esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Equipment and Data Description
The weight function is defined as follows:  
\[\Psi(u)=\frac{1}{u(1-u)}. \tag{1}\]  
where \(\Psi(u)\) is some nonnegative weight function chosen by the experimenter to accentuate the values, \(u\) denotes the cumulative distribution function value \(F(x)\) at a given data point \(x\). The Anderson-Darling statistic (\(A^{2}\)) is given by  
\[A^{2}=n\int_{-\infty}^{\infty}[F_{n}(x)-F(x)]^{2}\Psi(F(x))dF(x). \tag{2}\]  
where \(n\) is the sample size, \(F_{n}(x)\) and \(F(x)\) refer to the empirical and theoretical cumulative distribution function, respectively. Substituting the weight function into Equation (2), the Anderson-Darling statistic is transformed as follows:  
\[A^{2}=-n+\sum_{i=1}^{n}\frac{1-2i}{n}[\ln(u_{i})+\ln(1-u_{n+1-i})]. \tag{3}\]  
where \(u_{i}\) is the cumulative probability for the \(i\)-th ordered observation in the sample. The Shapiro-Wilk test is used to assess data normality, rejecting the null hypothesis and accepting the alternative if the \(p\)-value is less than 0.05. In other words, if the \(p\)-value is 0.05 or lower, the data do not follow a normal distribution, as shown in Equation (4). Given that each row of the input data contains 11,958 data points, a Shapiro-Wilk test was conducted using a randomly sampled subset of 5000 data points. The equation is presented below [33; 34; 35]. \[\mathrm{W}=\frac{(\sum_{i=1}^{n}a_{i}y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y})^{2}}. \tag{4}\]  
In Equation (4), \(W\) is the Shapiro-Wilk test statistic used for assessing normality. Here, \(y_{i}\) represents the \(i\)-th ordered observation, \(\overline{y}\) is the sample mean, and \(a_{i}\) is the predetermined weight based on expected values of order statistics from a standard normal distribution. The Kolmogorov-Smirnov test is a nonparametric method used to quantitatively compare differences between data distributions or assess whether they follow a normal distribution. If the \(p\)-value is lower than the significance level, the null hypothesis is rejected, indicating that the data do not follow a normal distribution. The equations are as follows:  
\[F_{n}(x)=\frac{1}{n}\sum_{i=1}^{n}I_{X_{i}\leq x}. \tag{5}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Instantaneous CO2** & **Main Engine Load** & **Shaft Power** & **Fuel Flow** & **M/E T/C RPM** & \(\cdots\) \\ \hline Anderson–darling & & & & Rejected null hypothesis & \\ Shapiro–Wilk (\(p\)-value) & 3.47E-61 & 1.64E-64 & 9.62E-78 & 8.88E-93 & 2.10E-66 & \(\cdots\) \\ K–S (\(p\)-value) & 0 & 0 & 0 & 0 & 0 & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the normality test for the experimental data. \[\mathrm{D}=\max|F_{n}(x)-F(x)|. \tag{6}\]  
Here, \(F(x)\) represents the empirical distribution function, \(n\) is the number of observations, and \(I_{Xi\leq x}\) is the indicator function. When D is lower than the significance level, the null hypothesis is rejected, indicating that the data do not follow a normal distribution.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
###### Abstract  
UAV-assisted mobile edge computing combines the flexibility of UAVs with the computing power of MEC to provide low-latency, high-performance computing solutions for a wide range of application scenarios. However, due to the highly dynamic and heterogeneous nature of the UAV environment, the optimal allocation of resources and system reliability still face significant challenges. This paper proposes a two-stage optimization (DSO) algorithm for UAV-assisted MEC, combining Stackelberg game theory and auction mechanisms to optimize resource allocation among servers, UAVs, and users. The first stage uses a Stackelberg game to allocate resources between servers and UAVs, while the second stage employs an auction algorithm for UAV-user resource pricing. Blockchain smart contracts automate task management, ensuring transparency and reliability. The experimental results show that compared with the traditional single-stage optimization algorithm (SSO), the equal allocation algorithm (EAA) and the dynamic resource pricing algorithm (DRP), the DSO algorithm proposed in this paper has significant advantages by improving resource utilization by 7-10%, reducing task latency by 3-5%, and lowering energy consumption by 4-8%, making it highly effective for dynamic UAV environments.  
