esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.  
multi-access edge computing; edge caching; mobile video; reinforcement learning +
Footnote †: journal: Medical Image Communication  
2022
# Recognition of mRNA N4 Acetylctidine (ac4C) by Using Non-Deep vs. Deep Learning
## 4 Results
### Performance Metrics  
The proposed model is evaluated in this study with the receiving operating curve region (ROC) and the recall area of precision (PRC). The PRC has the best choice to 'test the model's efficiency due to the imbalance of the benchmark data sets. Furthermore, in several recently published studies, accuracy (ACC), specificity (Sp) and sensitivity (Sn) have helped to evaluate the consistency of bioinformatics classification systems [52, 54, 55, 56, 57]. We are also using them to determine the model's efficiency. If n+ represents acetylcholine sites, then n- is represented at the non-acetylidine sites. It represented acetylcytidine sites that have been wrongly identified as non-acetylidine and is an incorrectly labelled number of non-acetylidine sites. These assessment measurements are described as in Equations (39)-(41). Figure 3 shows the comparison of different methods, on cross validation dataset and independent data test set. Figure 4 show the training process of DL-acc4C model and find very good accuracy and Table 3 shows the detail comparison with existing methods (PACES and XG-acc4C) [61] and proposed method (DL-acc4C), on the bases of ROC and PRC and find better result from proposed method. Figure 3 shows the training process of DL-acc4C model.  
\[ACC=1-(\frac{n_{+}^{+}+n_{+}^{-}}{n^{+}+n^{-}}) \tag{39}\]  
\[SN=1-(\frac{n_{-}^{+}}{n^{+}}) \tag{40}\]  
\[SN=1-(\frac{n_{+}^{-}}{n^{-}}) \tag{41}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Features** & **ROC** & **PRC** & **ACC** \\ \hline \multirow{4}{*}{Regression} & One-Hot & 0.812 & 0.381 & 0.887 \\  & NCP + ND & 0.976 & 0.392 & 0.885 \\  & K-mer & 0.842 & 0.274 & 0.903 \\  & EIIP + PseEIIP & 0.780 & 0.354 & 0.903 \\ \hline \multirow{4}{*}{SVM} & One-Hot & 0.784 & 0.361 & 0.900 \\  & NCP+ND & 0.821 & 0.384 & 0.903 \\  & K-mer & 0.847 & 0.429 & 0.907 \\  & EIIP + PseEIIP & 0.849 & 0.527 & 0.918 \\ \hline \multirow{4}{*}{BMAML} & One-Hot & 0.787 & 0.364 & 0.901 \\  & NCP+ND & 0.799 & 0.348 & 0.904 \\  & K-mer & 0.847 & 0.502 & 0.917 \\  & EIIP + PseEIIP & 0.863 & 0.514 & 0.911 \\ \hline \multirow{4}{*}{DL-ac4C} & One-Hot & 0.881 & 0.569 & 0.931 \\  & NCP+ND & 0.914 & 0.606 & 0.922 \\ \cline{1-1}  & K-mer & 0.901 & 0.559 & 0.938 \\ \cline{1-1}  & EIIP + PseEIIP & 0.932 & 0.663 & 0.931 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DL-ac4C and comparison with existing methods.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
###### Abstract  
Blockchain technology provides a reliable information access environment for the Internet of Vehicles, but the high latency and complex computing consensus mechanism in blockchain make it difficult to port to onboard devices. Recently, there are many methods to reduce the time cost of consensus by optimizing node grouping or reducing redundant calculations, but this would lower the security level of the blockchain. To address these issues and reduce the adverse effects of frequently changing channel quality on consensus results, a consensus mechanism based on vehicle comprehensive state factors for nodes selection (PoMS) is proposed. Firstly, the vehicle nodes utilize the machine learning model to predict local driving parameters and broadcast the predicted results to the other nodes. Secondly, each node uses interactive data to calculate the state values, and the leader comprehensively evaluates the nodes participating in the consensus and selects the nodes as relays. Finally, we also adopted a double-layer blockchain structure to accelerate the selection process of relay nodes. In order to verify the performance of the proposed consensus algorithm, we conducted tests on transmission time and communication quality. The experimental results show that compared to traditional consensus mechanisms, the algorithm proposed in this paper can reduce time overhead by an average of 12.7% and maintain a good transmission rates under a certain number of malicious nodes.  
