esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Decoder with DeMBFF  
The decoder with DeMBFF is defined very similarly, but now its task is to upsample and refine features to generate high-quality output. Each encoder consists of several MBFFs from the former layers and one denoising block. In addition, the feature of the encoder is passed via a skip connection. The process is illustrated in Figure 5, which is formulated as follows:  
\[D_{t}^{K}=\left\{\begin{array}{ll}D^{K}&\text{if }t=0,\\ MBF_{de}(D_{t-1}^{K},D^{K-l})&\text{if }0<t\leq K-1,\\ DB_{de}(D_{t-1}^{K},E^{n-K+1})&\text{if }t=K,\end{array}\right. \tag{10}\]  
where \(D^{K}\) represents the input of the current layer, \(FS=\{D^{1},\dots,D^{K-1}\}\) represents the multiscale features to be fused in the current decoder, and \(D_{t}^{K}\) represents the features after multiscale back-projection fusion. \(E^{n-K+1}\) indicates the feature of the encoder from the skip connection, where \(n\) is the number of encoders. Each decoder fuses features \(t\) times, including features from all prior decoders and one corresponding encoder. Therefore, \(t\) ranges from 0 to \(K\). The pseudo-code of the feature fusion process for a decoder with DeMBFF is shown in Algorithm 3.  
```
Input: Feature \(D^{K}\), Fusion features set \(\{D^{1},D^{2},...D^{K-1},E^{n-K+1}\}\), Fusion number \(K\) Output: The fused feature \(D_{t}^{K}\)
1 Assignment \(D_{0}^{K}\) with prior feature: \(D_{0}^{K}\gets D^{K}\)foreach\(t\in\{1,2,\dots,K-1\}\)do
2 Assignment \(D_{t}\): \(D_{t}\gets D_{t-1}^{K}\)
3 Find the fusion feature \(D^{K-1-t}\) in set \(\{D^{1},D^{2},\dots D^{K-2}\}\)
4 Calculate deconvolution and convolution times: \(Size_{in}\gets size(D^{K-1})\) \(Size_{out}\gets size(D^{K-1-t})\) \(times=(Size_{out}/\textit{Size}_{in})//2\) Downsampling \(D_{t}\) with multiple convolutions: \(D_{t}\gets Con_{times}(D_{t})\) Calculate the differences between \(D^{K-1-t}\) and \(D_{t}\): \(e_{t}\gets Diff(D_{t},D^{K-1-t})\)
5 Upsampling the differences \(e_{t}\) with multiple deconvolutions: \(e_{t}\gets Decont_{times}(e_{t})\) Update the back-projection feature: \(D_{t}^{K}=D_{t-1}^{K}+e_{t}\)
6 end for
7\(t=t+1\) Calculate denoising feature with Equation (8): \(D_{t}^{K}\gets DB_{de}(D_{t-1}^{K},E^{n-K+1})\)
```  
**Algorithm 3**Feature fusion process for decoder with DeMBFF.
## 4 Implementation and Comparison
### Performance Comparison for the Antilogarithmic Function  
In this part, we perform three sets of experiments with TSMC 90-nm CMOS technology which are mentioned in [29]. The results are shown in Table 6. The first set of the experiments is presented as an example in Section 3, where we set iw = 27, qw = 14, kw = 2 and MAE\({}_{\text{def}}\) =1.29 \(\times\) 10\({}^{-3}\). Compared to [29], the number of segments, area, delay, power and MAE are reduced by 6, 6.26%, 3.70%, 9.49% and 3.10%; while when we set iw = 14, qw = 14, kw = 2 and MAE\({}_{\text{def}}\) =1.24 \(\times\) 10\({}^{-3}\), the reduced results are 5, 12.13%, 1.64%, 15.2% and 0.81%. In the first set of experiments, the data in [21] also serve as a comparison to prove the advancement of our method. The delays of these two sets of experiments are both the sum delay of 1 comparator, 1 multiplexer and 2 adders. The differences are the width of the comparators and the number of y-intercept inputs.  
