esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Architecture and Performance Analysis
### Proposed System Architecture  
In this subsection, we discuss the technical issues associated with the high-rate OCC system proposed in this paper and the role of AI techniques in our analyzed system. Figure 6 shows the overall architecture of the proposed system. At first, the Tx diagram should remain the same as in the architecture of the vehicular RoI signaling system in [4]. The innovation in our work here mostly relies on the channel condition after Tx, where we considered the blurring process on LED images before the noise addition process. This consideration makes it more challenging for Rx to detect RoI and decode a high-rate data stream, notably when using the traditional method based on a linear mathematical model with fixed parameters.  
These technical issues lead to the design of an adaptive Rx that could be trained to self-modify its parameters and better reflect the correlation between real-world input data and our desired system output. To be more detailed, AI algorithms could be applied in Rx to enable the following two tasks: Multiple RoIs detection and tracking based on You Only Look Once (YOLO) framework [40] and the RoI information from S2-PSK scheme [8], enhancing the reliability of decoding data by using NN-based decoder for decoding the bit stream, and AI-based error correction (AIEC) for mitigating the error caused by channel condition [41]. In this paper, we focus on designing and analyzing the performance of an NN-based decoder to decode data which are contaminated by the blur process and noise.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**8 States Input**} \\ \hline
**Dimming** & **Dimming** & **Dimming** & **Dimming** & **Dimming** & **Dimming** & **Dimming** & **S\_Phase** \\
**1/8** & **2/8** & **3/8** & **4/8** & **5/8** & **6/8** & **7/8** & **Output** \\ \hline xx000000 & 1x00000x &  
Furthermore, Figure 7 illustrates a process where an NN decoder learning from real-world input data can adjust its parameters to minimize the bias between a predicted value and the actual S_Phase value of a LED group based on the optimization algorithm.  
In our experiments, we utilized LabVIEW to generate datasets for training an NN-based decoder and SER evaluation for several cases. Each dataset had \(10^{4}\) to \(10^{5}\) label samples of a pair of LED groups. We trained our model with 80% of datasets with the lowest SNR at each dimming level. The theoretical principle of our simulation is discussed in the following subsection.
## 3 Results
### Membrane Stabilization Test
#### 3.8.2 Effect of _P. emplica_ and _A. indica_ Extract on Browning  
To understand the mechanism of anti-glycating and AGEs formation inhibition activity, BSA and glucose were incubated together at 37 \({}^{\circ}\)C for 15 days in the absence and presence _P. emplica_ and _A. indica_ ethanolic extracts. It was reported that browning intensity was the initial indicator for glycation [35]. Thus, the browning ratio was estimated at 420 nm. The data show that ethanol extract of _P. emplica_ and _A. indica_ inhibited browning (hence, glycation) in a dose-dependent manner. BSA incubated without glucose showed the least browning. It can be suggested that time-variable structural modifications in BSA  
Figure 8: Protection from hyposolaninity-induced hemolysis. The figure shows that ethanol extract of _P. emplica_ and _A. indica_ protects against hyposolaninity-induced hemolysis in a concentration-dependent manner. The blue columns (from 1–6) at the x-axis denotes concentration 100–600 \(\upmu\)g/mL of _P. emplica_ extract. The yellow columns (from 1–6) at the x-axis denotes the concentration of 100–600 \(\upmu\)g/mL of _A. indica_ extract. Sample 7 denotes samples having diclofenac sodium with a concentration of 50 \(\upmu\)g/mL. The results are displayed as means \(\pm\) SEM (\(n\) = 3). The statistical differences are denoted with asterisk (*) for _P. emplica_, indicating significance at \(p\) < 0.05 in comparison with the reference sample, and a hashtag (#), signifying \(p\) < 0.05 for _A. indica_ in comparison with the reference sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Concentration (ig/mL)**} & \multicolumn{2}{c}{_P. emplica_} & \multicolumn{2}{c}{_A. indica_} \\ \cline{2-5}  & **Percentage Protection** & **S. E. M.** & **Percentage Protection** & **S. E. M.** \\ \hline
100 & 9.43 & 0.133 & 8.01 & 0.033 \\ \hline
200 & 17.98 & 1.079 & 15.63 & 0.078 \\ \hline
300 & 25.69 & 0.321 & 23.24 & 1.346 \\ \hline
400 & 35.73 & 1.058 & 33.47 & 0.588 \\ \hline
500 & 45.56 & 0.003 & 43.16 & 1.903 \\ \hline
600 & 59.28 & 0.711 & 57.27 & 0.283 \\ \hline \end{tabular}
\end{table}
Table 3: Protection from hyposolaninity-induced hemolysis.  