Internet of Vehicles; blockchain; consensus mechanism; node selection Article
## 4 Results
### Analysis  
We further analyzed the improvement capability and speed of our assessments while working on both cloud and local computing for both assessment types, as illustrated in Figure 9. For this evaluation, we collected the results starting from the beginning of training up to 10,000 iterations and assessed the improvement every 1000 iterations. The observations on the CAP-UDF method are shown below.  
There was a slightly better improvement compared to the AnchorUDF method during this period at 5000 iterations. However, at the end of each evaluation period, it was evident that our adopted method increased the likelihood of creating objects more effectively.  
In addition to the assessment of improvement, we also considered the training and testing time. The estimation is based on a batch size of 5000 epochs. We observed that our learning time frame was similar to that of the standard CAP-UDF method, as both rely on training resources. The main difference lies in the required AnchorUDF, which affects the process of creating an object and expanding its resolution. This process takes twice as long to complete compared to the standard CAP-UDF method, whereas for the evaluation of deployment to the client user, we observed that it took more than 10 s. This increase in time was primarily due to transportation costs, which included uploading files and transferring objects to the projector, subject to the mode of transportation. However, the evaluation process itself takes less time compared to the evaluation of Anchor-UDF. This is because pinning the object requires a higher resolution for object translation, which contributes to faster evaluation times. We can witness this capability by employing cloud computing, and we can thus achieve evaluation speeds that are comparable to the local computing of CAP-UDF and even surpass the evaluation speed of the local Anchor-UDF.
## 3 Suppression Methods for Low-Frequency Pressure Fluctuation
### Vortex Generators
#### 3.1.1 Two-Dimensional Vortex Generators  
The first vortex generators used to suppress low-frequency pressure fluctuations in open-jet wind tunnels were primarily two-dimensional sheet-like spoilers, commonly referred to in the literature as vanes, tabs, or teeth, etc. [16; 63].  
Figure 6 is a typical sheet-like vortex generator called a Seiferth-wing, which is characterized by rectangular sheets with two different angles of inclination to induce disturbances [16]. According to [PERSON] [16], these spoilers promote intense mixing within the jet shear layer, thereby dispersing the vortex rings and preventing their simultaneous impact on the collector. Implemented in the DNW-NWB low-speed wind tunnel in Braunschweig, Germany, Seiferth-wings successfully mitigated low-frequency oscillations; however, due to their generation of broadband noise, they were abandoned when the wind tunnel underwent acoustic modifications in 2009 [2].  
Researchers at NASA Langley Research Center designed six different configurations of nozzle spoilers for a 4 m \(\times\) 7 m open-jet wind tunnel [16] and used hot-wire anemometers to measure velocity pulsations at the jet center to assess their effectiveness in mitigating flow pulsations. The results showed that the alternating arrangement of triangular spoilers had superior effectiveness, as shown in Figure 7.  
The French S2A automotive wind tunnel used a system of two rectangular flaps with a small plunging angle, installed at the upper edge of the nozzle exit (see Figure 8). This solution had less intrusion into the jet than classical vortex generators, resulting in improved flow-field quality and reduced flow noise [8].  
Figure 6: Schematic of Seiferth-wings on wind tunnel nozzle.  
Figure 7: Schematic of triangular vanes on wind tunnel nozzle.  
[PERSON] et al. [50] investigated rectangular spoilers that protruded from and were aligned parallel to the nozzle wall. According to numerical simulations, using vortex generators was effective in reducing velocity pulsations and low-frequency pressure fluctuations.