In the third set of experiments, the fractional bits in the input and output are different, which are 10 and 12, respectively; furthermore, qw, kw and MAE\({}_{\text{def}}\) are 12, 3, and 4.79 \(\times\) 10\({}^{-4}\). In this case, the design reduces the number of segments, area, delay, power and MAE, and the number and percentages are 15, 2.41%, 0.64%, 19.97% and 0%, separately. The MAE value does not get smaller on account of the reason that the MAE\({}_{\text{def}}\) is close to the ideal MAE. If we want to get a more accurate MAE value, we can increase the number of iterations. Obviously, although the number of segments is much smaller, the reduction in area and delay is not much. We will analyze the reason below.  
Figure 8 is the hardware architecture comparison of the third sets of experiments, in which (a) represents the architecture in ML-PLAC, and where (b) represents the architecture in OML-PLAC. Compared to ML-PLAC, there are fewer comparators and smaller y-intercept multiplexer in OML-PLAC hardware architecture. However, the number of the multiplexer inputs in the SAA part is higher. Therefore, the overall performance improvement of the optimized architecture is not very large. The critical path of OML-PLAC is even longer when the logic gate size is the same. However, when we use DC for optimization, we can gain some timing advantages at the cost of area. As a result, the final experimental results can achieve a small improvement in all indicators.  
Figure 8: The hardware architectures in ML-PLAC (**a**) and OML-PLAC; (**b**) of the third sets of experiments in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Method** & **No.S** & **IO.F** & **NO.M** & **NO.A** & **qw** & **kw** & **New** & **Node** & **Fre** & **Area** & **Delay** & **Power** & **Energy** & **MAE\({}_{\text{Ref}}\)** & **MAE** \\ \hline This paper & 18 & 27/27 & 3 & 2 & 14 & 2 & 90 & 0.
## 4 Numerical Evaluations  
To further understand our experimental results, the radio propagation characteristics between an access UAV and a terminal using the mmWave band are investigated via numerical evaluations in a virtual environment imitating the experimental one. In wireless communications, propagation attenuation due to distance and coverage effects due to antenna directivity is essential for the link budget. That is why optimizing the position (altitude and horizontal location) of access UAVs is required, and it is important to understand the distance and coverage characteristics of our access UAVs. Furthermore, it is also significant to understand how inter-system interference via ground reflection affects our system performance in scenarios with multiple UAVs. Hence, distance characteristics, coverage characteristics, and ground-reflected wave characteristics will be numerically investigated in this section to supplement our experimental results.
# Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
## 3 Control Algorithm
### GA F-PID Controller
#### 3.3.2 Controller GA Optimization Algorithm  
The genetic algorithm achieves optimization through operations such as selection, crossover, and mutation, seeking the optimal solution with the maximum fitness value in the population. The parameter tuning process is shown in Figure 8, and the parameter settings are detailed in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Explanation** & **Parameters** & **Explanation** \\ \hline Encoding Method & Binary Encoding & Mutation Function & Constrained Adaptive Mutation \\ \hline Initial Population & Randomly Generate Within & & \\  & Specified Bounds & Crossover Probability & 0.9 \\ \hline Population Size & 30 & Mutation Function & 0.1 \\ \hline Selection Function & Random Uniform Selection & Maximum Evolution & 100 \\ \hline Crossover Function & Diversified Crossover & Stopping Generation & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Genetic algorithm parameters.  
The fitness function for this genetic algorithm optimization was set as the reciprocal of the objective function, as shown in Equation (7). The representation of control effectiveness (suspension performance) improves with increasing fitness value.  
\[f=\frac{1}{Q(x)}=\frac{1}{\frac{1}{I_{A}}+\frac{I_{2}}{J_{S}}+\frac{I_{3}}{J_{D}}} \tag{7}\]  
When using genetic algorithms for controller parameter optimization, the fuzzy PID controller model was invoked, and the suspension ride comfort index was input into the optimization main program of the GA. Genetic algorithms utilize fitness functions to control the program's operation and determine termination conditions based on the maximum number of iterations. When the genetic algorithm program reaches a specific number of iterations, it will automatically terminate and output the optimal solution. After 100 iterations, the curve of fitness function variation was obtained, as shown in Figure 9.  
Figure 8: The Basic Program of Genetic Algorithm.  
Figure 9: Fitness curve.  
