esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Algorithms for Computing the Inner Edit Distance of a Regular Language via Transducers
## 4 An \(O(n^{2}d)\) Algorithm for Edit Distance via Error-Detection
For the 'if' part of the second statement, assume that \(\mathbf{t}^{\prime\prime}_{k+1}\) realizes a partial identity. Consider any accepting computation \(C\) of \(\mathbf{t}^{\prime}_{k+1}\). We show that the label of \(C\) must be of the form \((w,w)\). If \(C\) is already a computation of \(\mathbf{t}^{\prime}_{k}\), then this holds, as \(\mathbf{t}^{\prime}_{k}\) realizes a partial identity. Now suppose that \(C=C_{1}C_{2}\) such that \(C_{1}\) is a computation of \(\mathbf{t}^{\prime}_{k}\) and \(C_{2}\) is a path in \(\mathbf{t}^{\prime}_{k+1}\) that starts with a transition as in (8) and then uses transitions as in (9). Let \(([k],p,p^{\prime})\) be the last state of \(C_{1}\), which is also the first state of \(C_{2}\). Then, \(C_{1}\) has some label \((w_{1},w_{1})\). In addition, the path  
\[\left([-1]\xrightarrow{\lambda/\lambda}([k],p,p^{\prime})\right)C_{2}\]  
is an accepting computation of \(\mathbf{t}^{\prime\prime}_{k+1}\), which implies that it has some label \((w_{2},w_{2})\). Hence, the label of \(C\) is \((w_{1}w_{2},w_{1}w_{2})\) and, therefore, \(\mathbf{t}^{\prime}_{k}\) realizes a partial identity. The improved algorithm is shown next:  
Algorithm DistErrDetect  
1. Input: NFA a
2. Construct the transducer \(\mathbf{sid}_{1}\) realizing the channel \(\mathbf{sid}(1)\)--see Figure 1
3. Construct the trim transducer \(\mathbf{t}^{\prime}_{1}=\mathbf{sid}_{1}\downarrow\mathbf{a}\uparrow\mathbf{a}\)
4. Let \(k\gets 1\)5. **while** (\(\mathbf{s}\) realizes a partial identity) 1. Construct \(\mathbf{t}^{\prime\prime}_{k+1}\) and \(\mathbf{t}^{\prime}_{k+1}\) from \(\mathbf{t}^{\prime}_{k}\) using the optimized construction 2. Let \(\mathbf{s}\leftarrow\mathbf{t}^{\prime\prime}_{k+1}\) 3. Let \(k\gets k+1\)
6. **return**\(k\)  
**Theorem 1**: _Algorithm DistErrDetect computes the edit distance of a language given via a trim NFA \(\mathbf{a}\) in time_  
\[O(n^{2}dr^{2}),\]  
_where \(d\) is the computed edit distance, \(n=|T_{\mathbf{a}}|\), and \(r\) is the cardinality of the alphabet used in \(T_{\mathbf{a}}\)._  
The correctness of the algorithm follows from the optimized construction and the above lemma. For the time complexity of the algorithm, we note the following. First, \(\mathbf{t}^{\prime}_{1}\) is constructed in time \(O(|\mathbf{a}|^{2}r^{2})\). Then, \(\mathbf{t}^{\prime\prime}_{2},\ldots,\mathbf{t}^{\prime\prime}_{d}\) are constructed according to the optimized construction. Each of these is constructed in time \(O(|\mathbf{a}|^{2}r^{2})\) and has \(O(|Q_{\mathbf{a}}|^{2})\) states and \(O(|T_{\mathbf{a}}|^{2}r^{2})\) transitions. In addition, each \(\mathbf{t}^{\prime\prime}_{k}\) is tested for partial identity in time \(O\big{(}|T_{\mathbf{a}}|^{2}r^{2}+|Q_{\mathbf{a}}|^{2}r\big{)}\), which is \(O(|\mathbf{a}|^{2}r^{2})\).