(incubated in the absence of glucose and extract) might be responsible for the browning in this sample. The ethanol extracts of _P. emblica_ and _A. indica_ extract exhibited 61.29 \(\pm\) 0.31% and 71.291 \(\pm\) 0.% at 600 \(\mathrm{\SIUnitSymbolMicro g}\)/mL browning as matched to glycated BSA incubated in the absence of extract but was incubated with glucose (100% browning) (Figure 9). These results show that the reduction in browning intensity in the existence of extracts was correlated with the lesser development of glycated brown products.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 4 Experiment
### Implementation Details  
#### 4.3.1 FDG-Trans  
In this experiment, one specific FDG-Trans is trained for each stock. The dataset consists of 100 stocks in total for one month period from China Securities Index (CSI 300) market. For each stock, the first 19 trading days are used as training data and the rest 3 days test data. For the input of FDG-Trans, recall that we have 4740 time steps for each stock every trading day. LOB information in every continuous 130 time steps will be used as a single input, and the rate of return after 5 min is the corresponding output. The number of 130 is chosen by the cross validation. For test data, all the possible 130 continuous time step information will be used. In other words, \(X_{1}=(\mathbf{x_{1}},\mathbf{x_{2}},\cdots,\mathbf{x_{130}})\), \(X_{2}=(\mathbf{x_{2}},\mathbf{x_{2}},\cdots,\mathbf{x_{131}})\). Thus, we have around 4610 pieces of input data every trading day. For training data, every 1 in 10 possible 130 continuous time step information will be used, which means, \(X_{1}=(\mathbf{x_{1}},\mathbf{x_{2}},\cdots,\mathbf{x_{130}})\), \(X_{2}=(\mathbf{x_{11}},\mathbf{x_{12}},\cdots,\mathbf{x_{140}})\). Thus, we have around 461 pieces of data every trading date. The reason to skip the other 9 is to reduce the auto correlation of our inputs, which would possibly lead to the problem of over fitting.  
In the training of FDG-Trans, [PERSON] is used as the optimizer. The initial learning parameter is set to be 0.001, with an exponential linear decay by 0.9 after every epoch. The training dataset is trained for 75 epochs. The detailed hyper-parameters are summarized in Table 1. The demonstrate the success of training process, the training losses on four sub-datasets are shown in Figure 6.  
Figure 6: Training losses of FDG-Trans.
## 7 Experimental Results
### Period Range  
In this part of the experimentation, the period range of input tasks is varied, and its effect is recorded with respect to the hyper-period, optimal threshold, and overhead. Experiments have been performed for different period ranges as summarised in Table 5.  
Figure 8 exhibits the effect of the varying period range on the hyper-period, optimal threshold, and overhead. In Figure 8a, much like in case of input load, although the period range rises, but the hyper-period always falls within the optimal threshold. It is because it has been fine-tuned after a number of attempts. The top line shows that the optimal threshold and the hyper-period have always fallen below the optimal threshold. The optimal threshold and overhead keep on increasing as the period range broadens. The reason for this is that, for a broad range of periods, there is more probability that higher period values will appear and, as a result, the hyper-period computed will not satisfy the optimal threshold. Consequently, the optimal threshold also increases and so does the overhead.  
