esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 7 Conclusions  
In this paper, we have analyzed the SC-FDM modulation technique in order to provide an efficient communication link between the vehicle and GCS, since the communication link is an essential part of the UAV. The main purpose of this work was to analyze the SC-FDM modulation technique by comparing it with the OFDM. In general, this paper is divided into two parts. In the first part, a UAV communication link, UAV communication system components, as well as some related works have been discussed. On the other hand, the second part provided a brief overview of SC-FDM and OFDM modulation techniques and discussed the simulation results obtained by using various types of modulation schemes such as BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM. The comparative results show that SC-FDM is more effective than OFDM for the UAV communication system, leading to a noticeable improvement in terms of efficiency of the UAV communication link and the flight time of quadcopters. Moreover, by analyzing the results of our work, we have found that BPSK and QPSK are optimal modulation schemes for the SC-FDM modulation technique.  
Due to the fact that the currently used 2.4 GHz band for UAV communication link is utilized by many different communication systems leading to the generation of interferences, some drones have already been designed to operate in the 5.8 GHz band. Thus, our further research will focus on analyzing the 5.8 GHz band for a UAV communication system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Subcarriers**} & \multicolumn{2}{c}{**Computation Time (ms)**} \\ \cline{2-3}  & **SC-FDM** & **OFDM** \\ \hline
256 & 222 & 350 \\
512 & 542 & 829 \\
1024 & 1278 & 1916 \\
2048 & 2941 & 4348 \\
4096 & 6653 & 9723 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation time for SC-FDM and OFDM.  
Figure 13: Computation time comparison of SC-FDM and OFDM.  
**Author Contributions:** Conceptualization, S.A. and S.-H.L.; Software, S.A.; Formal Analysis, S.-H.L. and O.-H.K.; Writing-Review & Editing, S.A. and O.-H.K.; Project Administration and Funding Acquisition, K.-R.K.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2018-2016-0-00318) supervised by the IITP (Institute for Information & communications Technology Promotion), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2016R1D1A3B03931003, No. 2017R1A2B2012456) and the Korea Technology and Information Promotion Agency for SMEs (TIPA) grant funded by the Korea government (Ministry of SMEs and Startups) (No. C0407372).  
**Acknowledgments:** The authors would like to thank the editors and the reviewers for their valuable time and constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Investigation of the Nonlinear Composite Action of FRP-Concrete Hybrid Beams/Decks
## 1 Introduction  
High strength-to-weight ratio and durability under corrosive chemical conditions have made fiber-reinforced polymer (FRP) composite materials an attractive solution to bridge members [1; 2; 3]. In the last two decades, there has been an increasing interest in using FRP composites to repair andstrengthen reinforced concrete (RC) and steel structures [4; 5]. In the new construction of bridge superstructures, hybrid FRP-concrete beams and decks provide benefits by combining the stiffness and low cost of concrete with the light-weight, fabrication speed, high strength, and durability of FRP [6]. Among them, pultruded FRP-concrete hybrid beams/decks have been proposed as an advanced [7; 8; 9] solution for bridge superstructures. After taking into account the expected improvements in transport, installation, and life-cycle differences, the initial cost of FRP can be offset, thereby leading the system competitive in the standard short-span bridge market and bridge decks [10; 11; 12]. The primary difference between these FRP-concrete hybrid beams/decks and the currently widely used traditional steel-concrete composite beams is that the FRP cannot be welded easily like steel, so the interfacial connections are usually weaker in terms of capacity and slip modulus [13; 14; 15; 16; 17; 18]. The static [19; 20; 21], fatigue [22], aggressive exposure [22], dynamic [23], and long-term [24] performances have been investigated in previous research. Hence the interfacial slip between FRP and concrete is expected to be high and reduces the stiffness and capacity and fiber-reinforced polymer-concrete hybrid beam (FCHB) [19; 20; 25; 26]. Strong interfacial connections are recommended to be used in the real applications of FCHB bridges [27; 28; 29; 30]. However, theoretically speaking, there should be a slip between FRP concrete even when strong connectors (e.g., perforated FRP ribs [10; 13] and FRP shear keys [14; 18]) are used. The evaluation of interfacial slip is an inevitable topic in the research and design of FCHB. Many researchers have proposed analytical solutions to quantifying the interfacial slip of FCHB according to a linear load-slip relationship of shear connection [31; 32]. A slip modulus was employed based on a chord slope of FRP-concrete push-out testing [10; 14; 16]. However, this simplification is not true for nearly all the connectors because nonlinearity occurs even at low load level which makes the chord slope different from the tangential slope [10; 14; 15; 16; 31; 32; 33]. Therefore, a method that can consider the nonlinear interfacial load-slip relationship is needed.  
