esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 1 Introduction
It is difficult to distinguish which word \"nice\" or \"terrible\" is related to the target word \"beef\" or \"juice\" if only the traditional attention-based method is applied. However, if a person has a good knowledge of grammar, she can easily realize that \"nice\" is the adjective modifier of \"beef\", and \"terrible\" is the modifier of \"juice\". Since the structure of the syntactic dependency tree is similar to the graph structure and the graph convolutional network (GCN) [17] is an effective convolutional neural network that is able to directly operate on graphs, this paper proposes an improved GCN to better extract and integrate the syntactic information displayed in the syntactic dependency tree of the sentences. Overall, semantic information and syntactic information are both crucial for determining the sentiment polarity of a specific target and this paper tries to embed the abundant semantic information and syntactic information into the word representation and specific aspect representation. Figure 1: An example of a syntactic dependency parse tree. NN: noun. DT: determiner. IN: preposition or conjunction, subordinating. JJ: adjective or numeral, ordinal. nsubj: nominal subject. The main contributions of this paper are as follows:  
* This paper proposes the novel attentional-encoding-based graph convolutional network (AEGCN) model, which leverages the syntactic structure of a sentence and utilizes multi-head self-attention combined with LSTM to capture context features and specific target features concurrently. The AEGCN model combines semantic information and syntactic information to predict the sentiment polarity of a targeted aspect. * Syntactic information has not attracted enough attention in many related studies. This paper builds an improved graph convolutional network with point-wise convolution over the dependency tree of a sentence to extract syntactic information and utilize a multi-head self-attention to obtain the syntactic information encoding. * This paper evaluates the proposed method on five datasets. Experiments show that the AEGCN achieved competitive performance over the state-of-the-art approaches. This paper applies pre-trained BERT The rest of this paper is organized as follows. Section 2 gives a brief review of the related work. Section 3 describes the AEGCN model. Section 4 shows the experimental results. Finally, Section 5 concludes the paper.
###### Abstract  
A novel cellulase was characterized from a newly isolated marine bacterium, strain J9-3. Phylogenetic analysis based on the 16S rRNA gene revealed that strain J9-3 belonged to the genus _Celluophaga_, and thus, it was named _Celluophaga_ sp. J9-3. An extracellular cellulase was purified from cell-free culture broth of J9-3 cultured in Marine Broth containing 0.2% carboxymethylcellulose. Sodium dodecyl sulfate-polyacrylamideide gel electrophoresis of the protein revealed a single band with an apparent molecular weight (Mw) of 35 kDa. Based on the NH\({}_{2}\)-terminal amino acid sequence (N-N-T-E-Q-T-V-V-D-A-Y-G), the gene (named _celA\({}_{93}\)_) encoding the protein was identified from J9-3 genomic sequencing data. _CelA\({}_{93}\)_ was expected to be translated into a premature protein (359 amino acids) and then processed to a mature protein (307 amino acids, Mw = 34,951 Da), which is consistent with our results. CelA\({}_{93}\) had high homology with many uncharacterized putative glycosyl hydrolases of the genus _Celluophaga_ and it was highly specific for carboxymethylcellulose and celloligosaccharides under optimum conditions (pH 7.5, 60 \({}^{\circ}\)C). Co\({}^{2+}\) completely recovered CelA\({}_{93}\) activity that was severely inhibited by ethylenediaminetetraacetic acid (EDTAA), indicating that CelA\({}_{93}\) required Co\({}^{2+}\) as a cofactor. Thus, CelA\({}_{93}\) is a Co\({}^{2+}\)-dependent endo-\(\beta\)-1,4-glucanase that can hydrolyze carboxymethylcellulose and celloligosaccharides into cellobiose at a relatively high temperature.  