# A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching
## 5 Results
Then, these optimal values of the CMOS inverter parameters are used to calculate the fall time (t), the rise time (t), and the propagation delay times (tPHL and tPHL). Table 8 summarizes the results obtained using the MA for each design set in Case III, which satisfies the symmetrical output waveform with equal rise and fall times, and the symmetrical propagation delay time (tPHL equal tPHL). Table 9 shows a comparison of the MA results with results obtained by different optimization algorithms. The optimal value of the CF (in ps) of the MA is compared to the optimal value of CF using PSOCFIWA [25], DE [26], ALC-PSO [29], CRPSO [27], PSO [30], HS-DE [32], and SOS [32]. The results show that the suggested MA outperforms most of the widely used optimization methods in finding the optimal CMOS inverter design with the lowest CF value. Figure 16 shows the convergence plot of the objective function for the eighth design set. The optimum value of the objective function is 4.67217 ps and it converges after 18 iterations. Figures 17-19 show the convergence plots of the inverter design parameters: C, \(\rm{(W/L)_{n}}\) and  
Figure 16: The MA results for the eighth design set of Case III. Figure 17: Plot of \(\rm C_{L}\) with iterations for the eighth design set of Case III. Figure 18: Plot of \(\rm(W/L)_{n}\) with iterations for the eighth design set of Case III. Figure 23: Propagation delay times using Spice for the eighth design set of Case III. (**a**) High to low (**b**) Low to high. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Set Number** & \(\mathbf{C_{L}}\) **(pF)** & **(W/L)n** & **(W/L)p** & \(\mathbf{t_{f}}\) **(ns)** & \(\mathbf{t_{f}}\) **(ns)** & \(\mathbf{t_{f}}\) **(ns)** & \(\mathbf{t_{f}}\) **(ns)** & \(\mathbf{t_{f}}\) **(ns)** & \(\mathbf{t_{f}}\) **(ns)** \\ \hline
1.0 & 0.767635 & 3.6 & 19.3 & 2.664692 & 2.076923 & 1.556271 & 1.0675 & 1.076540 \\ \hline
2.0 & 0.715929 & 3.4 & 18 & 2.638971 & 2.03107 & 1.543645 & 1.065625 & 1.043884 \\ \hline
3.0 & 0.603656 & 6.7 & 35.8 & 1.275526 & 0.9818394 & 0.806703 & 0.570023 & 0.530366 \\ \hline
4.0 & 0.239064 & 2.7 & 14.2 & 1.145440 & 0.9706936 & 0.738426 & 0.567141 & 0.346031 \\ \hline
5.0 & 0.368314 & 2.2 & 11.6 & 1.946853 & 1.6663289 & 1.184149 & 0.884956 & 0.579717 \\ \hline
6.0 & 0.386602 & 3 & 16.2 & 1.594699 & 1.301161 & 0.985 & 0.718125 & 0.472680 \\ \hline
7.0 & 0.414941 & 4.6 & 24.6 & 1.234606 & 0.978176 & 0.785312 & 0.569062 & 0.472680 \\ \hline
8.0 & 0.394871 & 7.3 & 39 & 0.840474 & 0.6761915 & 0.565393 & 0.416783 & 0.312893 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Spice results for Case III. Figure 22: Rise time and fall time using Spice for the eighth design set of Case III. (**a**) Rise Time (**b**) Fall Time.
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 4 Case Studies
### Experiments
#### 4.4.1 Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **A\(\rightarrow\)B** & **A\(\rightarrow\)C** & **A\(\rightarrow\)D** & **B\(\rightarrow\)A** & **B\(\rightarrow\)C** & **B\(\rightarrow\)D** & **C\(\rightarrow\)A** & **C\(\rightarrow\)B** & **C\(\rightarrow\)D** & **D\(\rightarrow\)A** & **D\(\rightarrow\)B** & **D\(\rightarrow\)C** & **Avg** \\ \hline ELM & 87.27 & 82.93 & 78.32 & 85.68 & 89.13 & 84.82 & 86.31 & 91.94 & 84.65 & 83.33 & 90.34 & 86.92 & 86.00 \\ KNN & 87.31 & 87.67 & 90.65 & 88.91 & 94.37 & 94.70 & 87.79 & 95.89 & 94.50 & 78.87 & 89.38 & 84.60 & 89.55 \\ BP & 95.86 & 93.35 & 92.53 & 87.91 & 94.25 & 95.04
## 2 Materials and Methods  
### System Components  
The proposed blockchain-based electric vehicle energy management and forecasting system adopts a decentralized architecture consisting of three key components: electric vehicles, aggregators, and blockchain. Each component performs distinct roles, and their interactions enable the overall functionality of the system.  
