concern, as many cells may be lost due to poor survival or limited retention [111].  
To overcome these limitations, several strategies to enhance the mobilization, expansion, and survival of EPCs are under investigation. Genetic modification, preconditioning, and tissue engineering approaches are being explored to improve the therapeutic efficacy of EPCs in cardiac regeneration. Furthermore, combined therapies using EPCs with other cell types, such as cells of mesenchymal origin, and biomaterial-made scaffolds are being studied to enhance their regenerative capabilities [112; 113].  
The EPCs successful clinical translation requires addressing the challenges related to cell sourcing, expansion, and engraftment. Future research efforts aimed at optimizing EPC-based therapies will contribute to their effective utilization in treating cardiovascular diseases and promoting cardiac tissue regeneration [114; 115].
# Structural Augmentation in Seismic Data for Fault Prediction
## 3 Seismic Fault Detection Based on U-Net
### Network Training
#### 3.3.2 Network Training Based on Synthetic Data  
The second type of methods use synthetic seismic data and theoretical fault labels to train the neural network [68,70,73]. These methods effectively alleviate the problem of training datasets scarcity and excel in some seismic data applications. In this paper, we make some minor changes based on the work of [PERSON] et al. [68] because these changes do increase the applicability of neural networks in fault recognition, especially for structures dominated by strike-slip faults. To briefly demonstrate the effectiveness of these changes, we use the neural network Net_syn_Wu from the open-source workflow by [PERSON] et al. [68] for comparison. To compare fairly with the structural data augmentation, we use Equations (1)-(14) to generate 800 synthetic data-label pairs to train the neural network Net_syn_our (Figure 9b).
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### Expert Knowledge
#### 4.4.2 Processing of Expert Knowledge  
To ensure that expert knowledge can be effectively applied in the DT system, the collected expert knowledge must be standardized and systematized.  
As experts may use inconsistent terminology and standards in their assessments, the collected expert knowledge cannot be applied directly. It must first be standardized to ensure a uniform representation of knowledge. For example, concerning the \"crack width\" assessment indicator, a unified quantitative standard must be established to categorize the severity as \"minor\", \"moderate\", or \"severe\". Standardization ensures that all expert knowledge is processed consistently during later monitoring and evaluation phases.  
Expert judgment is often accompanied by uncertainty and ambiguity, particularly in assessing complex structural damage. To effectively manage this expert knowledge, the system introduces fuzzy logic [51]. Fuzzy logic uses membership functions to convert qualitative judgments from experts into quantitative data, enabling the system to flexibly handle complex assessments. For instance, concerning \"crack width\", multiple fuzzy sets can be defined (e.g., \"minor crack\", \"moderate crack\", and \"severe crack\"). Each crack width is mapped to the appropriate fuzzy set through a membership function, thereby quantifying the crack severity. The system then uses the membership degrees for assessment and reasoning, providing a specific evaluation of the damage severity.  
The standardized expert knowledge is used to build an expert knowledge base, which is a system for the structured storage and management of expert knowledge. Through the systematization and integration of expert experience and judgments into the DTM, the expert knowledge base classifies knowledge according to the different structural components of the bridge (e.g., piers, decks, and supports) and includes information on damage types, causes, and monitoring methods.  
To further enhance the decision-making capabilities of the DTM, this expert knowledge base harmonizes qualitative insights with quantitative data from sensors and spatial models. This integration ensures a cohesive analysis of structural health, reducing fragmentation and supporting actionable insights for monitoring and maintenance.  
To ensure the continuing suitability of the expert knowledge base, it must be dynamically updated based on the latest bridge monitoring cases, technological advances, and standards. By verifying and calibrating the rules within the knowledge base, its suitability across diverse bridge structures and environmental conditions is enhanced.
###### Abstract  
The sorption of organic pollutants to marine plastic litter may pose risks to marine organisms, notably for what concerns their intake and transfer through microplastic (MP) ingestion. This study investigated the effects of polystyrene MP loaded with chrysene (CHR) on early-stage and physiological endpoints measured in the Mediterranean mussel _Mytitus galloprovincialis_. The same concentrations of virgin microplastics (MP) and MP loaded with 10.8 \(\mu\)g CHR/mg (CHR-MP) were administered to mussel gametes/embryos (25 \(\times\) 10\({}^{3}\) items/mL) and adults (5\(\times\) 10\({}^{3}\) items/L); further treatments included 0.1 mg/L of freely dissolved CHR and a second CHR concentration corresponding to that vehiculated by CHR-MP during exposure (3.78 \(\mu\)g/L and 0.73 ng/L for gamete/embryos and adults, respectively. None of the treatments affected gamete fertilization, while 0.1 mg/L CHR induced embryotoxicity. In adults, CHR-MP and MP similarly affected lysosomal membrane stability and neutral lipids and induced slight effects on oxidative stress endpoints. CHR affected tested endpoints only at 0.1 mg/L, with lysosomal, oxidative stress and neurotoxicity biomarkers generally showing greater alterations than those induced by CHR-MP and MP. This study shows that the CHR sorption on MP does not alter the impact of virgin MP on mussels and may pose limited risks compared to other routes of exposure.  
POPs; chemical sorption; bivalves; embryotoxicity; biomarkers Article
## 3 Result and Discussion
### Engine Emissions  
#### 3.7.1 Carbon Monoxide (CO)  
The variations of carbon monoxide (CO) for various fuel blends are shown in Figure 7. The CO emission of biodiesel blends include of B10 and B20 was lower than diesel. The low CO emission of biodiesel relative to diesel is due to the high cetane number of biodiesel [55], because biodiesel-diesel blends contain some extra oxygen in their molecules. Therefore, the oxygen contained in the fuel enhances complete combustion in the cylinder and reduces CO emission.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TSN & Time-Sensitive Networking \\ Industrial IoT & Industrial Internet of Things \\ AR & Augmented Reality \\ VR & Virtual Reality \\ WLAN & Wireless LAN \\ NIC & Network Interface Card \\ PTP & Precision Time Protocol \\ PHC & PTP Hardware Clock \\
5 GS & 5G System \\ UE & User Equipment \\ RAN & Radio Access Network \\ UPF & User Plane Function \\ AVB & Audio Video Bridging \\ CNC & Centralized Network Configuration \\ CUC & Centralized User Configuration \\ FTM & Fine Timing Measurement \\ TM & Timing Measurement \\ AP & Access Point \\ EDCA & Enhanced Distributed Channel Access \\ TDMA & Time-Division Multiple Access \\ CTS & Clear To Send \\ RTS & Request To Send \\ SIFS & Short Interframe Space \\ OFDMA & Orthogonal Frequency Division Multiple Access \\ RU & Resource Unit \\ TF & Trigger Frame \\ AID & Association Identifier \\ OBO & OFDMA Backoff \\ TWT & Target Wake Time \\ SP & Service Period \\ SR & Spatial Reuse \\ OBSS & Overlapping Basic Service Set \\ PD & Preamble Detection \\ CSR & Coordinated Spatial Reuse \\ AC & Access Category \\ TXOP & Transmit Opportunity \\ AF & Application Function \\ QoS & Quality of Service \\ TSCAI & Time-Sensitive Communication assistance Information \\ PSF & Per-Stream Filtering and Policing \\ PCF & Policy Control Function \\ gNB & generation NodeB \\ SMF & Session Management Function \\ CCA/Cs & Clear Channel Assessment Carrier Sense \\ \end{tabular}