This study reports a simple but fairly accurate solution for the nonlinear slip process using the finite difference method (FDM) in association with linear and nonlinear bond-slip models. Based on the computation results, existing analytical solutions using linear interfacial load-slip relationships were calibrated, and different bond-slip models are discussed.
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 5 Proposed Algorithm
### Frequency Domain Packet Scheduling (FDPS)
Then, the metric function is computed for all requesting devices using (27). The scheduler selects \(N\_sel\) devices with the highest metrics. Then, in FDPS, the RBs allocated to each IoT device are chosen with the objective to maximize the weighted preference matrix in (33).
## 2 Results and Discussion
### Antioxidant Activity
#### 2.3.4 Discussion on Structure-Activity Relationship of APs
Hydrophobic amino acids in APs have their significant radical scavenging ability because their non-polar aliphatic groups have been shown to have strong reaction activity to hydrophobic PUFAs [1; 2; 3]. Aromatic amino acids (Phe and Tyr residues) can maintain ROS at a stable state by donating protons to free radicals that are deficient in electrons [6; 32]. Pyrrolidine ring of Pro residue can strengthen the backbone flexibility of bioactive peptides and remove singlet oxygen because of its property of low ionization potential [5]. [PERSON] et al. [1] proved that Tyr residues could interdict the chain reaction of peroxidation mediated by free radicals because it could shift and change free radicals into steady phenoxy radicals. As a consequence, the hydrophobic and aromatic amino acid residues in the sequences of F7-P3 (Pro and Phe residues), F7-P5 (Pro, Tyr, and Lys residues), and F7-P8 (Tyr, Pro, and Met residues) should have an active influence on their activities of lipid peroxidation inhibitory and radical scavenging. Hydroxylproline (Hyp) is an important component in collagens, but no Hyp was found in the amino acid sequences of eight collagen peptides (F7-P1 to F7-P8). The reason for this phenomenon needs careful study in the future. Therefore, the composition of amino acids and molecular size of eight collagen peptides (F7-P1 to F7-P8) plays a key role in their antioxidant activities. Small molecular size and hydrophobic amino acids help them access free radicals in an oxidant/antioxidant system with increased ease and efficiency. Moreover, antioxidant amino acids further change free radicals into more stable structures or inhibit the propagation of the peroxididizing chain reaction mediated by free radicals.
## 3 The Proposed Method
### Surrogate Model Assisted Predictor  
Because of the high computational cost to solve the cable forces optimization problem, it is appropriate to introduce the surrogate model method to predict the cable forces of the desired completion state. As shown in Figure 4, the following steps are developed in this work to train and select a proper surrogate model.  
1. **Sampling**: Generate training data (or samples) from the design of experiment (DOE) [24]. In this work, the samples are collected by means of a classic full factorial experiment. As for the cable-force predictor, the input data are the identified design variables, and the output data are the optimum cable forces taking the form of controlling points mentioned in Section 3.1. Considering that the cable forces optimization problem is well solved with the minimum bending energy method and the proper algorithm, there should be no anomalous sample.
2. **Training**: Configure the output function based on the machine learning algorithm. The property of the chosen surrogate model can be evaluated with root mean square error (RMSE) and training time. The surrogate model with the least RMSE and an  
Figure 4: Steps to construct a cable-force predictor with surrogate model method.  
acceptable training time is the best solution. In this work, the following models are considered and compared: Polynomial Regression (PR) [25], Gaussian Process Regression (GPR) [26], Regression Tree (RT) [27], and Support Vector Regression (SVR) [28].
3. **Testing**: Create new testing data, conduct the experiment and obtain the actual responses. Expose the new data to the output function and get the predictions. Compare the actual responses and the predictions, then evaluate the accuracy of the surrogate model. If the test fails, it means that the discretization of the variables is either not dense enough or not rational; so, the design of the experiment should be adjusted.  