**Electric vehicles:** Equipped with sensors and black boxes, electric vehicles collect various data while driving. These data are used to predict energy consumption through a deep neural network model (local model). The trained model's parameters are encrypted and signed for privacy protection before transmission to the aggregator.  
**Aggregator:** The aggregator collects individual model parameters from multiple electric vehicles and uses the FL-QLMS technique to create a robust global model (global model). This global model is then securely uploaded to the blockchain.  
**Blockchain:** The blockchain verifies the integrity of the global model submitted by the aggregator and manages it transparently. Multiple nodes in the blockchain network validate the model's validity through a consensus algorithm. Only approved models are recorded to prevent tampering and forgery. Each electric vehicle is granted access rights to download the latest global model safely.  
The interaction process between these components is as follows:  
1. **Data collection and local training**: Electric vehicles collect data and train a local energy consumption prediction model.
2. **Data transmission**: The trained local model parameters are encrypted and sent to the aggregator.
3. **Data aggregation and global model creation**: The aggregator collects, selects, and aggregates the local models using the FL-QLMS technique to create a global model.
4. **Blockchain verification and storage**: The global model is submitted to the blockchain, where its integrity is validated and it is securely recorded.
5. **Model distribution**: The verified global model is made available for download by electric vehicles, which update their local models, thus continuously improving their performance.  
Figure 1 illustrates the flow of data between the main components of the system. Electric vehicles collect data, train local models, and transmit encrypted data to the aggregator. The aggregator aggregates the data, applies the FL-QLMS algorithm, and creates a global model, which is then sent to the blockchain for verification, storage, and distribution. The blockchain ensures the integrity and security of the data and makes the global model available to electric vehicles, completing the cycle.  
Figure 1: System components for electric vehicle data management.
## 1 Introduction
### Tracking Activity Levels and Acute Coronary Syndrome  
Physical activity is a major component in a CR program and is integral to recovery from and ACS event [18]. This is primarily because physical activity has been shown to mitigate the progression of atherosclerosis which leads to ACS [19]. A substantial body of evidence shows that increased physical activity results in significant improvements in many ACS risk factors including hypertension, dyslipidemia, obesity, insulin resistance and psychological health. However, these health benefits are only achieved when patients engage in the recommended total weekly amount of physical activity of at least moderate-intensity level for 150 minutes per week or more [19]. One of the main aims of CR is to encourage patients to achieve the recommended physical activity levels [10; 18]. Cardiac rehabilitation also has medical, psychological, social and behavioral benefits and can increase functional capacity [19].  
The use of wearable physical monitoring devices offers significant potential for researchers and clinicians working to promote or measure a patient's activity levels. Activity tracking devices have the potential to effect physical activity behavior change, make a direct and real-time impact on self-management of physical activity and offer clinicians real world assessments of their patients' daily activity patterns [20]. Wearable fitness tracking devices are ideal to use in the post-discharge ACS population to help track physical activity and to motivate sustained changes in moderate-intensity physical activity [21]. Current devices have become more user-friendly and can run for 5-7 days before a recharge is required [21].  
Finally, global positioning systems (GPS) devices are now commonly used in society e.g., in high performance sport analysis, driving and even tracking movements of pets. Specialist software is required for the download and analysis of these data. The GPS data maps the distance and locations travelled and these can be matched to service locations. Unlike activity tracking devices, GPS devices do not differentiate travel by walking or vehicle nor educate or foster engagement with the tracking process.