drones; blockchain; Stackelberg games; auction algorithms; two-tier games +
Footnote †: journal: Applied sciences
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 5 Discussions and Perspectives
The technique of PL is not yet mature and still needs time to be optimized. The targets of improvement, such as the channel number, insertion loss, and crosstalk, are still far from applicable. Although the physical processes of these devices are different, their fundamental principle is the same, that is, coupling the fundamental mode to higher order modes in the corresponding channels, and carefully adjusting the four degenerated modes in a particular higher order. In special combinations of amplitudes and phases among four degenerated modes, the OAM mode can be obtained. So far, two types of combination among the four degenerated modes can be used to generate the OAM mode. The first type is the pure OAM state. In this situation we can obtain an OAM beam directly. The other type is the state consisting of two orthogonal polarized OAM modes with the opposite TC. By selecting the polarization, we can obtain the corresponding OAM mode. The latter method exhibits an extra benefit that the TC is adjustable. In summary, OAM beams are receiving increasing interest in various areas due to their novelty and potential applications. There are many methods used to generate OAM beams, each with its own advantages and disadvantages. With different bottlenecks, all the technique still require time to realize commercialization. As a branch of OAM generation methods, fiber methods have contributed to the generation of OAM beams with the advantages of miniaturization and low insertion loss but challenges in robustness. Despite this, along with the efforts from researchers all around the world, we may see an increasing number of applications based on OAM beams in the future. [PERSON] mainly wrote the Section 1, Section 4, prepared the copyright and integrated the contents of advances. [PERSON] mainly wrote the Section 2, Section 3, Section 5. [PERSON], [PERSON] and [PERSON] provided constructive improvements and feedback. [PERSON] largely guides the completion of this review. This work was jointly supported by the National Natural Science Foundation of China under grant nos. 61835006, 11674177, 61775107 and 11704283, Tianjin Natural Science Foundation under grant no. 16J[CZD][C31000, the National Key Research and Development Program of China under grant nos. 2018 YFB0504401 and 2018 YFB070, and the 111 Project (B16027). The authors declare no conflict of interest.
## 3 Experimental Outcomes
### Mechanism of Adsorption  
The adsorbate, NIFGS, is a cellulosic material. The processes of the adsorption of CV dye onto cellulosic NIFGS comprises cellulose, hemicellulose and lignin. All the three materials invariably contain -OH groups. The plausible mechanism of cationic CV dye adsorption is likely to take place as follows:  
* The progression of adsorption is a multistep activity;
* The factors that have credible influence on the process of adsorption are the solution acidic level (pH), the concentration of dye, the amount of adsorbent used and variation in temperature;
* Monolayer is a formation initiated when the CV mass transfer occurs onto NIFGS;
* The process of diffusion is likely to be a slow process;
* The strong adherence of CV dye onto NIFGS is probably by bonds established between dye N\({}^{+}\) anions and hydrogen of cellulosic -OH group;
* Weak interaction is due to Van der Waals forces of attraction and strong electrostatic forces of attraction is because the -N\({}^{+}\) cationic group and the -OH\({}^{+}\) group negative charge of NIFGS contribute substantially to the adherence of the dye onto NIFGS.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 2 System Model and Problem Formulation  
The proposed system consists of a hybrid access point with a single antenna, an IRS surface with \(N\) reflecting elements, \(K\) users equipped with a single antenna each (denoted \(\{u_{1},u_{2},\ldots,u_{K}\}\)), a single-antenna eavesdropper, and a single-antenna energy harvesting jammer. We consider the H-AP to have no direct link with the users, but there is line of sight between the H-AP and users through the IRS to facilitate energy and information transfer, as shown in Figure 1 and Table 3. A TDMA scheme is employed where in each coherence time frame, the H-AP broadcasts energy signals to wireless users and the EH jammer in the downlink phase. All users and the jammer harvest energy on downlink, while the UDs transmit information to the H-AP on uplink, as shown in Figure 2. Within the system, an eavesdropper tries to intercept uplink transmissions between the H-AP and users, while the jammer generates a signal to interfere with the eavesdropper (see Figure 1).