**Keywords:**_Celluophaga_ sp. J9-3; endo-\(\beta\)-1,4-glucanase; carboxymethylcellulose; cellobiose; Co\({}^{2+}\)-dependent +
Footnote †: journal: Applied Physics Letters
# Reactive Molecular Dynamics Simulations of Polystyrene Pyrolysis
## 2 Results and Discussions
### Tissue Characterization and Carbon Ring Analysis of Pyrolysis Products
#### 2.3.1 Carbon Ring Evolution with Different Heating Rates  
As described in Section 3.1, the temperature was raised to 3000 K at 5 K/ps, 10 K/ps, 50 K/ps, 100 K/ps, and 200 K/ps heating rates, respectively. The changes in the various types of carbon rings during the heating process at different heating rates are shown in Figure 4. There are three main types of carbocycles generated during pyrolysis, i.e., 5-, 6-, and 7-membered rings, which we denote using 5r, 6r, and 7r, respectively. Since there are only 6r in the PS pyrolysis precursor, the change in its number represents the pyrolysis process, while the presence of 5r and 7r represents the generation of new pyrolysis products.  
Figure 4 shows that higher heating rates can inhibit the early cleavage of the 6r and the production of 5r and 7r. Consistent with the previous analysis, this suggests that the low-rate heating (5 K/ps, 10 K/ps) contributes to the sufficient cleavage of the precursor,  
Figure 4: Variation in carbon rings with time during heating to 3000 K at different heating rates.  
resulting in the production of type-rich pyrolysis intermediates, as 5r and 7r are significantly observed. In sharp contrast, 6r changes extremely slowly in the high-rate heating plots, with a very small number of pyrolysis intermediates, and no new product formation is observed even at a heating rate of 200 K/ps. The analysis of carbon rings further proves that low-rate heating helps yield more diverse pyrolysis intermediates.  
The changes in carbon rings during the next thermostatic simulation at 3000 K are shown in Figure 5. As can be seen, there is a clear inflection point in the 6r plots. At the early stage of the thermostatic simulation, the cracking of the precursor continues and 6r keeps decreasing. During this period, the magnitude of the decrease in the 6r plots is proportional to the rate of heating, i.e., the higher the heating rate, the greater the magnitude of the decrease in the 6r plots, indicating that insufficient cleavage at the heating stage is compensated for in the thermostatic stage.  
Subsequently, accompanying the completion of the precursor pyrolysis and the secondary cross-linking reaction, graphite-type tissues are generated in large quantities, as evidenced by the fact that the amount of 6r starts to rise rapidly and becomes the main structure in the final product, outnumbering both 5r and 7r. Since the heating rate hardly affects the equilibrium product, the same equilibrium tissue is obtained at 3000 K for the five heating rates.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 4 Opportunistic Routing and Problem Formulation
### Overview of Opportunistic Routing  
In opportunistic routing, a sender does not rely on a single relay node to forward a packet in the routing process, while the sender transmits its packet to neighbor nodes at the same time, and one of these neighbor nodes will be selected to forward the packet. By properly setting up the selection mechanism, the most suitable relay node will be selected.  
In order to obtain a higher packet delivery ratio, neighbor nodes cooperate to forward packets. The cooperation means that each neighbor node can forward the packet, but the priority of forwarding the packet is different. The Node with the Highest Priority (NHP) firstly forwards the packet. Once the packet has been forwarded, other neighbor nodes give up forwarding the packet, and drop the packet immediately. Thus, each node needs to set a timer based on its priority. The higher the priority is, the shorter the timer duration is. This ensures that the NHP firstly forwards the packet.  
As shown in Figure 3, vehicle \(\theta_{i}\) is the sender, and it holds a packet and needs to forward the packet to its neighbor vehicles \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) simultaneously. Once the packet has been received, they start a timer and listen to whether the packet is forwarded by one vehicle. Assuming that the priority of \(\theta_{1}\) is highest, it firstly forwards the packet. In this case, \(\theta_{2}\) and \(\theta_{3}\) give up forwarding the packet, and drop it at once.  
In our methodology, each packet acts as an agent in DRL. The agent selects a relay node based on the environment knowledge and it will be transmitted toward the relay node. The proposed SROR algorithm makes full use of the ability of DRL to learn the environment knowledge, so that the agent can select the appropriate relay node, avoid the attack nodes to participate in the routing, and finally construct a secure and stable route.