# Experimental Investigation on the Dynamic Characteristics of Bubble-in-Chain Near a Vertical Wall
###### Abstract
However, as _Re_ surpasses 100, this scenario undergoes a transformation and the bubble encounters an outward-directed lifting force away from the wall [14]. Under conditions of high Reynolds number and low Weber number, near-wall spherical bubbles in an ideal fluid tend to migrate towards the wall due to bubble-wall interactions, resulting in repeated collisions. The collision frequency decreases as the initial distance between the bubble and the wall increases. Simultaneously, with increasing initial distance, the wavelength of the bubble's upward trajectory elongates while its average velocity initially decreases and subsequently increases [15]. For conditions characterized by high Reynolds and Weber numbers, the deformation of the bubble becomes more pronounced, with an emphasis on the interplay between energy components within the bubble that significantly influences its motion. Notably, during collisions between the bubble and a solid wall, an increase in surface energy compensates for kinetic energy losses, thereby aiding in maintaining a relatively constant bounce amplitude [1]. In the vertical near-wall shear flow, bubbles may also experience multiple collisions with the wall. This phenomenon can be attributed to the additional energy generated by the oscillation of the bubble shape, which is enough to counteract the damping effect of the bubble colliding with the wall. When the bubble size is small, in addition to collisions, sliding of the bubble along the wall will also be observed [16]. Despite significant advancements in the study of near-wall bubbles, certain knowledge gaps still exist. Presently, the majority of research efforts have primarily focused on investigating the behavior of individual bubbles, whereas in practical engineering applications, bubbles often manifest as chains or clusters. Consequently, findings from single bubble studies may not be directly applicable to complex scenarios involving multiple bubbles. Furthermore, existing research has been insufficient in capturing the statistical characteristics and long-term evolutionary trends associated with bubble behavior. To overcome these limitations, this study employed a dual-camera orthogonal shadow method combined with feature matching technology to accurately track near-wall bubble chains. Expanding on the dynamic characteristics of individual bubbles near walls, this study further analyzed the statistical properties and movement patterns of continuous bubbles, extending the scope of investigation from single bubbles to multi-bubble scenarios. Moreover, discussions were conducted on the dynamic characteristics of bubble chains under various operating conditions, aiming to provide more extensive theoretical support and practical guidance for engineering applications.
## 3 Results and Discussion
### Damage Evolution
#### 3.2.2 Energy Evolution Process

Table 3: Fitting parameters for the relationship between \(u\) and stress level of different specimens. Figure 12: The relationship between \(u\) and stress level for specimens at (**a**) cyclic stress step of 20 MPa; (**b**) \(\beta=0^{\circ}\); (**c**) \(\beta=30^{\circ}\); and (**d**) \(\beta=90^{\circ}\). Figure 14 displays the relationship between \(u_{d}\) and stress level for specimens at different cyclic stress steps and loading-foliation angles. The results show that \(u_{d}\) increases slowly in the first few cycles and surges in the later cycles. To further explore the evolution law of energy dissipation, \(u_{d}/u\) in each cycle is analyzed as shown in Figure 15. Regardless of the loading-foliation angle, \(u_{d}/u\) presents a U-shaped variation trend with the increase in stress level. Meanwhile, at different stress levels, as the loading-foliation angle increases, \(u_{d}/u\) generally increases first and then levels off or reduces slightly, with \(u_{d}/u\) reaching the minimum and maximum values at \(\beta\) = 0\({}^{\circ}\) and \(\beta\) = 45\({}^{\circ}\), respectively. For an individual specimen, the dissipated energy in the first cycle is always the largest, primarily due to the fact that the rock specimen needs to absorb a large amount of energy to compact the original microdefects when it is loaded for the first time, and this part of the energy is mainly embodied in the form of dissipated energy. Accordingly, it can be concluded that if the damage degree of the specimen is relatively low, increasing the maximum stress level or increasing the number of cycles will reduce the percentage of dissipated energy of the specimen under cyclic loading. However, once the accumulated damage in the specimen reaches a certain degree, increasing the stress level or the number of cycles will increase the percentage of dissipated energy, with most energy used to drive the crack propagation and coalescence. Figure 14: _Cont._  
Figure 13: The relationship between \(u_{e}\) and stress level for specimens at (a) cyclic stress step of 20 MPa; (b) \(\beta\) = 0\({}^{\circ}\); (c) \(\beta\) = 30\({}^{\circ}\); and (d) \(\beta\) = 90\({}^{\circ}\).