Figure 8b explains the effect of the optimal threshold on the period range. The graph structure is similar to input load in a sense that, as the period range increases, there is more probability that the hyper-period will get a higher value. It will cause a more frequent adjustment in the optimal threshold, and, as a result, the overhead will also rise. Overall, the effect of the input tasks and the period range is similar, but, in the case of the period range, the increase in hyper-period, overhead and optimal threshold is sharper. Therefore, the impact of the period on hyper-period is more than that of the number of tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Tasks** & **Period Range** & **Execution Time Range** & **Arrival Time Range** \\ \hline
10 & 1–15 & 1–5 & 1–5 \\
10 & 1–20 & 1–5 & 1–5 \\
10 & 1–25 & 1–5 & 1–5 \\
10 & 1–30 & 1–5 & 1–5 \\
10 & 1–35 & 1–5 & 1–5 \\
10 & 1–40 & 1–5 & 1–5 \\
10 & 1–45 & 1–5 & 1–5 \\
10 & 1–50 & 1–5 & 1–5 \\
10 & 1–60 & 1–
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 3 Evolutionary and Clean Slate Approaches
### Challenges of SDN
#### 3.1.2 Overlay Networks (ON)  
These networks have become increasingly important in the context of the internet's massive growth. Table 1 illustrates some of the networks that fall under this category, including peer-to-peer (P2P) file sharing [32], content delivery caching networks, voice-over IP, and testbed networks. One example of P2P file sharing is Napster, which has not been in use since 2002 due to copyright laws [33]. The internet was originally viewed as an overlay with additional features, such as packet-switched networks, which were added to meet the needs of peer-to-peer research [34].  
In ONs, nodes are connected by virtual links and a Locator ID Separation protocol (LISP) is used. ONs pose certain implications for the emergence of future internet architectures.  
The authors in [34] discussed various types of overlay networks, including content delivery networks (CDNs), routing overlays, and security overlays. Overlay networks pose interesting questions and unique challenges, such as determining the best path between the source and destination in a large network with multiple routes. This requires efficient routing algorithms that can handle diverse network topologies and routing requirements. Additionally, security overlays aim to provide secure communication channels that are resistant to attacks, while CDNs help to distribute content more efficiently by caching popular content closer to the end-users. The emergence of overlay networks has driven research in areas such as network virtualization, software-defined networking, and network function virtualization, with the goal of improving network flexibility, scalability, and security.  
Another important question to consider is how to enable independent decision making in a network consisting of interconnected ISPs. These decisions are influenced by several factors, including the internal structure of the ISPs and the Border Gateway Protocol (BGP), which serves as a network protocol that computes and distributes the best path from each source to its destination.
###### Abstract  
Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.  
Soil Moisture Content (SMC); Global Navigation Satellite Systems Reflectometry (GNSS-R); CYGNSS; Soil Moisture Active Passive (SMAP); Fresnel reflection coefficients +
Footnote †: journal: Remote Sensing, 2020, 12, 122
## 2 Theoretical Discussion
### Key Dynamic Stability Evaluation Parameters Review
#### 2.1.2 Spring Coefficient and TSS Calculation Methods  
Track support stiffness is a measure of the track's stiffness in response to the rail-wheel contact load. According to the Korean Railway Code (KR-C 14080) [14], for sections of the railway track where the track modulus is expected to change, the change in the stiffness over the transition zones should be designed within the allowable value relative to the speed of the train (refer to Figure 2); additional factors to be taken into consideration are vibration acceleration of the vehicle body, wheel load fluctuation, and yield stress of rail fatigue and upper rail pressure [15].  
The stiffness of the track is derived from the comprehensive spring coefficient from the individual components that comprise the entire track structure. AREMA and the Korean Railway Track Specification (KRTS-VE) uses the spring coefficient (\({k_{s}}\)) calculation method, where \(k\) is derived by calculating the spring coefficient (stiffness) of the track components/layers to calculate the track support stiffness (TSS).  
