esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Influence of Time Delay in Signal Transmission on Synchronization between Two Coupled FitzHugh-Nagumo Neurons**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,* 1**  
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China; [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (Z.L.)  
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China  
Correspondence: [EMAIL_ADDRESS]  
Received: 7 March 2019; Accepted: 20 May 2019; Published: 27 May 2019  
**Featured Application: The paper provides a new method to consider the synchronization between two neurons, which can be used to analyze other coupled oscillators.**  
**Abstract:** In this paper, the energy method is employed to analytically investigate the influence of time delay in signal transmission on synchronization between two coupled FitzHugh-Nagumo (FHN) neurons. Unlike pre-existing methods that deal with synchronization problems, our major idea is to consider the change rate of the energy of the synchronization error system, since the original system's synchronization is equivalent to the disappearance of the energy of the error system. In rewriting the original coupled system in the corresponding energy coordinates based on the energy method, we find that the change rate of energy of the error system can be divided into two parts (periodic and non-periodic). The synchronization criterion for the original system can then be obtained by letting the non-periodic part of the change rate of the energy be less than zero. The correctness of the analysis is illustrated with numerical simulations. Our analytical results show that time delay in signal transmission has very significant effects on the synchronization between two FHN neurons. If the time delay in signal transmission is not taken into account in the two coupled FHN neurons, synchronous spikes cannot be achieved in the system for any given coupling strength. By adjusting the value of the time delay in signal transmission, the neural system can freely switch between neural rest and synchronous spikes. This means that time delay in signal transmission is crucial for the occurrence of synchronous spikes in the FHN neural system, which contributes to our understanding of the interaction between neurons. We analytically show the influence of the time delay on the synchronization between two FHN neurons, which was seldom considered by other researchers.  
**Keywords:** FHN neuron; synchronization; time delay; energy method
## 3 Research Questions  
As previously mentioned, the present study is an attempt to investigate the improvement in SRT by exploiting an active video game based on NUIs. To achieve this, additional issues need to be investigated, except for the improvement in SRT. The main issues to explore are:  
1. The effect of Tennis Attack on players' SRT during and after the gameplay. Here, in-game SRT is defined as the time of the scores that each player will achieve while playing the game and real-world SRT as the time of the scores that each player will achieve while taking the validated test after playing the game;
2. the effect of the game on different types of players based on their expertise or as gamers;
3. the way that players adapt to Tennis Attack.  
With this aim, the research questions are as follows:  
* RQ1: Does Tennis Attack improve the in-game reaction time of players?
* RQ2: Does Tennis Attack improve the real-world reaction time of players?
* RQ3: Is there any significant relationship between a player's expertise as an athlete and their improvement?
* RQ4: Is there any significant relationship between a player's gaming experience and their improvement?
* RQ5: Is Tennis Attack user friendly to all users?
## 5 Performance Evaluation
### Simulation Results
#### 5.2.3 Changes in Network Density vs. Network Lifetime  
The performance in terms of network lifetime with the same simulation setup as in Figure 5 is shown in Figure 8. The initial battery power used was the same as that of the AA battery, and the network lifetime was calculated in days. The lifetime value represents the lifetime of the first dead node in the network. A duty cycle of 0.5 was used. In addition, packet rates of 0.1 and 0.5 were considered (Figure 8a and Figure 8b, respectively). As shown in the figure, the network lifetime decreased with an increase in density. This is because a highly dense network produces more redundant packets and drains energy more quickly. However, the proposed protocol exhibited relatively better performance, and its network lifetime decreased slowly compared to the others. In high-density FCM-OR, the lifetime was improved by 24% and 27% under low-traffic and high-traffic conditions, respectively.  
Figure 8: Comparison of the network lifetime with varying non-uniform densities under different traffic loads.  
Figure 7: Comparison of the average number of forwarders per hop with varying non-uniform densities and duty cycles.  
A comparison of the network lifetime with varying node densities and duty cycles for single packet transmission from source to sink is shown in Figure 9. As indicated in Figure 9a,b, the proposed FCM-OR protocol exhibited a longer network lifetime than the ORW and ORR protocols for different duty cycles (0.6 and 0.8). Moreover, as the duty cycle increased, the node lifetime decreased slowly in our protocol compared with other protocols. FCM-OR could improve the lifetime of high-density networks by 5%.