## 7 Regularization
### \(\ell_{1}\) Regularization (Lasso)  
The cost function with \(\ell_{1}\) regularization is  
\[\operatorname{err}(W)=\sum_{i}l_{i}(W)+\kappa\,\|W\|_{1}, \tag{110}\]  
where we chose \(\kappa\geqslant 0\) to denote the regularization hyperparameter. As a side remark, please note that mixtures of \(\ell_{2}\) and \(\ell_{1}\) regularizations are also possible, implying the following cost: \(\sum_{i}l_{i}(W)+1/2\lambda\,\|W\|_{2}^{2}+\kappa\,\|W\|_{1}\)[62; 63]. The meaning of \(\ell_{1}\) is that, again, weights areconstrained to stay within a certain ball, but this time a diamond-shaped ball implied by the 1-norm, i.e., the sum of absolute values of weights. This \"encourages\" many weights not only to be small but to be exactly zero (or at least very close to zero in the numerical sense, e.g., less than \(10^{-5}\)). That is why \(\ell_{1}\) is often regarded as a feature selection mechanism [12] and known by the name _lasso_--least absolute shrinkage and selection operator [64].  
Commonly, in practice, derivatives of (110) take the form  
\[\frac{\partial\operatorname{err}(W)}{\partial w_{k}}=\sum_{i}\frac{\partial l _{i}(W)}{\partial w_{k}}+\kappa\ \operatorname{sgn}(w_{k}), \tag{111}\]  
in spite of \(|w_{k}|\) not being differentiable at zero [56; 63; 65].
## 2 Materials and Methods
### Data
#### 2.1.2 Remote Sensing Data  
In this study, the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the leaf area index (LAI) from Moderate Resolution Imaging Spectro-radiometer (MODIS) were used [40]. These data provided by MODIS are characterized by long temporal coverage and high spatial resolution [41]. They were downloaded through the AppEEARS tool ([[https://appeears.earthdatacloud.nasa.gov/task/point](https://appeears.earthdatacloud.nasa.gov/task/point)]([https://appeears.earthdatacloud.nasa.gov/task/point](https://appeears.earthdatacloud.nasa.gov/task/point)) (accessed on 17 May 2023)) [42], including MOD13A1, MYD13A1, MCD15A3, and MOD15A2. All of them had the same spatial resolution of 500 m. Among them, MOD13A1 and MYD13A1 provided NDVI and EVI data from 2000 to 2020, with a temporal resolution of 16 days. MCD15A3 and MOD15A2 offered LAI data from 2000 to 2020 with temporal resolutions of 4 days and 8 days, respectively. To achieve an 8-day temporal resolution of the NDVI and EVI time series, MOD13A1 and MYD13A1 were combined temporally. As MCD15A3  
Figure 1: The spatial distribution of the 16 eddy covariance flux sites of cropland used in this study. (**b,c**) are detailed explanations of the two black boxes in (**a**) above. The base map is the world map from the Köppen–Geiger Climate Classification (www.glon2o.org/kopper (accessed on 10 May 2024)).  
did not produce data in 2000 and 2001, MOD15A2 was used to fill in this gap. Then, linear interpolation was employed to convert the data into daily time series, and the daily time series of NDVI, EVI, and LAI were finally resampled at a half-hourly scale in order to match the temporal resolution of the eddy covariance data [28].
## 4 Experiments and Analysis
### Accuracy Analysis
In order to show the accuracy of the reconstructed 5.00 mm plane in detail, the corresponding cutaway view at 150 column from 1 to 300 rows in Figure 12a is shown in Figure 12b, it can be seen that the height of the reconstructed 5.00 mm plane is between 4.925 mm and 5.077 mm which is consistent with the measuring result of 5.00 mm plane in Table 1: it effectively verified that the proposed method has higher measuring accuracy.
