esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 1 Introduction  
The future technique of 5G-based cognitive radio network (CRN) has high throughput and low latency with a wide range of connectivity. This target was achieved by the non-orthogonal multiple-access (NOMA) technique to reuse the resources in spatial [1] and temporal varieties. A NOMA uses the distinct channel gains within the same cell and assigns the same frequency to a number of users. A NOMA is combined with a multiple-input multiple-output (MIMO) system [2] to obtain a high diversity and a high spectral efficiency in a multi-cell substructure. A serious interference will be caused due to the high spectral efficiency in NOMA network. Thus, security is needed for the design of communication systems in the MIMO-NOMA framework [1, 2]. The transmitting and receiving beamforming techniques were used in [3] to reduce the inter- and intra-cell interferences. The eavesdroppers may secretly listen to the channel by simply ignoring the receiving beamformer technique. The signal alignment and the receiving beamformer methods were used in [4, 5] to control the interference and raise the total throughput. The greedy algorithm and a convex approximation method were used in [6] to solve the scheduling problem. A two-stage transmission [7] scheme was used in the cooperative NOMA system model. During the first stage, the source transmits the code symbol to the relay and the destination. At the last stage, the relay decodes and forwards a new code symbol to the destination with the corresponding power allocation factor. The artificial noise method was used in [8] to secure the transmission in the MIMO-NOMA system model. The convex optimization problem was solved in [9] for the better MIMO-NOMA network. In [10], an investigation was basedon the joint subcarrier (SC) assignment and the power allocation problem in NOMA features in the presence of eavesdroppers. The cooperative jamming (CJ) technique and the smart sensor protocol algorithm (SSPA) were implemented in the security of the better communication link. Then, the overall energy efficiency was increased. The relay-based downlink NOMA network contains the physical layer security (PLS) and was considered in [11]. The receiver beamforming technique used in [3; 4; 5; 8; 9] does not provide a beneficial assurance for the system security in a multi-cell network.  
In this article, the proposed beamforming technique was compared with the existing zero forcing technique, which was used in [12]. The simulation results provide better evidence for the enhancement of the proposed method.
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
## 2 Materials and Methods  
### Materials  
M3-size single crystalline silicon (Si) based PERC-type PV cells with 21.2% efficiency were received from a company (Shinsung E&G, Seongnam-si, Korea) and used without any additional treatment. Lead-tin (Sn60 Pb40, KOSWIRE, Flowery Branch, GA, USA) wires with 0.26 mm diameter were used for string fabrication of MBB modules. Flux (920 CXF, KESTER) was used for removing the oxide layer on the wires and PV cells. Electrically conductive adhesive (ECA) (DS-0860A, DAEJOO, Korea) was used. Glasses (Low Iron Float Glass, Nuri-Corporation, Seongnam-si, Korea) with 3.2 T were purchased for the encapsulation process. Ethylene-vinyl acetate (EVA) films (HEP-02T, HANWHA, Seoul, Korea) with a transmittance of 91.08% were used. The white backsheets and black backsheets were purchased from the SFC Corporation (Seoul, Korea). Home-made equipment was exploited for the shingled and MBB PV modularization process.
## Materials and Methods
### Chemical Characterization by High Performance Liquid Chromatography (HPLC)  
Chemical characterization of the obtained extracts and quantification of the selected compounds was carried out by HPLC method [19], using Agilent HP 1100 HPLC-diode array detection (DAD) system equipped with an autosampler. The components were separated using reversed-phase Zorbax CB-C18 column (\(4.6\times 150\) mm, \(5\upmu\)m particle size) held at 25 \({}^{\circ}\)C. Solvent A was 0.1% aqueous acetic acid while solvent B was 0.1% acetic acid in acetonitrile. The mobile phase used was delivered in gradient mode (3.25 min 10% B; 8 min 12% B; 15 min 7 133 25% B; 15.8 min 30% B; 25 min 90% B; 25.4 min 100% B), with flow rate of 1 mL/min. UV detection was set at 280 nm. The HPLC mobile phase was prepared fresh daily and filtered through a 0.45 \(\upmu\)m nylon filter. The injection volume was 10 \(\upmu\)L Identification and quantification of gallic, chlorogenic, \(p\)-hydroxybenzoic, caffeic, syringic, \(p\)-coumaric and trans-cinnamic acid, epicatechin, rutin, quercetin and naringenin was performed. Standards of the selected compounds were dissolved in methanol and injected into the system under the same conditions in order to obtain calibration curves.  