# Throughput of Buffer with Dependent Service Times
## 3 Buffer Model  
We consider a packet buffer in which a queue of packets waiting for transmission is stored. The queue is emptied from the front in FIFO manner, while the service (transmission) times of packets are mutually dependent and follow Markovian service process [16]. The description of the Markovian service process requires an underlying phase process, \(S^{*}(t)\), with the space of possible phases \(\{1,\ldots,m\}\), and two \(m\times m\) matrices, \(\mathbf{L}_{0}\) and \(\mathbf{L}_{1}\). Moreover, \(\mathbf{L}_{0}\) has negative elements on the diagonal and non-negative elements off the diagonal, whereas \(\mathbf{L}_{1}\) is non-negative and \(\mathbf{L}_{0}+\mathbf{L}_{1}\) is a transition-rate matrix.  
During a busy period, the phase process \(S^{*}(t)\) evolves exactly as a continuous-time Markov process with transition-rate matrix \(\mathbf{L}_{0}+\mathbf{L}_{1}\). Moreover, if \(S^{*}(t)=i\) at some \(t\) during a busy period, then at \(t+\Delta\), the phase may switch to \(j\) with probability \((\mathbf{L}_{0})_{ij}\Delta+o(\Delta)\), and the current packet transmission is continued, or the phase may switch to \(j\) with probability \((\mathbf{L}_{1})_{ij}\Delta+o(\Delta)\), and the current packet transmission is completed, so the packet leaves the buffer. During an idle period \(S^{*}(t)\) remains constant, i.e., the last active phase during the previous busy period is preserved.  
The buffer receives packets according to a renewal process. The interarrival interval span has a distribution function \(G(t)\) of any form. We assume that the expected span of the time interval to the next packet, \(\overline{G}\), is finite,  
\[\overline{G}=\int_{0}^{\infty}\
u\mathrm{d}G(\
u)<\infty, \tag{1}\]  
and denote its standard deviation by \(S\)  
\[S=\sqrt{\int_{0}^{\infty}(\
u-\overline{G})^{2}\mathrm{d}G(\
u)}, \tag{2}\]  
while the coefficient of variation by \(C\) is  
\[C=\frac{S}{\overline{G}}. \tag{3}\]  
The arrival rate, \(\Lambda\), measured in packets per time unit, is the reciprocal of \(\overline{G}\):  
\[\Lambda=\frac{1}{\overline{G}}.\]  
The buffer size is finite and equal to \(K\), which includes the transmission position. If, upon the entry of a new packet, the buffer is already saturated, i.e., there are \(K\) packets stored, then the new packet is deleted, i.e., lost.  
The rate of packet transmission, \(\mu\), can be calculated as  
\[\mu=\pi\mathbf{L}_{1}\mathbf{1}, \tag{4}\]  
where \(\pi\) is the stationary distribution for transition-rate matrix \(\mathbf{L}_{0}+\mathbf{L}_{1}\), whereas  
\[\mathbf{1}=[1,\dots,1]^{T}. \tag{5}\]  
The load is defined in the usual way as  
\[\frac{\Lambda}{\mu}. \tag{6}\]  
The buffer occupancy at time \(t\) is represented by \(N(t)\). It includes the transmission position, if taken.  
Lastly, it is assumed that the first arrival time has distribution \(G(t)\), as any other interarrival interval. In other words, the arrivals are not shifted at the time origin.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 7 Endoscopy
### Miscellaneous
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Database** & **Result** & **Limitations** & **Advantages** \\ \hline \multirow{4}{*}{[PERSON] et al., 2018 [57]} & \multirow{4}{*}{Segmentation of polyps} & & CVC-356, & & & \\  & & CVC612 [58], & & & \\  & & CVC-968, & & & \\ \cline{1-1} \cline{5-6}  & & GANs with Xcept only spatial features are extracted & \begin{tabular}{c} CVC-12, \\ Kvasir [53], \\ and Nerthus [33], \\ publicly available \\ \end{tabular} & Accuracy = 90.9\% & \begin{tabular}{c} No information \\ about poor quality images or \\ processing time is \\ given \\ \end{tabular} & Robust method that achieves high accuracy \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & \\ \hline \hline \multirow{4}{*}{[PERSON] et al., 2020 [59]} & \multirow{4}{*}{Segmentation of polyps} & MobileNetV2 with residual block only spatial features are extracted & \
\begin{tabular}{c} Kvasir Capsuel- \\ Kvasir-SEG [60], \\ 1000 images, \\ publicly available \\ \end{tabular} & Accuracy = 94.5, F2 = 0.83\% &
\begin{tabular}{c} Achieves high accuracy and \\ robustness with \\ publicly available \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of contributions where the segmentation task is applied to endoscopy videos.