From Figure 9 it can be observed that starting from generation 38, the fitness value tends to stabilize at 2.2441. At this point, the optimal variables are determined to be (\(K_{p}\), \(K_{i}\), \(K_{d}\)) = (10, 6, 1.5). A simulation analysis of the air suspension system was conducted in Simulink using the vehicle model parameters as listed in Table 1.
# Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas
## 4 Results
### Modeling Temporal Aggregated Crash Data  
The dependent variable for the modeling temporal aggregated crash data is the total number of pedestrian crashes over the whole period (2009-2015) in a given street. The model developed by using Zero-Truncated Negative Binomial Distribution (ZTNB) is presented in Table 3. The variables related to the risk exposure LnVveh, LnSPV, and LnPVQ were not significant. For this reason, the model is the same in all cases (Model 0).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Parameter**} & \multirow{2}{*}{**S.E.**} & \multirow{2}{*}{\(p\)**-Value**} \\  & & & \(\beta\)**j** & \\ \hline Built & ROAD\_L & 0.0012 & 0.0002 & \textless{}0.0001 \\ environment & PARKS & \(-\)0.3418 & 0.151 & 0.0236 \\  & NET\_C & 0.5003 & 0.2313 & 0.0305 \\ Pedestrian & DIST & 0.0035 & 0.0005 & \textless{}0.0001 \\ infrastructure & SLOPE & 9.5943 & 2.3012 & \textless{}0.0001 \\ Road & WIDTH\_W & 0.3525 & 0.1646 & 0.0323 \\ infrastructure & DIRECTIONS & \(-\)0.4972 & 0.2211 & 0.0245 \\  & R\_MED & \(-\)0.3313 & 0.1665 & 0.0466 \\ Intercept (\(\beta_{0}\)) & \(-\)6.8345 & 0.7629 & \textless{}0.0001 \\ AIC & & & 238.3586 & \\ MAD & & & 2.8962 & \\ RMSE & & & 3.8673 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ZTNB model—aggregated crash data (Model 0).  
In the category of pedestrian infrastructure, the variables SLOPE and DIST indicate that they contribute to the increase in the frequency of pedestrian crashes. The steeper road incline may be related to higher speeds practiced by drivers. However, this variable may also be related to the distance between crosswalks because steeper streets, or far back crosswalks, can lead to a higher probability that pedestrians will not cross the street at crosswalks and will choose the fastest and easiest way to cross the street.  
The result of the variable NET_C is opposite to what would be expected. The continuity of the pedestrian infrastructure should help to reduce the frequency of crashes due to minor conflicts between pedestrians and vehicular traffic. On the contrary, this result may indicate that lack of good walking conditions may lead to safer behaviors.  
Lastly, within the group of road infrastructure, two variables had a positive effect on the safety of pedestrians: DIRECTIONS and R_MED. These variables could help to reduce the estimated number of pedestrian crashes, first, by the fact that in one-way streets the task of observing vehicles is much easier for pedestrians and there may be no vehicle masks that move in another direction. Second, medians serve as a refuge for pedestrians when they cross the streets as they can do it in two different stages.  
Otherwise, the carriageway width indicates that it contributes to the increase of the frequency of crashes with pedestrians, which can be justified by the rise in the time necessary for pedestrians to cross the road and sense of comfort and safety felt by drivers, leading them to the practice higher speeds.
## 4 Proposed PDM-Based UWOC Link Description
The low-pass filter effectively attenuates frequencies above 750 MHz, ensuring that the receiver primarily processes the desired low-frequency signal components and mitigates the impact of noise on the system's performance. The BER tester is a critical instrument used to measure the bit error rate for each channel. In our proposed PDM-UWOC system, since we are utilizing two polarization states, we measure the BER separately for each polarization channel. The BER tester compares the received data to the transmitted data and determines the ratio of erroneous bits to the total number of bits transmitted. This measurement allows us to quantitatively evaluate the system's performance and assess its error-correcting capabilities. The additional simulation parameters are described in Table 1.