Salicin was determined by a slightly modified HPLC method described earlier [20], using Zorbax CB-C18 column (\(4.6\times 150\) mm, \(5\upmu\)m particle size) and mobile phase consisting of bidistilled water, tetrahydrofuran and ortho-phosphoric acid (97.7:1.8:0.5) (v/v/v) delivered in isocratic mode.
## 3 Methodology
### Data Complexity Measurement
The K-means algorithm is adopted to cluster the samples according to the Euclidean distance from the sample point to the cluster center in the feature space and obtain three clusters. Then, the intra-class local density of the samples is sorted into the three clusters, taking the smallest intra-class local density value in each cluster and sorting in descending order to obtain \(p_{i,min_{1}}\), \(p_{i,min_{2}}\), and \(p_{i,min_{3}}\), defining the \(j\)th sample \(j\in\{1,\ 2,\cdots,m_{i}\}\), which satisfies \(p_{i,\ j}\geq p_{i,min_{1}}\) as the easy-to-classify sample subset, \(T_{i,\ 1}\); the \(j\)th sample, which satisfies \(p_{i,min_{2}\leq p_{i,\ j}<p_{i,min_{1}}\), is the relatively easy-to-classify sample subset, \(T_{i,\ 2}\); and the \(j\)th sample, which satisfies \(p_{i,min_{3}\leq p_{i,\ j}<p_{i,min_{2}}\), is the hard-to-classify sample subset, \(T_{i,\ 3}\).
## 2 MSFGWO Algorithm Model
### Steps of MSFGWO Algorithm  
The proposed MSFGWO algorithm incorporates the perturbation mechanism of the VNS algorithm and the mutation strategy of the GA to enhance the overall optimization capability of the GWO algorithm. Additionally, the EWM method is employed to minimize the satellite handover frequency between consecutive time intervals, thereby extending the continuous observation duration. This results in improved stability and accuracy of the obtained positioning solutions.  
Step 1: Filter all available satellites with elevation angles greater than 10\({}^{\circ}\) and assign them numbers. Set the number of selected satellites to n, based on the receiver's channel count.  
Step 2: Set the initial population size and iteration times for the MSFGWO algorithm.  
Step 3: Calculate the DGDOP values of various satellite combinations in the initial population, and select the initial \(\alpha\), \(\beta\), and \(\delta\) wolves based on their fitness values.  
Step 4: Apply the shaking procedure from the VNS method to each satellite combination in the initialized population.  
Step 5: Perform the GA mutation strategy on each combination of satellites obtained in Step 3.  
Step 6: Use the results obtained through these operations as new populations and calculate DGDOP values for each satellite selection scheme while updating the wolves in the population.  
Figure 3: Mutation operation in GA.  
Step 7: Determine whether the maximum number of iterations has been reached or not; if not, repeat Steps 3-5; if yes, proceed to Step 7.  
Step 8: Evaluate and select the final selection scheme based on the EWM, combining previous and current selection results, DGDOP values, and the visibility time of selected satellites.  
Accordingly, the flowchart of the MSFGWO algorithm is shown in Figure 4.