\[{k_{\text{bss}}}=\frac{1}{\frac{1}{k_{p}}+\frac{1}{k_{r}}+\frac{1}{k_{sld}}+ \frac{1}{k_{sld}}+\frac{1}{k_{sg}}} \tag{2}\]  
where:  
\({k_{p}}\): the stiffness of the pad (kN/mm),  
\({k_{r}}\): the stiffness of the resilience (if applicable) (kN/mm),  
\({k_{sld}}\): the stiffness of the sleeper (if applicable) (kN/mm),  
\({k_{slab}}\): the stiffness of the ballast-less slab (kN/mm),  
\({k_{sg}}\): the stiffness of the subgrade (kN/mm),  
\({k_{bss}}\): the stiffness of the ballast-less slab track structure (kN/mm)  
\[{k_{t}}=\sqrt[4]{\frac{64 EI}{d^{3}}}{k_{s}^{3}} \tag{3}\]  
\(E\): modulus of elasticity (GPa),  
\(I\): moment of inertia (mm\({}^{4}\)),  
\(d\): distance of the sleepers (mm),  
\({k_{s}}\): stiffness of the track structure (kN/mm),  
\({k_{t}}\): track support stiffness (kN/mm).  
Figure 2: Allowable track modulus change depending on the train speed [8].  
Korea employs the KR-C 14030 standard for the dynamic stability performance of track structures; it was adopted from the AREMA and UIC codes. For the trackside measurement of TSS, [PERSON]'s law is implemented to calculate the track support stiffness to derive the immediate TSS value/condition of the track structure. Compliant with [PERSON]'s formula, the trackside measurement TSSs is derived using the variables of maximum dynamic wheel load during trackside measurement and the maximum rail displacement (deflection);  
\[K_{t}=\frac{Q_{dyn}}{\delta_{max}} \tag{4}\]  
where;  
\(K_{t}\): is the trackside measurement based track support stiffness (kN/mm);  
\(Q_{dyn}\): is the dynamic wheel load (maximum value derived from the trackside measurement) (kN);  
\(\delta_{max}\): is the rail vertical displacement (maximum value derived from the trackside measurement (mm);
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 2 Related Work
### HTTP-Based Adaptive Streaming (HAS) Method  
HTTP-based adaptive streaming (HAS) is a method that streams small pieces (chunks) of media to a server depending on the user's network status. Since the media file has various bit-rate types encoded, a bit rate can be selected depending on the network conditions. In addition, the chunks streaming method also makes it easy to select the media quality, even while watching a video [15]. However, HAS considers only the network status as a variable. That is, HAS does not consider \"users\" in the online streaming service, or their behavior, as variables (e.g., as in the OTT service).
## 4 Verification Experiment Results and Analysis
### HiLExperiment Configuration  
The vehicle mass was \(M=1104\) kg and the vehicle wheelbase was \(L=2.48\) m. One of the difficulties we overcame was poor performance under conditions where curvatures changed rapidly or speeds were high. Therefore, we take the lane change and return maneuvers as an item. As shown in Figure 6, the solid red line is the target trajectory. Considering the adaptability of the algorithm at different speeds, the vehicle should be set to a total of nine speeds between 10-90 km/h.  
The central processing unit of the trajectory tracking controller is an Intel Core i7-8700. The operating system is Ubuntu 16.04.4. The compiler uses release mode optimize program running efficiency. The main parameters of the NMPC tracker in the experiment are as follows: Discretization period \(T=0.2\) s, prediction time horizon \(n=25\), lateral deviation weight coefficient \(k_{1}=1\), yaw angle deviation weight coefficient \(k_{2}=500\), control input weighting coefficient \(k_{3}=1000\), rotation wheel angle constraint \(\delta_{max}=0.6\) rad, and rotation wheel angle control increment constraint \(\Delta\delta_{max}=0.04\) rad.