###### Abstract
This regressor is insensitive to data normalization, enabling it to leverage waveform amplitude information during training. The model can predict local magnitudes with an average error close to zero and a standard deviation of approximately 0.2. [PERSON] [11] used a CNN model to predict three-component 20 Hz broadband waveforms within 50 s, incorporating the event distance, azimuth, depth, and amplitude. The final convolutional layer of this model has fewer nodes than the output classifications, effectively compressing and transmitting relevant input information, which helps reduce output noise. However, due to the use of long waveforms and limited data, real-time prediction is challenging, and the model is prone to overfitting during CNN training. [PERSON] [12] et al. combined a CNN with a Long Short-Term Memory network (LSTM) to frame earthquake magnitude prediction as a classification problem. By introducing new magnitude prediction information as supplementary content and leveraging the strengths of both convolutional and recurrent architectures, their CNN-LSTM model fully exploits the LSTM's ability to learn and extract temporal features, thereby improving the magnitude prediction accuracy. The attention mechanism selectively filters crucial information from large datasets, focusing computational resources on salient features while ignoring irrelevant data [13]. The Convolutional Block Attention Module (CBAM) [14; 15] is a lightweight, general-purpose attention module that can be seamlessly integrated into any CNN architecture and trained end to end with the underlying network. To address issues such as missing training samples, overfitting in traditional neural networks, and limitations in feature extraction by single-model approaches, this study proposes a transfer learning framework that transfers knowledge from related domains to target classification tasks. Specifically, we adopt a residual shrinkage network transfer learning method, pretraining a ResNet18 model [16] on the ImageNet dataset and augmenting it with an attention mechanism and soft thresholding modules to enhance the capture of effective feature information. The extracted feature vectors are then fed into a classifier for training. By transforming the prediction problem into a classification task and using seismic waveforms as input, this approach achieves improved prediction accuracy.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 2 Problem Description
### Problem Assumption  
To highlight the key points of the problem, combined with the actual situation, this paper makes the following assumptions in MSOOSP:  
* One satellite can only communicate with one neighbor at a time.
* The communication between two satellites is unidirectional, where one satellite serves as the information sender and the other as the information receiver.
* The paper does not consider the communication costs incurred due to pre-processed information broadcasting.
* The paper does not consider changes in satellite power and storage caused by factors other than mission observations.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 3 Hardware Structures for Nonlinear Operation
### Nonlinear Operation Based on Optical Modulator  
Another way to realize nonlinear structure is to use the optical modulator. The optical modulators that can realize nonlinearity are placed in the dotted line of Figure 7. The basic principle is that the light signal of the weighted sum is converted into a voltage by the photodetector, and then, applied to the optical modulator. This affects the transmission spectrum. The realization of the optical nonlinear function is by changing the transmittance of the optical signal through the modulator. The structures of optical modulators can be implemented by MZI modulators [38], electro-absorption modulators [39], and micro-ring resonator modulators [40], as shown in Figure 8. Different nonlinear functions can be realized by changing the bias voltage of the modulator.  
Figure 8: Optical modulator implementation diagram. (**a**) MZI modulator [38], (**b**) electro-absorption modulator [39], (**c**) micro-ring cavity modulator [40].  
In 2019, [PERSON] et al. realized the sigmoid nonlinear function based on the serial structure of MZI composed of two SOAs and a single SOA [41]. The schematic diagram is shown in Figure 9. This structure is mainly based on the cross-phase modulation effect and cross-gain modulation effect of SOA. When the input pulse width is very small, within the integration window, the arrangement of the input light pulse signal and the number of pulses will affect the pulse width of the output signal. SOA has a high nonlinear coefficient, and at the same time, has a gain effect on optical signals.
## 2 Calculation Methods of Surrounding Rock Pressure
### China's Specifications for Design of Highway Tunnels (SDHT)
#### 2.3.3 China's Specifications for Design of Highway Tunnels  
China's \"Specifications for Design of Highway Tunnels (SDHT) (2018) [27]\" is based on the statistics of tunnel collapse [20]. Due to the difference of section size between highway tunnels and railway tunnels, some parameters were revised when referring to the formula.  
Figure 4: Calculation diagram of surrounding rock pressure of shallow tunnels in the SDHT.  