## 2 Materials and Methods
### Independent Dataset  
We further tested the proposed model using the independent dataset proposed in [15]. This dataset contained 100 strong enhancers, 100 weak enhancers, and 200 non-enhancers. Table 7 shows the results of the comparison of the proposed model with other state-of-the-art models in the first stage, whereas Table 8 shows results of the comparison of the proposed model with other state-of-the-art models in the second stage. The achieved results show that the proposed model in both stages performed better in terms of sensitivity. This illustrates the robustness of the model by correctly predicting the true enhancer sites and their strength. In general, the combination of the results of the proposed model and iEnhancer-CNN provides more confidence at prediction time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **Accuracy** & **MCC** \\ \hline iEnhancer-EL & 69.00 & 61.05 & 65.03 & 0.31 \\ iEnhancer-2L & 62.21 & 61.82 & 61.93 & 0.24 \\ EnhancerPred & 62.67 & 61.46 & 62.06 & 0.24 \\ iEnhancer-XG & 74.94 & 58.55 & 66.74 & 0.33 \\ iEnhancer-SStep & 75.53 & 60.80 & 68.10 & 0.37 \\ iEnhancer-DSNet & 63.00 & 67.00 & 63.00 & 0.26 \\ tan2019 emsemble & 79.65 & 38.28 & 58.96 & 0.19 \\ iEnhancer-CNN & 73.64 & 76.8 & 76.43 & 0.45 \\ iEnhancer-Deep & 83.57 & 78.16 & 80.86 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison between the state-of-the-art predictors and the proposed model in the second stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **Accuracy** & **MCC** \\ \hline iEnhancer-EL & 75.67 & 80.39 & 78.03 & 0.56 \\ iEnhacer-2L & 78.09 & 75.88 & 76.89 & 0.54 \\ EnhancerPred & 72.57 & 73.79 & 73.18 & 0.43 \\ iEnhancer-XG & 75.70 & 86.50 & 58.55 & 0.62 \\ iEnhancer-5 Step & 81.10 & 83.50 & 82.30 & 0.65 \\ iEnhancer-DSNet & 76.00 & 76.00 & 76.00 & 0.52 \\ tan2019 emsemble & 73.25 & 76.42 & 74.83 &.49 \\ iEnhancer-CNN & 75.88 & 88.88 & 80.63 & 0.69 \\ iEnhancer-Deep & 86.99 & 88.5 & 87.77 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between the state-of-the-art predictors and the proposed model in the first stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sensitivity** & **Specificity** & **Accuracy** & **MCC** \\ \hline iEnhancer-EL & 71.00 & 78.50 & 74.75 & 0.4969 \\ iEnhacer-2L & 71.00 & 75.00 & 73.00 & 0.4604 \\ EnhancerPred & 73.50 & 74.50 & 74.00 & 0.4800 \\ iEnhancer-CNN & 78.25 & 79.00 & 77.50 & 0.5850 \\ iEnhancer-Deep & 81.5 & 67.00 & 74.02 & 0.4902 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison in the first stage between the state-of-the-art predictors and the proposed model in the independent test.  
Figure 5: Graphical representation of the comparison in the first stage.  
Figure 6: Graphical representation of the comparison in the second stage.
# Soliton Content of Fiber-Optic Light Pulses
## 2 The Nonlinear Schrodinger Equation and Some of Its Solutions
### Soliton Solutions  
#### 2.1.1 The Fundamental Soliton  
In the anomalous dispersion regime, the NLSE Equation (1) supports a stable solution known as the fundamental soliton:  
\[A(z,t) = \sqrt{P_{0}}\text{ sech}\left(\frac{(t-t_{\text{s}})-\Omega\beta_{2 }(z-z_{\text{s}})}{T_{0}}\right) \tag{2}\] \[\times \exp\left[i\frac{\gamma P_{0}}{2}(z-z_{\text{s}})-i\Omega\left((t -t_{\text{s}})-\frac{1}{2}\Omega\beta_{2}(z-z_{\text{s}})\right)+i\varphi_{ \text{s}}\right]\quad.\]  
Here, \(P_{0}\) is the peak power; \(T_{0}\) is the pulse duration; \(\Omega\) is the deviation of the soliton's center optical frequency from that of the frame of reference, \(\omega_{0}\). \(t_{\text{s}}\), \(z_{\text{s}}\) and \(\varphi_{\text{s}}\) are the initial values of center time, start position and phase offset, respectively. As long as a single soliton is considered, frequency offset and initial values may be set to zero without loss of generality so that a considerably simplified version:  
\[A(z,t)=\sqrt{P_{0}}\text{ sech}\left(\frac{t}{T_{0}}\right)\text{ exp}\left[i\frac{\gamma P_{0}}{2}z\right] \tag{3}\]  
remains. In either case, amplitude and duration are coupled and must fulfil:  
\[P_{0}T_{0}^{2}=\frac{|\beta_{2}|}{\gamma}\quad. \tag{4}\]  
The propagation of a fundamental soliton with \(\Omega=t_{\text{s}}=z_{\text{s}}=\varphi_{\text{s}}=0\) is shown in Figure 1. As the temporal evolution (sech term) does not contain \(z\), the temporal shape does not change during propagation. Without nonlinearity, the shape would change appreciably after one dispersion length \(L_{\text{D}}=T_{0}^{2}/|\beta_{2}|\). The other relevant length scale is the nonlinearity length \(L_{\text{NL}}=1/(\gamma P_{0})\) after which self-phase modulation becomes appreciable. As Equation (4) shows, for the soliton \(L_{\text{D}}=L_{\text{NL}}\). This equality expresses the balance between both.  