With the predictor constructed, the optimized cable forces in each iteration round can be predetermined in subsequent Sp2. The introduction of the surrogate model significantly speeds up the iteration process, thus enabling efficient design optimization later.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 3 System Model and the Proposed Algorithm
### Details of the Personalized UAS Method
According to Equation (2), the softmax output value of \(chunk\#1\), which is the probability of the user to select this chunk, is derived as shown in Equation (3):  
\[p_{chunk\#1}=\frac{e^{z}_{chunk\#1}}{e^{z}_{chunk\#1}+e^{z}_{chunk\#2}+...+e^{z}_{chunk\#m}}=\frac{e^{z}_{chunk\#1}}{\sum_{i=1}^{n}e^{z}_{chunk\#i}}, \tag{3}\]  
where  
\[Z=w*x+b. \tag{4}\]  
Based on (3) and (4), and given three chunks, if the viewing pattern of the user of each chunk is (0.8, 0.9, 0.7), and \(Z\) is \(-ln(1/p-1)\), \(Z\) can be derived as:  
\[(Z_{chunk\#1},Z_{chunk\#2},Z_{chunk\#3})=(-\ln(\frac{1}{0.8}-1),-\ln(\frac{ 1}{0.9}-1),-\ln(\frac{1}{0.7}-1))=(1.39,2.2,0.85). \tag{5}\]Applying \(Z\) to softmax, we obtain:  
\[S_{z} = \arg\max\left(\frac{e^{1.39}}{e^{2.2}+e^{1.39}+e^{0.85}},\frac{e^{2.2 }}{e^{2.2}+e^{1.39}+e^{0.85}},\frac{e^{0.85}}{e^{2.2}+e^{1.39}+e^{0.85}}\right) \tag{6}\] \[= \arg\max(0.26,\underline{[0.59]}\ 0.15)=2\rightarrow[\text{Chunk \#2 will be selected}]. \tag{7}\]  
Therefore, the UAS algorithm can set the download priority according to the viewing pattern of the user, so chunk #2 will be downloaded first. The priorities can be different in each of the categories and for each of the users. As a result, even for the same user, the order of sorting result according to the probability of each chunk is derived differently, and depends on the type of video. This priority value changes as the user takes action, and while watching the videos. The pseudo code specified in Algorithm 1 briefly shows the execution process of the proposed personalized UAS method.
## 3 Results and Discussion
### Impedance Spectroscopy Analysis
The total conductivity response (\(\sigma_{total}\)) is 2.31 \(\times\) 10\({}^{-4}\) S-cm\({}^{-1}\), with a bulk conductivity (\(\sigma_{b}\)) of 7.07 \(\times\) 10\({}^{-4}\) S-cm\({}^{-1}\) and a grain boundary conductivity (\(\sigma_{gb}\)) of 3.64 \(\times\) 10\({}^{-4}\) S-cm\({}^{-1}\). Following the relationship (\(\sigma\propto\) R\({}^{-1}\)), the maximum conductivity (\(\sigma_{max}\)) of the material is noticed at the minimum value of the arc response. For the obtained BCTZ \(x\) = 0.15, \(\sigma_{max}\) = 2.48 \(\times\) 10\({}^{-2}\) S-cm\({}^{-1}\) is reached at 580 \({}^{\circ}\)C.
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis
### Against N and M Values
It is interesting to note that, although the handover overhead of both OTFS and OFDM increases with larger \(M\) and \(N\), their ping-pong ratios decrease. This is because the additional overhead in OTFS and OFDM arises from a longer duration per handover, rather than a greater number of handovers, as we previously explained. In fact, for OFDM, the number of handovers declines at higher values of \(M\) and \(N\), attributed to the enhanced averaging process. Consequently, the ping-pong ratio of both OTFS and OFDM diminishes as \(M\) and \(N\) grow, as shown in Figure 17. For instance, when \(M\) and \(N\) increase from 32 to 256, the ping-pong ratio of the RSS scheme grows by a factor of 1.27. In contrast, the ping-pong ratios of OFDM and OTFS are reduced by factors of 16.3462 and 63.9, respectively. These findings affirm the superior performance of the proposed OTFS scheme across all tested values of \(M\) and \(N\).
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 3 Network Model and Mathematical Foundations
### Network Service Request (NSR) Model  
In this analysis, it is assumed that the NSR parameters including the number of nodes in each NSR, CPU capacity, bandwidth, security level, delay, and user device type are assigned. Every NSR is represented by an undirected graph represented as, \(G^{NSR}=(N^{NSR},E^{NSR})\), where \(N^{NSR}\) denoted the nodes of the graph and \(E^{NSR}\) indicates the edges between them. Each node in NSR should be specified as a subset containing \((CPU_{i}^{NSR},LC_{i}^{NSR},SR_{i}^{NSR},LT_{i}^{NSR},DT_{i}^{NSR})\), \(i\in N^{NSR}\) where \(CPU_{i}^{NSR},LC_{i}^{NSR},SR_{i}^{NSR}\), \(LT_{i}^{NSR}\) and \(DT_{i}^{NSR}\) are the required CPU Capacity, Link Capacity, Security Level, Life Time and Device Type of the \(i^{th}\) node, respectively. The data transfer rate between nodes \(i\) and \(j\) is calculated using the formula \(BW_{ij}^{NSR}\). We assume that the NSRs arrive during a single transmission window [33]. Nodes and connections for the NSR are allotted and set up at the beginning of each time period using the physical infrastructure. Every time a new NSR comes in, the available resources in the underlying physical infrastructure is adjusted based on the \(LT^{NSR}\) of the prior request.