# Preamble Design and Collision Resolution in a Massive Access IoT System
## 3 Preamble Design
### Sub-Preamble Sequence Design
(2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Coexistence of Human-Type and Machine-Type Communications in Uplink Massive MIMO. _IEEE J. Sel. Areas Commun._**2020**. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatial Group Based Random Access for M2M Communications. _IEEE Commun. Lett._**2014**, _18_, 961-964. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] An Early Preamble Collision Detection Scheme Based on Tagged Preambles for Cellular M2M Random Access. _IEEE Trans. Veh. Technol._**2017**, _66_, 5974-5984. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Preamble Collision Resolution Scheme via Tagged Preambles for Cellular IoT/M2M Communications. _IEEE Trans. Veh. Technol._**2018**, _67_, 1825-1829. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] MAP-Based Pilot State Detection in Grant-Free Random Access for mMTC. In Proceedings of the IEEE 21 st International Workshop on Signal Processing Advances in Wireless Communication, Atlanta, GA, USA, 26-29 May 2020. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Novel Cross-Layer Protocol for Random Access in Massive Machine-Type Communications. In Proceedings of the IEEE International Conference on Communications Workshops, Kansas City, MO, USA, 20-24 May 2018; pp. 1-6. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multiple Delay Estimation for Collision Resolution in Non-Orthogonal Random Access. _IEEE Trans. Veh. Technol._**2020**, _69_, 497-508. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Random-Access Control Method for MTC in an LTE System Inspired by the Minority Game. _IEEE Trans. Veh. Technol._**2018**, _67_, 9037-9041. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Efficient Random-Access Scheme for Massive Connectivity in 3 GPP Low-Cost Machine-Type Communications. _IEEE Trans. Veh. Technol._**2017**, _66_, 6280-6290. [CrossRef]
* (21) 3 GPP TS 36.300, Release 16 10.1.5 Random Access Procedure. December 2016. Available online: [https://www.3 gpp.org/ftp/Spe](https://www.3 gpp.org/ftp/Spe) cs/archive/38_series/36.300 (accessed on 1 January 2021). * (22) 3 GPP TS 38.321, Release 16 5.1 Random Access Procedure. October 2020. Available online: [https://www.3 gpp.org/ftp/Specs/archive/38_series/38.321](https://www.3 gpp.org/ftp/Specs/archive/38_series/38.321) (accessed on 1 January 2021). * (23) 3 GPP TS 38.211, Release 16 6.3.3 Physical Random-Access Channel. October 2020. Available online: [https://www.3 gpp.org/ftp](https://www.3 gpp.org/ftp) /Specs/archive/38_series/38.211 (accessed on 1 January 2021). * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Enhanced Access Reservation Protocol with a Partial Preamble Transmission Mechanism in NB-IoT Systems. _IEEE Commun. Lett._**2017**, _21_, 2270-2273. [CrossRef]
## Abbreviations  
Below is a list of abbreviations used throughout this paper, along with their corresponding definitions.  
\begin{tabular}{l l} BEC & Binary Erasure Channels \\ BER & Bit Error Rate \\ DNC & Direct Network Coding \\ FEC & Forward Error Correction \\ FL & Federated Learning \\ GA & Gaussian Approximation \\ GMAC & Gaussian Multiple-Access Channels \\ IM/DD & Intensity Modulation Direct Detection \\ LEDs & Light-Emitting Diodes \\ LiSAC & Lighting, Sensing, and Communication \\ LLR & Log-Likelihood Ratio \\ LOS & Line of Sight \\ NLOS & Non-Line of Sight \\ NOMA & Non-Orthogonal Multiple Access \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OOK & On-Off Keying \\ PNC & Physical-Layer Network Coding \\ RF & Radio Frequency \\ SNR & Signal-to-Noise Ratio \\ VLC & Visible Light Communication \\ VLP & Visible Light Positioning \\ \end{tabular}