Among the conserved quantities of the integrable equation are the soliton energy:  
\[E_{\text{sol}}=2P_{0}T_{0} \tag{5}\]  
and the soliton center frequency \(\Omega\). As will be described below (Section 4.1), these two values are found from inverse scattering in the form of imaginary and real parts of a complex eigenvalue. We point out that in a dispersive medium, a nonzero frequency translates to a relative motion, so that the frequency is usually referred to as the velocity. The number of solitons is also preserved, even in cases when there is more than one.  
Figure 1: Evolution of a fundamental nonlinear Schrödinger equation (NLSE) soliton.
# Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
## 3 Results and Discussions
### FTIR Analysis  
Figure 4 shows the FTIR spectrum of VG grown on the silicon/silicon oxide substrate (violet spectrum). In the spectrum of the vertical graphene obtained after a PECVD process, no peaks can be observed that can be attributed to some functional groups. The peaks observed in the sample spectrum can be attributed to CO\({}_{2}\) molecules (2400-2200 cm\({}^{-1}\)) and O-H bonds from the water molecules absorbed by the carbonaceous material (4000-300 and 1900-1200 cm\({}^{-1}\)). The peaks at around 620 cm\({}^{-1}\) can be attributed to the vibration mode of the Si-Si bonds in the wafer on which the VG was grown. Also, a film of SiO\({}_{2}\) is observed due to the appearance of peaks of reduced intensity in the 1200-1000 cm\({}^{-1}\) region.  
Figure 3: AFM micrograph of VG.  
Also, Figure 4 compares the spectra of the VG grown on the working electrode before (red spectrum) and after the electrochemical processes (green spectrum). A similarity between the two spectra can be observed, confirming the maintenance of the structural properties of the VG regarding electrochemical processes. No contamination of the film is observed after the stages of the EIS steps. As can be seen from the VG-blank spectrum, graphene does not show IR (infrared) bands, so it can be said that the spectra of the two samples are characterized by absorption bands that can be attributed to the substrate. In the 1200-400 cm\({}^{-1}\) range, peaks can be observed that can be attributed to the Si-O bonds in the SiO\({}_{2}\) film and Si-Si in the silicon wafer. The peaks in the 2200-2000 cm\({}^{-1}\) region can be assigned to SiHx bonds. Also, the FTIR image has a spectrum for the Si/SiO\({}_{2}\) substrate (blue spectrum).  
The FTIR analysis confirmed that VG is not affected by the electrochemical processes involved in the EIS method. Both spectra of VG before and after the EIS measurements are almost identical, with the same peaks. This analysis concludes that VG maintained its properties after the EIS characterization.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 1 Introduction
### Notation and Organization  
Vectors are represented using boldface lower-case letters. The conjugate transpose is represented by \((.)^{H}\), and \(|.|\) denotes the absolute value. A probability density function (PDF) for random variable (RV)\(x\) is represented by \(f_{X}(x)\) and the corresponding cumulative distribution (CDF) is \(F_{X}(x)\). Probability of a given event \(\theta\) is denoted by \(\mathrm{P}(\theta)\). The non-central chi square distribution with non-central parameter \(\delta\) and degree of freedom (DoF) \(n\) is denoted by \(\chi^{2}(x;n;\delta)\). The subscripts 0 and \(\tau\) denote the variables at measurement phase and at a given transmission realization after \(t=\tau\) ms from measurement, respectively.  
The rest of the paper is organized as follows: Section 2 discusses the system model. Section 3 introduces the sources of SINR variability and the related statistical distributions. User mobility and changes in other-cell precoding are discussed. Backoff selection for robust link adaptation is considered. Finally, conclusions are drawn in Section 4.