## 3 Image Processing
### Defect Image
#### 3.1.2 Segmentation and Normalization  
After locating defects, defect regions need to be segmented along the axis of the rope. In this paper, the projection method was utilized to obtain positional information of two dimensions. First, the binary image was projected to transverse with the sum and let the curve binary, then the derivative of this curve was calculated, with the axial starting position of the defect shown with 1 and the end shown with \(-1\). To acquire accurate positions of defects, we calculated the maximum and minimum in the target region, the center of the two values was considered the new central point, and we again extracted defect images 18 points long. After processing all down-sampling gray images, each defect UME defect had a nine-frame picture.  
To guarantee that all the centers had similar position, the center of the image needed to be normalized. On the basis of the first frame image, its central coordinate was \((9,j_{c})\); when \(j_{c}\) was less than 9, the image needed to be processed as follows:  
\[\left\{\begin{array}{ll}g(i,j+N/2-j_{c})=f(i,j),&0\leq j\leq N-j_{c}-1\\ g(i,j-N+j_{c})=f(i,j),&N-j_{c}\leq j\leq N-1\end{array}\right., \tag{6}\]  
when \(j_{c}\) was more than 9, the image needed to be processed as follows:  
\[\left\{\begin{array}{ll}g(i,j+j_{c}-N/2)=f(i,j),&0\leq j\leq j_{c}-N/2\\ g(i,j-j_{c}+N/2)=f(i,j),&j_{c}-N/2<j\leq N-1\end{array}\right., \tag{7}\]  
and when \(j_{c}\) was 9, there was no transformation, where \(f(i,j)\) is the image before being transformed and \(g(i,j)\) is the normalized image. Taking the wire rope from Section 2.2 as an example, the primary resolution MFL image and down-sampling images are shown in Figure 11, and the contrast of all images was increased for defect visualization.  
Figure 10: Raw down-sampling gray image (**top**); sharpened defect image (**middle**); and defect location image (**bottom**). In the first location were two defects along the circumference with one and two broken wires.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 1 Introduction
For managing robots with unknown parameters and dead zones, [PERSON] [21] offered a deep-reinforcement-learning system. Three components make up the algorithm: a state network that estimates information about the robot manipulator's state; a proposed critic network that assesses the effectiveness of the network; and an action network that learns a method for enhancing the performance metrics. [PERSON] [22] achieved tracking control over a manipulator in a continuous action space, but the algorithm's learning process was unstable and was heavily influenced by even minor changes to the hyperparameters. There is currently no deep-reinforcement-learning system that can reliably and continuously control the manipulator for route-tracking operations, according to the research findings. To address the aforementioned issues, this paper proposes a reinforcement-learning method for multi-DOF manipulator-path tracking, which converts the tracking-accuracy requirements and energy constraints into cumulative rewards obtained by the control strategy to ensure the stability and control accuracy of the tracking trajectory. The entropy of the policy is used as an auxiliary gain of the agent and introduced into the training process of the control strategy, thereby increasing the robustness of the path tracking. The method has good results in manipulator-path tracking, which not only avoids the process of finding the inverse kinematic solution, but also does not require a dynamics model and can ensure control over the tracking of the manipulator in continuous space. The remainder of this essay is structured as follows. The theoretical foundation of the algorithms in this paper, as well as the specific algorithmic applications and simulated experimental settings corresponding to the three manipulators, are presented in Section 2 after an introduction to the theoretical knowledge of deep-reinforcement-learning algorithms. The findings of the three manipulators' simulation experiments are mostly shown in Section 3. The simulation results are analyzed in Section 4, along with the algorithm used in this paper in comparison to other algorithms. The work is concluded in Section 5, which also identifies potential future research areas.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 5 Performance Evaluations
### Simulation Parameters  
The parameters used in this simulation relate to the mobility model, speed, number of mobile producers, topologies, and NDN set-up. Two different topologies, Abilene and the 4 \(\times\) 4 grid, are used for two different scenarios. The speed of the mobile producer ranges between 0 m/s and 100 m/s, depending on the scenario set-up. A summary of the parameters with their respective values is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Notation** & **Parameters** & **Value** \\ \hline \(a\) & Transmission latency btw client and router & 1 \\ \(b\) & Transmission latency between routers & 5 \\ _Ldw_ & Wired link delay & 2 ms \\ _Ldw_ & Wireless link delay & 10 ms \\ _Qd_ & Queuing delay & 5 ms \\ _Bw_ & Wired bandwidth & 100 mbps \\ _Buul_ & Wireless bandwidth & 11 mbps \\ _Sname_ & Size of the signaling packet & +16 bytes \\ _Sdata_ & Size of data packets & 2000 bytes \\ _Suint_ & Size of Interest packet & 40 bytes \\ \(q\) & The probability of link failure & \(0\leq q\leq 1\) \\ _\(\gamma\)_ & Interest arrival rate & 0–10 \\ _\(\mu\)_ & Subnet crossing rate & 0–100\% \\ \hline \end{tabular}
\end{table}
Table 1: Numerical Analysis Parameters and Values.