## 3 Results
### Simulations
#### 3.2.2 Ethylene  
We now consider ethylene, a small molecule where we can be confident that the simulations are converged, to investigate the effect of the number of trajectories included in the simulations. In the case of ethylene, full convergence requires \(>\)500 trajectories [49]. In Figure 5, we compare the modified molecular intensity for elastic scattering from ethylene for a small set of 20 trajectories, shown in Figure 5a, with a large set of 1000 trajectories shown in Figure 5b. Comparison of the two signals indicates that the subset of 20 trajectories underestimates the dispersion at longer times, with the full set of 1000 trajectories showing a distinct deterioration of the signal over time. Interestingly, the limited subset of 20 trajectories _is_ sufficient to capture the evolution of the dynamics qualitatively. In part, this reflects that the diffraction signal is dominated by the comparatively simple dynamics of the  
Figure 4: Results for \(\tilde{\text{K}}_{2}\), calculated using the reduced mass of \(\text{K}_{2}\) and the \(B\)-state potential of \(\text{D}_{2}\), excited to the electronic \(B\)-state by a 14.3 eV pulse with 20 fs duration. (**a**) The \(\tilde{\text{K}}_{2}\) vibrational wavepacket probability density, \(|v(R,t)|^{2}\), with \(R\) the internuclear distance in Bohr and \(t\) the time; (**b**) Contour plot of the corresponding modified (elastic) scattering intensity, \(M(q,t)\), with \(q\) the momentum transfer in \(\text{\AA}^{-1}\) and \(t\) the time. In both plots, the time \(t\) is given in units of the classic oscillation period \(T=335.8\) fs. The modified scattering intensity \(M(q,t)\) is defined in Equation (16), and we use the form factor for K in the denominator.  
C-C bond [75], which is well reproduced by the smaller subset of trajectories, but the observation is more general since we know from earlier studies that a small number of trajectories can do a good job of capturing the essence of a reaction path [79]. This observation is also commensurate with recent work on the ring-opening reaction of CHD, which found that the time-dependent diffraction pattern for the reaction could be reproduced accurately by a comparatively small number of trajectories representative of the dynamics [6]. Overall, this is good news for the analysis of ultrafast diffraction experiments because it suggests that a sensible analysis of experimental results does not require fully converged quantum molecular dynamics simulations (which are simply out of reach in many molecules of interest). However, it does raise another issue, which is the influence of the width parameters associated with trajectories, as discussed in the example of CHD next.
# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
## 2 Materials and Methods
### Training Images for Model Creation  
We developed the software for object detection with a deep learning technique via in-house MATLAB software; we used a deep learning-optimized machine with an Nvidia Quadro P5000 graphics card (Nvidia Corporation, Santa Clara, CA, USA), 8.9 Tera floating-point single-precision operations per second, 288 GB/s memory bandwidth, and 16 GB memory per board. We performed the image training as transfer learning by initial weight using You Only Look Once Version 2 (YOLOv2) [32], with the MATLAB deep learning Toolbox and Computer Vision System Toolbox. The training model hyperparameters were as follows: maximum training epochs--10; initial learning rate--0.00001; mini-batch size--96. We used stochastic gradient descent with momentum for optimization with an initial learning rate. We set the momentum and L2 regulation to 0.9 and 0.0001, respectively. We performed image training nine times based on the training subsets in Figure 2.
###### Abstract  
We suggest green rust (GR), one of layered nanoclays, as a potential oxygen scavenger. In order to achieve controlled oxygen scavenging ability, GRs were prepared with either sulfate or conducting polymer. X-ray diffraction (XRD) patterns showed that both GRs had hydrotalcite phase with slight differences in crystallinity upon anion type. X-ray adsorption spectra (XAS) indicated that the local structure of both GRs were similar regardless of the type of anion. On the other hand, zeta-potential values of GRs were different from each other according to the type of anion; GR with sulfate showed positive charge and GR with conducting polymer had slight negative charge due to the homogeneous hybridization. Scanning electron microscopy (SEM) also suggested that the hybridization of conducting polymer and GR was fairly homogenous without the formation of phase segregation or serious aggregation. According to the oxygen-scavenging activity test, GR with conducting polymer showed a retarded oxygen-scavenging rate compared with GR with sulfate due to protection and controlled oxidation-reduction by hybridized polymer. The current results suggested that the hybridization of nanoclay with conducting polymer could be utilized in long-term oxygen scavenging applications with a controlled oxidation-reduction reaction.  
green rust; conducting polymer; oxygen scavenging; retarding oxidation; food packaging Article