## 3 Results
### Model Fitting
There was a significant (_p_ < 0.05) interaction between the MAE time and the microwave power (X1X2), the extraction time and the solid-liquid ratio (X1X3), and the microwave power and the solid-liquid ratio (X2X3). Figure 3j-l display 3D response surface plots that illustrate how the impact of the MAE independent factors and their interactions affect antioxidant activity. The second-order polynomial equation of DPPH is shown as Equation (7). \[ \begin{split}\text{Y}_{4}=95.20&-3.284\text{X}_{1} +2.188\text{X}_{2}+2.662\text{X}_{3}-9.75\text{X}_{1}^{2}+20.13\text{X}_{2}^{2} \\ &\qquad+3.030\text{X}_{1}\text{X}_{2}+4.330\text{X}_{1}\text{X}_{3 }+2.50\text{X}_{2}\text{X}_{3}\end{split}  \tag{7}\]  
Figure 3j shows that an MAE time of 24-25 min combined with a microwave power of 150-250 W tends to achieve the lowest 50% radical scavenging inhibition concentration (IC50), below 90 \(\upmu\)g/mL. Figure 3k shows that an MAE time of 24-25 min with a solid-to  
Figure 3: Response surface 3D plots showing the interactive effect of MAE parameters on (**a–c**) yield, (**d–f**) TPC, (**g–i**) TPC, and (**j–l**) IC50. liquid ratio of 1:60-1:130 g/mL similarly tends to yield the lowest IC\({}_{50}\), below 80 ug/mL. Figure 3l shows that using a microwave power between 190 and 240 W and a solid-to-liquid ratio of 1:60-1:170 g/mL is also likely to achieve the lowest IC\({}_{50}\), below 95 ug/mL. When the extraction time is less than 24 min, the DPPH radical scavenging is low. Moreover, When the microwave power is below 150 W or above 250 W, the DPPH radical scavenging activity is low. Low microwave power may not be sufficient to extract an adequate amount of polyphenols, leading to diminished antioxidant activity. Conversely, excessive microwave power can cause the thermal degradation of polyphenols, which also results in a reduction in their antioxidant properties [9]. The optimization of MAE parameters using FCCD and RSM successfully developed reliable models for yield, TPC, TFC, and DPPH scavenging activity. These models are characterized by high R\({}^{2}\) and R\({}^{2}\) Adj values and non-significant lack-of-fit, confirming their reliability. The significant quadratic terms in the models enable the use of polynomial equations, which effectively predict conditions to maximize extraction yield and antioxidant concentrations while minimizing IC\({}_{50}\), further emphasizing the efficiency of MAE for functional ingredient production.
## 2 Related Work
### Multi-Hop Approaches  
In these approaches, underwater sensing data are forwarded hop-by-hop along routing paths. Thus, routing technique plays the key role in these kinds of approaches. Different from terrestrial wireless sensor networks (WSN), the underwater nodes move with water flows involuntarily in the underwater environment [12; 13; 14; 15; 16], and routing protocols designed for WSNs can not be directly applied to UWSNs. Thus, the research focus in this area is to design energy-efficient and reliable routing protocols.  
Based on the information required in the protocols, most underwater routing can be classified into location-based and depth-based. Location-based routing protocols, such as vector-based forwarding [17], hop-by-hop vector-based forwarding [18], depth-controlled routing [19], etc., assume that each node knows its location and each packet contains the position of the sender. Depth-based routing protocols, such as depth-based routing [20], directional depth-based routing [21], and depth-based multi-hop routing [22], etc., use depth information to route the packets instead of the full location coordinates.  
Due to the energy-extensive consumption of underwater acoustic communication and limited battery power, multi-hop approaches will reduce the lifetime of the network.