## 3 Materials and Methods
### Method Validation
#### 3.4.4 Selectivity and Specificity  
Selectivity was ensured by application of the multiple-reaction monitoring mode in MS acquisition, in addition to the confirmation of retention time. In this mode, a specific ion (precursor) selected in the first-stage quadrupole (Q1) was dissociated in collision cell yielding a selective and specific product ion detected after passing the second stage quadrupole (Q3).  
Specificity, reflecting any potential influence of concomitant sample constituents (e.g., medications) on the determination of target antibiotics, including ion suppression/enhancement and extraction efficiency, was demonstrated by analyzing plasma samples from six ventilated patients without piperacillin or impenem treatment. The acceptance criteria were noninterference at the retention times of analytes.  
To assess the matrix effect, the detector response to the respective concentration of an analyte (QC standards: 0.01, 0.1 and 0.5 mg/L) spiked with a pure desorption solvent was compared to the same analytes' concentrations when spiked to a desorption solvent after previous TFME extraction from blank plasma (derived from patients free of considered antibiotics).
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 4 Comparison of Traditional Sliding Mode Control and Improved Sliding Mode Control  
### Traditional Sliding Mode Control  
For some occasions that require a constant voltage power supply, the system resonance compensation network can meet the demand of the secondary side steady voltage power supply without changing the operating frequency and structure of the system. During the working process of the non-contact excitation motor, it is usually necessary to apply a stable voltage across \(R_{L}\). However, when the rotor is excited by the wireless energy transmission structure, the size of the equivalent load will also change with the change in the speed, so it must be ensured that the output voltage of the non-contact excitation motor is stable. To ensure the stability of the output voltage, a closed-loop control link must be added to the original buck circuit. In the electronic power transformation, the sliding mode control algorithm is often used to stabilize the output voltage, so on this basis, the sliding mode control and buck transformation are used.  
As shown in Figure 5, the full-bridge rectifier circuit is followed by a BUCK circuit, a schematic diagram of a buck converter circuit. First, according to the working principle of the buck converter, a continuous conduction mode is designed in this system. The following state equations are listed according to whether the switch is turned on or off. When the PWM drive circuit drives the switch Q to conduct, that is, the switch state u = 1, the charge is transferred to the inductor L in the course under the action of the DC voltage \(E\), and \(D\) is in the cut-off state under the movement of the reverse voltage. Currently, the differential equation for a buck converter is  
\[\left[\begin{array}{c}\frac{d{{\it i}_{L}}}{dt}\\ \frac{d{{\it p}_{C}}}{dt}\end{array}\right]=\left[\begin{array}{cc}-\frac{1 }{L}&0\\ -\frac{1}{R}&\frac{1}{C}\end{array}\right]\left[\begin{array}{c}v_{c}\\ i_{L}\end{array}\right]+\left[\begin{array}{c}\frac{E}{L}\\ 0\end{array}\right] \tag{9}\]  
When the switching state u = 0, the switch tube is turned off. At this time, the DC power input cannot act on the circuit, diode \(D\) is in the state under the action of forwarding voltage, and the energy absorbed by inductance \(L\) when the switch tube is turned on is transferred to resistance \(R\). At this time, the differential equation of buck converter is  
\[\left[\begin{array}{c}\frac{d{{\it i}_{L}}}{dt}\\ \frac{d{{\it v}_{C}}}{dt}\end{array}\right]=\left[\begin{array}{cc}-\frac{1 }{L}&0\\ -\frac{1}{R}&\frac{1}{C}\end{array}\right]\left[\begin{array}{c}v_{C}\\ i_{L}\end{array}\right] \tag{10}\]  
Figure 5: System topology with sliding mode control.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 3 New Room Acoustic Solver Using Explicit TD-FEM with Modified Adams Method
### Frequency Domain Dispersion Analysis  
We consider the following frequency domain expressions of the semi-discretized matrix equations, Equations (5) and (6), assuming the time factor of \(e^{i\omega t}\).  