The calculation formula of the load of loose surrounding rock in deep tunnel was as follows,  
\[q_{deep}=0.45\times 2^{s-1}\times\gamma\omega\left(kN/m^{2}\right) \tag{17}\]  
where \(S\) is the classification grade of surrounding rock in SDHT. The rock classification method combines qualitative and quantitative analyses, considering the rock strength, rock integrity, groundwater, initial stress, and joint surface. While a brief illustration is available in Appendix A, \(\gamma\) is rock density, \(\omega=1+i(B-5)\) is the influence coefficient of the width, \(B\) is tunnel width, and \(i\) represents variation of surrounding rock pressure with variation in \(B\). Herein, \(B=5\) m was adopted as a benchmark. When \(B<5\) m, consider \(i=0.2\), and when \(B>5\) m, consider \(i=0.1\).  
In accordance with SDHT, the cover depth threshold was as follows:  
\[H_{P}=(\;2\sim 2.5)h_{q} \tag{18}\]  
where \(H_{P}\) is the cover depth threshold and \(h_{q}\) is load-equivalent height. The calculation formula was as follows:  
\[h_{q}=\frac{q_{deep}}{\gamma}=0.45\times 2^{s-1}\times\omega\left(kN/m^{2}\right) \tag{19}\]  
Vertical pressure was calculated in two parts. When the cover depth h was greater than the cover depth threshold \(H_{P}\), it was determined as a deep tunnel, and the vertical pressure was calculated according to Equation (17). When the cover depth h was less than the cover depth threshold \(H_{P}\), it was determined as a shallow tunnel, and the vertical pressure was calculated according to Equation (16).
## 2 Suggested Topology and Its Operation
### Output Voltage and Frequency Control  
The regulation in the load voltage and frequency may be ensured with the help of the zero, inverting, and non-inverting states of the proposed topology. Various techniques to regulate the output voltage are discussed below.
## 4 Experimental Results
### Optical Comparison Operation
In both cases, it is shown that constellations similar to the comparator codes, which were \"11 10 10 00\" and \"00 10 01 00\", were located at near the base constellation. Similarly, constellations different from the comparator codes were located away from the base constellation. From the results above, all constellations were at a position in conformity with the Hamming distance based on each comparator code: \"11 10 10 00\"  
Figure 5: Constellations of the 8-bit optical comparator: **(a)** “00 00 00 00” comparator. **(b)** “11 00 11 00” comparator. In these figures, all constellations are located in conformity with the \(Hamming\) distance based on the comparator codes “00 00 00 00” and “11 00 11 00”, respectively. The inconsistent bits with the comparator code are underlined in each comparator. Figure 6: Constellations of the 8-bit optical comparator: **(a)** “11 10 10 00” comparator. **(b)** “00 10 01 00” comparator. In these figures, all constellations are located in conformity with the \(Hamming\) distance based on the comparator codes “11 10 10 00” and “00 10 01 00”, respectively. The inconsistent bits with the comparator code are underlined in each comparator. and \"00 10 01 00\". Therefore, it is shown that the author experimentally demonstrated the feasibility of the optical comparison operation for 8-bit code at 10 Gbaud.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Other Novel Deep Space Probe Navigation Methods  
StarNAV is a novel celestial navigation method that utilizes relativistic effects, which mostly provides the velocity information of the spacecraft. Ref. [16] proposes a star angle modified with relativistic effects (SAMRE)/StarNAV integrated navigation method. The measurement model of SAMRE is established by considering relativistic effects in the measurement model of star angle. Ref. [17] proposes a time dilation-modified TD-TOA/StarNAV (TDM-TDTOA/StarNAV) integrated navigation method. The effect of time dilation on navigation accuracy is analyzed and compensated in the measurement model of time differential time of arrival (TDTOA). Ref. [18] explores a new navigation method using multi-path solar panel-reflected solar oscillations. Solar disk velocity difference is another novel celestial navigation measurement, which can be obtained by four spectrometers installed on the four corners of a quadrangular pyramid [19]. Ref. [20] proposes an augmented-state Sun direction/solar disk velocity difference integrated navigation, which greatly suppresses the effect of spectrometer installation error on navigation accuracy.