## 1 Introduction
Consequently, utilizing digital maps in the evaluation of receiver sensitivity and communication performance can enhance the accuracy of the results. The main contributions of this article are as follows. (1) According to the developed digital map, a terrain profile and a contour map are obtained. The vehicle communication system propagation model is established based on the actual terrain. Through the above propagation model, the power received by the receiver and the reduction in the receiver's sensitivity are calculated. The correctness of the evaluated model is verified in comparison with the free-space propagation model and the measured data. (2) Using the actual terrain propagation model of the vehicle communication system, the isolation between the vehicle-mounted antennas is calculated. Compared with the measured data, the correctness of the evaluation model is verified. (3) The decreases in the communication distances of the vehicle communication system under a free-space propagation model and an actual terrain propagation model are analyzed and compared. The remainder of this paper is structured as follows. Section 2 provides a succinct introduction to the theoretical mechanism of EMI. Section 3 presents the modified five-level evaluation model for armored vehicular communication systems, encompassing the model's architecture, evaluation components, methodologies, and criteria for each level. To validate the proposed model, Section 4 utilizes an example of an armored vehicular communication system. In Section 5, the performance of the proposed model is discussed. Finally, Section 6 concludes the paper by summarizing the key findings and implications.
## 2 Calculation Model and Solution Method
### Establishment and Solution of the Difference Equation
#### 2.2.1 The Difference Equation  
To establish the difference equation, the pile length is first divided into \(n\) equal parts, of length \(\lambda\). For layered soil, attention should be paid to the disposal of sections at the soil layer interface, as shown in Figure 2, and this allows for the following system to be established:  
\[\left\{\begin{array}{l}\left(\frac{dy}{dz}\right)_{i}=\frac{y_{i+1}-y_{i-1}} {2\lambda}\\ \left(\frac{d^{2}y}{dz^{2}}\right)=\frac{y_{i+1}-2y_{i}+y_{i-1}}{\lambda^{2} }\\ \left(\frac{d^{3}y}{dz^{3}}\right)_{i}=\frac{y_{i+2}-2y_{i+1}+2y_{i-1}-y_{i-2} }{2\lambda^{3}}\\ \left(\frac{d^{4}y}{dz^{4}}\right)_{i}=\frac{y_{i+2}-4y_{i+1}+6y_{i}-4y_{i-1}+ y_{i-2}}{\lambda^{4}}\end{array}\right. \tag{3}\]  
Figure 1: Force analysis of a single pile micro-element.  
Equation (3) is substituted into Equation (2) to obtain the deflection difference equation for the pile shaft, with section \(i\):  
\[f_{i}y_{i+2}+b_{i}y_{i+1}+c_{i}y_{i}+d_{i}y_{i-1}+g_{i}y_{i-2}=0 \tag{4}\]  
where \(f_{i}=EI\), \(b_{i}=-4 EI+N_{i}\lambda^{2}+\xi\lambda^{3}/2\), \(c_{i}=6 EI-2N_{i}\lambda^{2}+\lambda^{4}b_{0}c_{zi}\), \(d_{i}=-4 EI+N_{i}\lambda^{2}-\xi\lambda^{3}/2\), and \(g_{i}=EI=f_{i}\).
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 1 Introduction  
Coordinated multipoint (CoMP) techniques reduce intercell interference and increase the cell-edge throughput by integrating numerous evolved node base stations\({}^{\prime}\) (eNBs) transmissions. Specifically, the CoMP methodology involves joint transmission (JT), dynamic point selection (DPS), coordinated scheduling (CS), and coordinated beamforming (CB). Additionally, CoMP can be used on homogeneous as well as heterogeneous networks. In densely deployed small-cells, the CoMP with CS method is applied to maximize the radio network's ability within the macrocell region covered by the macro eNB [1; 2; 3], as portrayed in Figure 1. Besides, diversity and interference suppression are also associated with CoMP [4]. To accomplish the features of the future generation wireless communication systems, such as speedy data rate with outstanding link quality and unlimited access, priority is being given to the implementation of CoMP ultra-dense, small-cell network (SCN)-based transmission, where SCN allows the additional flexible establishment and excellent communication link quality.  