## 4 Data Cleaning and Synchronization
### Audio Synchronization  
To synchronize audio recordings from the action camera with those from a Tascam-DR-07X audio recorder, we start by standardizing the sample rates of both sources, ensuring uniformity for accurate processing. The heart of this synchronization process is the use of cross-correlation, a statistical technique that measures the similarity between two audio waveforms. This involves determining the time lag, represented as \(\tau\), that maximizes the similarity between the two signals. The cross-correlation function, symbolized as \(R(\tau)\) and defined in Equation (1), is instrumental in identifying the point of highest correlation between the two audio streams. This peak correlation signifies the optimal alignment point, indicating the exact time shift needed to achieve synchronization between the recordings. This method provides a precise mechanism for aligning audio files from different sources, ensuring their temporal coherence for further analysis or integration.  
\[R(\tau)=\sum_{n}x[n]\cdot y[n+\tau] \tag{1}\]  
To achieve alignment, one of the audio signals needs to be adjusted in time by inserting a silent interval that matches the absolute value of the identified lag. This adjustment ensures that the inherent qualities of the audio files remain intact, even after incorporating the necessary time shift. By adding this silent period, we align the start times of the audio tracks from the action camera and the Tascam recorder, ensuring they begin simultaneously and are precisely synchronized. Figure 7 illustrates a segment where the two audio files have been successfully synchronized, demonstrating the effectiveness of this adjustment in achieving temporal coherence between different audio sources.  
Figure 6: GUI interactive interface for comparative image selection: 4K dataset vs. stereocamera dataset.
## 3 Simulation Experimental Design
### Vehicle Operating Rules
#### 3.1.1 Car-Following Rules
Thus, \(b_{kin}\) increases in the course of time and approaches the comfortable deceleration. The simulation of CAVs in this paper employs the car-following model based on cooperative adaptive cruise control (CACC). CACC is a longitudinal vehicle control model that utilizes a constant time gap strategy, proposed by the PATH lab at UC Berkeley, and relies on vehicle-to-vehicle wireless communication technology. In adaptive cruise control (ACC), the following vehicle adjusts its own speed based on the driving data obtained from the lead vehicle through sensors such as radar or infrared. This allows for a smaller inter-vehicle gap while maintaining safety. However, ACC can only obtain information from the lead vehicle. In contrast, CACC enables vehicles equipped with the CACC system to gather information about surrounding vehicles, thereby perceiving the driving environment more comprehensively and rapidly. CACC is widely used in research on CAVs. Its basic principles can be described as follows [41]:  
\[e=x_{i-1}-x_{i}-Tv_{i} \tag{11}\]  
\[v_{i}=v_{kpurev}+k_{p}e+k_{d}\dot{e} \tag{12}\]  
where \(x_{i}\) is the displacement of ego vehicle, \(v_{i}\) is the velocity of ego vehicle, \(e\) is the difference between actual distance and desired distance, \(T\) is the minimum safe headway time gap, \(i-1\) represents the front vehicle, and \(v\) is the velocity of the vehicle at the previous moment. The simulation is carried out based on the SUMO platform, which developed the CACC simulation model based on the research of CACC [41; 42; 43; 44]. The CACC model in SUMO has four control modes as follows. 1. Speed control mode, which is designed to maintain the pre-defined by driver's desired speed and is activated when there are no preceding vehicles in the range covered by the sensors or when the time gap is larger than 2 s;
2. Gap control mode, which aims to maintain a constant time gap between the CACC-equipped vehicle and its predecessor, is activated when the gap and speed deviations (concerning the preceding vehicle) are concurrently smaller than 0.2 m and 0.1 m/s, respectively;
3. In gap-closing control mode, the gap-closing controller enables a smooth transition from speed control mode to gap control mode and is triggered when the time gap is less than 1.5 s;
4. Collision avoidance mode prevents near-end collisions when safety-critical conditions prevail. This mode is activated when the time gap is less than 1.5 s, and the gap deviation is negative. Additionally, when there is no leader vehicle in the fleet, the CACC vehicle degrades. Its speed control strategy will be calculated through the Krauss model, another car-following model for human-driven vehicles.