## 4 Types of Catalysts Used in Transesterification
### Biocatalysts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{**Feedstock Oil**} & \multirow{2}{*}{\begin{tabular}{c} **Catalyst** \\ **Conc.** \\ **(wt. \%)** \\ \end{tabular} } & \begin{tabular}{c} **Oil to** \\ **Methanol** \\ **Molar** \\ **Ratio** \\ \end{tabular} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{\
\begin{tabular}{c} **Temp.** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Yield of** \\ **Bodiesel** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**Reference**} \\  & & & & & & & \\ \hline Palm Kernel shell/CaO & \multirow{2}{*}{Sunflower} & \multirow{2}{*}{3} & 1:9 & 4 h & 65 & 99.8 & [126] \\  & based biochar & & & & & & \\ _Musa balbisiana_ & Waste Cooking Oil & 2 & 1:6 & 3 h & 60 & 100 & [116] \\ Snail shell & Soybean & 3 & 1:6 & 7 h & 28 & 98.0 & [127] \\ _Heteropanax fragrans_ & Jatropha & 7 & 1:12 & 1.11.9 & 65 & 97.7 & [128] \\ Rice straw waste & Waste cooking oil & 3.5 & 1:15 & 2.5 h & 65 & 97.3 & [129] \\ Morringa leaves & Soybean & 6 & 1:6 & 2 h & 65 & 86.7 & [130] \\ Sheep bone & Canola & 5 & 1:12 & 5 h & 60 & 95.1 & [131] \\ _Sesamammit indicum_ waste & Sunflower & 7 & 1:12 & 0.67 h & 65 & 98.9 & [132] \\ _Brassica nigra_ waste & Soybean & 7 & 1:12 & 0.42 h & 65 & 98.7 & [133] \\ Egg shell & Rubber Seed & 5 & 1:9 & 4 h & 65 & 97.8 & [134] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Plants and Animals based green catalysts for biodiesel production. extracellular lipases. Various factors such as selectivity, efficiency, stability and recyclability make them best choice to carry out transesterification. The inactivation of biocatalysts due to methanol, acyl acceptor for transesterification that limits the mass of alcohol during the reaction. This irreversible inactivation is due to aggregation as solvent molecules generally bind to protein. Non-covalent binding of solvent molecules to the enzyme result in its competitive inhibition [136]. Hence, the mechanism must be understood in order to design various strategies to solve these problems. A way to sort out this issue is the immobilization of enzymes.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 2 Demand Side Management
### Implementation of the Proposed Algorithm  
The load profile varies during the day, and the best load shape can be achieved by executing an adaptive algorithm that chooses appropriate DSM techniques when applicable. The commutations between CVR and DLC occur among different consumption periods, where, generally, DLC is efficient to operate in high power consumption periods while CVR gives better controllability and voltage reduction when the consumption is minimal. The proposed combination between CVR and DLC was tested based on two methods: the first method consists of running the CVR and DLC proposed algorithm during the entire day, while the second method implements CVR during low peak demand periods and DLC during high peak demand periods.  
Figure 3: DLC algorithm flowchart.  
The CVR and DLC methods of DSM are implemented through multiple actions: total load curtailment and partial load curtailment. The goal of the combined algorithm is to reduce the power consumed by reducing both voltage and current in an efficient way. The flowchart in Figure 4 starts the aforementioned algorithms simultaneously by measuring the consumed power and system's bus voltage; the effectiveness of each algorithm depends on a timetable which schedules the algorithm over the entire day or throughout specified periods of the day. Afterwards, the necessary action is taken either by changing the tap of the transformer or curtailing part of the load.
# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 3 Multi-feature Structure Fusion Based on Linear Discriminant Analysis
### Multi-feature Structure Fusion Based on Linear Discriminant Analysis
#### 3.3.1 Weight Matrix Construction of the Same Kind Feature  
Firstly, we extract the selected features in training image samples, such as the pool5 layer feature of ResNet-152 and the relu5-4 layer feature of VGG-19 in Figure 2. Suppose the number of training samples is \(m\); the class number of samples is \(c\); the feature dimensions of ResNet-152 and VGG-19 are \(n_{1}\) and \(n_{2}\) respectively. We define feature matrix \(X_{1}=[x^{1}{}_{1},x^{1}{}_{2},\ldots,x^{1}{}_{m}]\in\mathbb{R}^{n_{1}}\) and \(X_{2}=[x^{2}{}_{1},x^{2}{}_{2},\ldots,x^{2}{}_{m}]\in\mathbb{R}^{n_{2}}\). Then, we calculate the respective class feature matrices \(\overline{X}_{1}=[\overline{x}^{1},\overline{x}^{1}{}_{2},\ldots,\overline{x }^{1}{}_{c}]\in\mathbb{R}^{n_{1}}\) and \(\overline{X}_{2}=[\overline{x}^{2}{}_{1},\overline{x}^{2}{}_{2},\ldots, \overline{x}^{2}{}_{c}]\in\mathbb{R}^{n_{2}}\), here \(\overline{x}^{q}_{p}=\frac{1}{c_{p}}\sum\limits_{i=1}^{c_{p}}x^{q}_{i}\), \(p=\{1,2,\cdots c\}\), \(q=\{1,2\}\) and \(c_{p}\) is the sample of each class. Finally, construct weight matrix \(W_{1}=[w^{1}{}_{i,i}]_{cc}\) and \(W_{2}=[w^{2}{}_{i,i}]_{cc}\) for each kind of feature, where the element of matrix \(W_{1}\) and \(W_{2}\) are as follows:  
\[w^{1}{}_{i,j}=\left\{\begin{array}{ll}e^{-d(\overline{x}^{1}{}_{i}\overline{ x}^{1}{}_{j})/t},&\overline{x}^{1}{}_{i}\text{ and }\overline{x}^{1}{}_{j}\text{ are }k\text{ neighbor}\\ 0\text{,}&\text{else}\end{array}\right. \tag{12}\]  
\[w^{2}{}_{i,j}=\left\{\begin{array}{ll}e^{-d(\overline{x}^{2}{}_{i}\overline{ x}^{2}{}_{j})/t},&\overline{x}^{2}{}_{i}\text{ and }\overline{x}^{2}{}_{j}\text{ are }k\text{ neighbor}\\ 0\text{,}&\text{else}\end{array}\right. \tag{13}\]  
In the Formulas (12) and (13), \(d(a,b)\) represents the Euclidean distance of vectors \(a\) and \(b\), and \(t\) is selected to be 0.4. The weight matrix \(W_{1}\) and \(W_{2}\) reflect the relationship between each class center and others. In other words, these weight matrixes are the description of the relationship between classes.
## 3 Materials and Methods
### Assessment of Anti-Aging Properties  
#### 3.8.1 Determination of Anti-Elastase Activity  
The evaluation of the anti-aging properties of CBD- and TER-loaded hydrogels was performed using a fluorimetric kit (Abcam, ab118971, Cambridge, MA, USA), assessing the possibility of inhibiting the activity of the enzyme neutrophilic elastase (NE). The analyses were performed based on the instructions provided by the manufacturer according to the procedure previously described by [PERSON] et al. (2018). Fluorometric measurements of elastase activity were performed in a 96-well plate with black walls and a clear bottom. As part of the analysis, analyses were carried out for extracts from all four tested hydrogels at concentrations of 0.1, 1.0, and 5.0%. Initially, NE enzyme solutions, NE substrate, and inhibitor control (succinyl-alanyl-alanyl-prolyl-valine chloromethyl ketone; SPCK) were prepared according to the manufacturer's instructions. The NE solution was then added to all reaction wells. Next, test solutions of hydrogel extracts, inhibitor control, and enzyme control (assay buffer) were added to each well and mixed in the dark at 37.0 \({}^{\circ}\)C for 5 min. Meanwhile, a reaction mixture was prepared by mixing an assay buffer and NE substrate. The prepared solution was added to each well, and the fluorescence was immediately measured at excitation wavelength \(\lambda\) = 400 nm and emission \(\lambda\) = 505 nm using a microplate reader (FilterMax F5, Thermo Fisher Scientific, Waltham, MA, USA). Measurements were performed in the kinetic mode (for 30 min at 37.0 \({}^{\circ}\)C). Two independent experiments were performed in which all samples were analyzed in duplicate. The influence of the tested hydrogels on NE activity was calculated using the following equation:  
\[\%\;\text{relative activity}=\frac{\Delta\text{RFU test inhibitor}}{\Delta \text{RFU enzyme control}}\times 100\% \tag{12}\]