###### Abstract  
The objective of this study is to further promote and apply the structural monitoring system to medium and small bridges, given the relative delays in scientific research, technical specifications, and engineering practice for the safety monitoring of small and medium-span girder bridges, as well as the relative simplicity of the structural system of these bridges, their well-defined forces, and the relatively large proportion of live load responses during operation. These concepts are proposed based on the evaluation method of live loading (As therefore, this paper suggests the notion of sensor acquisition frequency and appropriate acquisition frequency based on the live load assessment method and the fundamental reliability theory). Based on the time-history curve depicting the midspan deflection response of the vehicle-bridge coupling system, the frequency domain analysis reveals that the power spectrum at \(-3\) dB corresponds to the response cutoff frequency. Significantly, the cutoff frequency mentioned is double the acquisition frequency considered suitable for the study. Based on the definition of a quasi-static response, it can be deduced that the velocity of a load does not exert any influence on the quasi-static response of a bridge structure. As a result, the derivation of the components related to the midspan deflection of a bridge's quasi-static response is presented, together with a recommended set of methodological guidelines for the extraction of finite elements. This study introduces a novel approach for determining the cutoff frequency of the structural response by utilizing the characteristics of amplitude spectrum estimation and power spectrum estimation in frequency domain analysis. The cutoff frequency of the signal is determined by analyzing the amplitude-frequency curve of the power spectrum. Subsequently, the probability density function of the original time-history curve data is estimated based on the amplitude spectrum. Finally, reliability analysis is conducted by calculating the ratio of the amplitude spectrum area of the signal obtained through a reasonable acquisition frequency to the area of the amplitude spectrum function of the original signal. This analysis verifies the reliability of the proposed method for determining the midspan deflection acquisition frequency.  
bridge engineering; small- and medium-span beam bridges; structural health monitoring; quasi-static response; reasonable acquisition frequency +
Footnote †: journal: Applied Sciences
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results
### Simulation Model  
Figure 8 shows the node deployment, where total 48 nodes (circles) are located around each grid point with 10% variation in the spacing and a sink (filled circle) at the center of the network. Each transmission range could be different in real world applications, but we assumed that all of the nodes have the same transmission range. We believe that the differences are inevitable errors since it is too complicated and difficult to solve but can be avoided if the nodes are placed accordingly such that the differences do not occur. The traffic generated by a node follows the Poisson arrival process with a rate of \(\lambda\). In cases when multiple _DATA_ packets need to be transmitted, the nodes prioritize the relayed data from the neighbors over their own data. In addition, we assumed that the nodes operate in half-duplexing mode [32] and the packet loss is considered to occur only through collisions.  
Table 1 summarizes the system parameters used in the simulation. For the values of the data rate and the power consumed for transmission and reception, we refer to the commercial Teledyne Benthos ATM-885 underwater modem [33].  
Figure 8: Node deployment.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 4 Experimental Results
### Mode Prediction Accuracy Evaluation
In the opposite case where rotation is dominant, like Blue Sky, the sensitivity for the zoom models is limited, but it also has a limited effect overall because of the rarity of the other mode. For some sequences, especially Twilight Scene, the accuracy when compared to the alternative trying every possible transform is very low. However, in this case skipping wrongly according to the reference leads to better results that will be explained in the following subsection. To better investigate why the mispredictions were so common for some sequences, we recorded the affine parameters that were used both when our proposed method predicted accurately and when it gave a bad prediction. The results for the Rotating Disk sequence are presented in Figure 1. It appears that while for all cases smaller (in absolute value) parameters are more common, the wrong predictions have an even higher percentage of small values than the accurate prediction. This suggests that in these cases, the transform brings a smaller gain. As differentiating between the different motions for smaller movements is more complex, the limited accuracy for mode prediction can be understood.