## 5 Biological Activities of Rice Phytolexins
### Bioactivities of Phenolic Phytoalexins
Interestingly, Sakurantin was methylated to produce skurantent-4\({}^{\prime}\)-methyl ether, which was found to be inactive against both _Leishman_ spp. and _T. cruzi_, suggesting that the presence of a hydroxyl group at C-4\({}^{\prime}\) together with a methoxyl group at C-7 is required for the antiparasitic activity. Further drug design studies targeting skuranten derivatives could contribute to the development of promising therapeutic agents for leishmaniasis and Chagas' disease. In a study conducted by [PERSON]  
**Funding:** This research received no external funding. **Data Availability Statement:** Not applicable. **Acknowledgments:** Special thanks go to [PERSON], who designed the graphical abstract. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
3 LN0  
Structure of compound 5c-S bound at the active site of COX-2  
4 HZ5  
Pyrrolopyrimidine inhibitors of DNA gyrase B and topoisomerase iv  
4 KIK  
Human IkB kinase beta  
5-LOX  
5-Lipoxygenase  
ACC  
Acetyl-CoA carboxylase  
ADP  
Adenosine diphosphate  
AF-2  
Furylfuramide  
BenSer  
\(N\)-Benzoylerserotonin  
BenTry  
\(N\)-Benzoyltryptamine  
BenTyr  
\(N\)-Benzoyltytramine  
BGC  
Biosynthetic gene cluster  
CDP  
Copalyl diphosphate  
CHI  
Chalcone isomerase  
CHS  
Chalcone synthase  
CinSer  
\(N\)-_trans_-Cinnamoylserotonin  
Cin-Try  
\(N\)-_trans_-Cinnamoyltryptamine  
Cin-Tyr  
\(N\)-_trans_-Cinnamoyltytramine  
CM  
Chorismate mutase  
CouSer  
\(p\)-Coumarolyserotonin  
CS  
Chorismate synthase  
cv. Cultivar  
CYPs  
Cytochrome P450 enzymes  
DAHP  
3-Deoxy-D-arabinoheptulosonate 7-phosphate  
DAHPS  
3-Deoxy-D-arabinoheptulosonate 7-phosphate synthase  
DHQDT/SDH  
3-Dehydroquinate dehydratase/shikimate dehydrogenase  
DHQS  
3-Dehydroquinate synthase  
diTrp  
Diterpene-synthase  
DMAPP  
Dimethylallyl diphosphate  
EC\({}_{50}\)  
Half maximal effective concentration  
_ent_-CPP  
(_5R,9S,10S_)-Copalyl diphosphate  
ER  
Endoplasmic reticulum  
FerAgm  
\(N\)-Feruloylagmatine  
FerPut  
\(N\)-Feruloylputrescine  
FerSer  
\(N\)-Feruloylserotonin  
FerTry  
\(N\)-Feruloyltryptamine  
FPP  
Farnesyl diphosphate  
GAs  
Gibberellins  
GFP  
Green fluorescent protein  
GGPP  
Geranylgeranyl diphosphate  
GGPPS  
Geranyl digraphosphate  
GFP  
Geranyl diphosphate  
HDR  
4-Hydroxy-3-methylbut-2-enyl diphosphate reductase  
HEL  
Hen egg-white lysozyme  
HBMPP  
4-Hydroxy-3-methylbut-2-enyl diphosphate  
IC50  
Half maximal inhibitory concentration  
IDS  
Isoprenyl diphosphate synthase  
IPP  
Isopentenyl diphosphate  
IPPI  
Isopentenyl diphosphate isomerase