\[i\omega\mathbf{D}\mathbf{p}=\mathbf{M}\mathbf{v}, \tag{13}\]  
\[i\omega\mathbf{D}\mathbf{v}=\mathbf{f}-c_{0}^{2}\mathbf{K}\mathbf{p}-i\omega c_{0}\mathbf{C}\mathbf{p}. \tag{14}\]  
Therein, \(i\) and \(\omega\) respectively represent the imaginary unit and the angular frequency. For dispersion error analysis, we assume an ideal condition, which is plane wave propagation in a free field. Figure 1 shows plane wave propagation in discretized free field using square FEs with element length of \(h\). The plane wave in polar coordinate system is expressed as  
\[p_{x,y}=e^{i(kx\cos\theta+kys\sin\theta)}. \tag{15}\]  
To realize the ideal condition in dispersion error analysis, we remove the source and the dissipation terms from Equations (13) and (14). Then reconstructing the two equations in terms of sound pressure \(\mathbf{p}\) engenders the following expression as  
\[(c_{0}^{2}\mathbf{D}^{-1}\mathbf{M}\mathbf{D}^{-1}\mathbf{K}-\omega^{2}\mathbf{I})\mathbf{p}=0, \tag{16}\]  
where \(\mathbf{I}\) represents a unit matrix. The first term in Equation (16) can be rewritten as  
\[\mathbf{D}^{-1}\mathbf{M}\mathbf{D}^{-1}\mathbf{K}\mathbf{p}=Ap_{x,y}\,, \tag{17}\]  
with  
\[\begin{split}& A=-\frac{1}{2h^{2}}(\alpha_{m}^{2}(C_{x}-1)-C_{x}-1)( \alpha_{m}^{2}(C_{y}-1)-C_{y}-1)(1+\alpha_{k}^{2}(C_{x}-1)(C_{y}-1)-C_{x}C_{y}), \\ & C_{x}=\cos(k^{h}h\text{cos}\theta),\ \ \ C_{y}=\cos(k^{h}h\text{sin} \theta),\end{split} \tag{18}\]  
where \(k^{h}\) represents the numerical wavenumber. More detailed calculation procedures can be found in one report of the literature [31] for 2D case and in another [24] for the 3D case. The substitution of Equation (17) into Equation (16) leads to a dispersion relation as  
\[c_{0}=c^{h}k^{h}\sqrt{\frac{1}{A}}\,. \tag{19}\]  
Taking the Taylor expansion with respect to \(k^{h}\), the numerical sound speed is evaluated as  
\[c^{h}\!\approx\!\!c_{0}(1-\frac{(kh)^{2}}{24}((4-3\alpha_{m}^{2})-2(3\alpha_{k }^{2}-2)\cos^{2}\!\theta\sin^{2}\!\theta)). \tag{20}\]  
The use of the following integration points clearly eliminates the second-order dispersion error term in Equation (20).  
\[\alpha_{k}=\pm\sqrt{\frac{2}{3}}\,,\ \ \ \ \alpha_{m}=\pm\sqrt{\frac{4}{3}} \tag{21}\]  
With Equation (21), the fourth-order accuracy in space is achieved as shown below.  
\[c^{h}\approx c_{0}(1-\frac{(kh)^{4}}{1440}(8-19\cos^{2}\!\theta\sin^{2}\! \theta)) \tag{22}\]
# Continual Learning for Table Detection in Document Images
## 3 Experimental Setup
### Datasets  
In total, we utilized four modern publicly available datasets: TableBank, PubLayNet, PubTables-1M, and FinTabNet. Table 1 summarizes datasets' statistical information.  
* **TableBank**[18] TableBank has been collected from the _arXiv_ database [64], containing more than 417 K labeled document images. This dataset comes with two splits, Word and Latex. We combined both for training.
* **PubLayNet**[65] PubLayNet is another large-scale dataset that covers the task of layout analysis in documents. Contrary to manual labeling, this dataset has been collected by automatically annotating the document layout of PDF documents from the PubMed CentralTM database. PubLayNet comprises 360 K document samples containing text, title, list, figure, and table. All document samples from the PubLayNet dataset that contain tabular information were excluded for our experiments.
* **PubTables-1M**[66] This dataset is currently the largest and most complete dataset that addresses all three fundamental tasks of table analysis. For our experiments, we include the annotations of table detection from this dataset that consists of more than 500 K annotated document pages. Furthermore, we unify the annotations for various tabular boundaries in this dataset with a single class of tables to conduct joint training.
* **FinTabNet**[67] We employ FinTabNet to increase samples' diversity. FinTabNet is derived from the PubTabNet [19] and contains complex tables from financial reports. This dataset comprises 70 K document samples with annotations of tabular boundaries and tabular structures.