## 4 Discussion
#### Limitations  
Methodological problems and the failure to evaluate potential confounding variables, such as socioeconomic status (SES), parental intake, and vegetable availability, which were likely linked to vegetable intake, are some of the study's limitations. Analytically, it seems that SES plays a vital role in children's healthy dietary habits as higher family SES is favorably linked with vegetable consumption [48; 49]. Additionally, there is a higher likelihood that children of parents who consume an adequate daily quantity of vegetables will also follow healthy dietary habits [50]. Moreover, the fact that home availability of vegetables was not recorded constitutes another potential limitation, as it is considered to positively associate with children's consumption of vegetables [51]. This study's cross-sectional methodology, which makes it impossible to prove causation, is another potential limitation. Because the information on PA, sedentary time, sleep patterns, and eating habits was self-reported, social desirability bias could affect it. However, because responses were anonymous, participants had little incentive to underreport. Lastly, statistical significance is easily attained due to the huge sample size, and as a result, it candetet trivial differences. Consequently, future research, such as longitudinal studies to examine temporal relationships, intervention studies to promote vegetable consumption, or qualitative research, could be applied to better understand barriers and facilitators of vegetable consumption among schoolchildren.
## Appendix A Integrals with Squares of the Laguerre Polynomials  
Since \(\left|\exp\left[ir^{2}\dot{\varepsilon}/(2\hbar\varepsilon)\right]\right|^{2}= \exp\left(-Kr^{2}\right)\), due to identity (9), the calculation of the mean values \(\langle r^{2}\rangle\) in the state (6) can be reduced (using the substitution \(x=Kr^{2}\)) to the calculation of the integrals  
\[I_{j}=\int_{0}^{\infty}x^{a+j}e^{-x}\Big{[}L_{n}^{(a)}(x)\Big{]}^{2}dx. \tag{11}\]  
These integrals are absent in the available reference books (such as [4], for example). However, it is easy to see that \(I_{1}=-\lim_{b\to 1}\partial J(b)/\partial b\), where \(J(b)\) is the special case of a more general integral 7.414.4 from [4]:  
\[J(b) = \int_{0}^{\infty}x^{a}e^{-bx}\Big{[}L_{n}^{(a)}(x)\Big{]}^{2}dx \tag{12}\] \[= \frac{(2n+a)!(b-1)^{2n}}{(n!)^{2}b^{2n+a+1}}F\bigg{[}-n,-n;-2n-a; \frac{b(b-2)}{(b-1)^{2}}\bigg{]}.\]  
Here, \(F(u,v:w;z)\) is the Gauss hypergeometric function. Using the definition of this function, we can write  
\[J(b) = b^{-2n-a-1}\bigg{\{}\frac{(n+a)!}{n!}[b(2-b)]^{n}+(b-1)^{2}[b(2- b)]^{n-1}\frac{(n+a+1)!}{(n-1)!} \tag{13}\] \[+(b-1)^{4}f(b)\bigg{\}},\]  
where \(f(b)\) is some function, which is regular at the point \(b=1\). Calculating the derivative of function (13) with respect to \(b\) and putting \(b=1\) in the final expression, we arrive at the formula  
\[I_{1}=\frac{(n+a)!}{n!}(2n+a+1). \tag{14}\]  
Its consequence is Formula (15). Similarly,  
\[I_{2}=\lim_{b\to 1}\partial^{2}J(b)/\partial b^{2}=\frac{(n+a)!}{n!}[6n(n+a+1) +(a+1)(a+2)]. \tag{15}\]
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 4 Results and Discussion
### Comparison with Existing Works  
Table 6 presents a comparison between the proposed method and previous related works in the field of image steganography. The performance of the proposed method was evaluated and compared to those of the classical least significant bit (LSB) method, the over-multiplexed Vega (OMVG) method, the Kaur method, and the Liao method based on the Peak Signal-to-Noise Ratio (PSNR) of cover images. The results show that the proposed method outperforms the other ways regarding PSNR, with the highest values observed in Images 2 and 3, where the PSNR reached 85.8855 and 85.6411, respectively. In contrast, the classical LSB method had the lowest PSNR values among the comparison methods, with PSNR values ranging from 50 to 58.62 in Images 1 to 3.