To achieve better throughput and vigorous mobility, the dual connectivity (DC) technique is presented in 3 rd generation partnership project (3 GPP) release 12 by utilizing radio resources from different eNBs. In comparison with release-11 carrier aggregation (CA), DC is an effective technique introducing standalone secondary eNB (SeNB) for small-cell deployment. For the case of DC-associated UE, it can establish concurrent connections with a master eNB (MeNB) and SeNB. Usually, both eNBs operate at different carrier frequencies and maintain an interconnection using general backhaul links [5; 6].  
Proper advancements in long-term evolution (LTE), LTE-Advanced, and LTE-Advanced Pro technology have already allowed the commercial deployment of the 5G new radio (NR) networks [7; 8]. The utilization of centimeter wave (cmWave) as well as millimeter wave (mmWave) radio frequencies, are observed in both high-bandwidth and low-bandwidth transmissions (3.4 to 3.6 GHz below 6 GHz and then 24.25 to 27.5 GHz, 27.5 to 29.5 GHz, 37 GHz, 39 GHz, and 57 to 71 GHz) [9]. With the help of both frequency division duplexing (FDD) and time division duplexing (TDD) techniques, the multiple access schemes use cyclic prefixed orthogonal frequency division multiplexing (OFDM) in downlink and cyclic prefixed discrete Fourier transform-spread-OFDM (DFT-s-OFDM) in uplink transmission for the NR physical layer [10]. In addition, multiple OFDM-scalable numerologies are supported in 5G NR with various subcarrier spacings (15 KHz, 30 KHz, 60 KHz, 120 KHz, and 240 KHz) and utilization of both low- and higher-order digital modulations (256-QAM, 64-QAM, 16-QAM, QPSK, BPSK, and \(\pi/2\)-BPSK modulation) [11]. In general, the use of CP in OFDM is the most popular way to suppress intersymbol interference (ISI); however, its waste of spectrum resources cannot be neglected when considering the ever-growing high-speed demand [12].
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 3 Modeling
### LTE QoS Services and Applications  
The LTE QoS class indicator (QCI) specifications, alongside the desired priorities for each class of service, are presented in Table 3[26; 27]. By observing Table 3, one can notice that for a specific service type (either guaranteed bit rate (GBR) or non-guaranteed bit rate (non-GBR)) a priority is specified. Each resource type is related to a group of QCIs. Regarding the performance measures, such as the packet error loss and packet delay budget, they are given a minimum threshold value to be achieved to satisfy the required performance for the LTE network. For example, regarding the route between the UE and the gateway, a conversational video of a value of 2 for the QCI must have a priority of 4, a packet error loss rate less than \(10^{-3}\), and a packet delay less than 150 ms.  
Although our investigations were limited to three service categories, in theory, the number of services can be greater, assuming that three types of service categories is common in the literature; however, in our model, increasing the number of service categories maximizes the state space and leads to a state space explosion issue. Agreeing with [6], in the study, the following classes of service are considered:  
1. The web browsing (WB) service: the service belongs to the type of content one can download from the internet, such as files, pictures, music, and movies. A 60% ratio of the total traffic is assumed in the study for this type of service, as most of the applications on the Internet are content-download based and the bandwidth required depends on the QoS that needs to be satisfied for this content. Therefore, to guarantee maximum reliability for the transmitted and received packets for this type of service, a reliable TCP protocol is considered.
2. The voice-over IP over LTE (VOLTE) service: although little bandwidth is required for this kind of service, it is given high priority. It is called the voice-over-Internet protocol over LTE. In this study, the VoLTE (voice-over LTE) name is assumed for this type of service. Emergency calls (e.g., 112) are the best example, considering that each country has a different code for their emergency call. A 10% ratio of the total traffic is assumed for this service, as it is only required for rare emergency cases.
3. The video streaming (STR) service: 30% of the total traffic ratio is given to this type of service. Video streaming requires a reliable connection with a guaranteed QoS. To achieve this reliability, the availability of a large bandwidth is vital.