## 2 Materials and Methods
### Data
#### 2.2.2 MODIS Data  
MODIS products (MOD9A1, MOD15A2H) with 8-day composites and a 500 m resolution were used in this study ([[https://lpdaac.usgs.gov/products/mod09a1v061/](https://lpdaac.usgs.gov/products/mod09a1v061/)]([https://lpdaac.usgs.gov/products/mod09a1v061/](https://lpdaac.usgs.gov/products/mod09a1v061/)) (accessed on 22 February 2024) [30]. MOD15A2H provides Leaf Area Index (LAI) data. MOD09A1 surface reflectance data, processed from MODIS Level 1B radiance measurements, contain the following seven spectral bands: red (620-670 nm), near-infrared (NIR1: 841-876 nm; NIR2: 1230-1250 nm), shortwave infrared (SWIR1: 1628-1652 nm; SWIR2: 2105-2155 nm), blue (459-479 nm), and green (545-565 nm) [65]. The Land Surface Water Index (LSWI) [66] and Enhanced Vegetation Index (EVI) [67] were derived from MOD09A1. A Savitzky-Golay filtering algorithm was applied to mitigate cloud-induced atmospheric noise in the time-series remote sensing data [68]. EVI and LSWI were calculated as follows:  
\[EVI=G\times\frac{\rho_{NIR}-\rho_{Red}}{\rho_{NIR}+(C_{1}\times\rho_{Red}-C_{ 2}\times\rho_{Blue})+L} \tag{1}\]  
\[LSWI=\frac{\rho_{NIR}-\rho_{SWIR}}{\rho_{NIR}+\rho_{SWIR}} \tag{2}\]  
where \(\rho_{NIR}\), \(\rho_{Red}\), \(\rho_{Blue}\), and \(\rho_{SWIR}\) are the reflectances of the blue, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, respectively, in Equation (1); G is set to 2.5, C\({}_{1}\) is set to 6, C\({}_{2}\) is set to 7.5, and L is set to 1.
## 2 Bayesian Approach  
The Bayesian approach is the method of estimation for unknown population parameters which utilizes both sample and prior information. The prior distribution is our beliefs about the unknown population parameter before some information is taken into account. The prior information is broadly divided into two-parts (i) informative prior and (ii) non-informative prior. The prior distribution of the parameter that has some known information regarding an unknown population parameter is called the informative prior, if the prior and sampling distribution belongs to the same family of distribution it is called the conjugate prior. In this study, the study variable \(X\) with mean \(\theta\) and variance \(\delta^{2}\) with the in-control process and taking the conjugate prior (normal prior) with parameters \(\theta_{0}\) and \(\delta_{0}^{2}\) is defined as follows:  
\[p(\theta)=\frac{1}{\sqrt{2\pi\delta_{0}^{2}}}\exp\left\{-\frac{1}{2\delta_{0}^ {2}}(\theta-\theta_{0})^{2}\right\} \tag{1}\]  
while if nothing is known about the population parameter, it is said to be a non-informative prior, which has less effect on the posterior distribution, and in this case, the prior distribution is proportional to the uniform distribution. The probability function \(p(\theta)\) represents the uniform prior distribution given by  
\[p(\theta)\propto\sqrt{\frac{n}{\delta^{2}}}=c\sqrt{\frac{n}{\delta^{2}}} \tag{2}\]  
where \(c\) is the proportionality constant.  
In the case of the uniform prior, the invariance property does not exist, thus [PERSON] [20] proposed the prior function which is proportional to the Fisher information matrix. The prior probability function suggested by [PERSON] is given below:  
\[p(\theta)\propto\sqrt{I(\theta)} \tag{3}\]  
where \(I(\theta)\) denotes the Fisher information matrix.  
The posterior distribution which is based on the combination of a sample and a prior distribution for a population parameter \(\theta\) is defined as follows:  
\[p(\theta/x)=\frac{p(\
icefrac{{x}}{{\theta}})p(\theta)}{\int p(\
icefrac{{x} }{{\theta}})p(\theta)d\theta} \tag{4}\]  
For a new data set \(Y\), the posterior predictive distribution based on posterior distribution is given by  
\[p(y/x)=\int p(\
icefrac{{y}}{{\theta}})p(\theta/x)d\theta \tag{5}\]  
The LFs play a vital role in the Bayesian inference which is used to reduce the risk related to the Bayes estimator. The two different LFs such as symmetric (SELF) and asymmetric (LLF) have been used in this study.
# CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge
## 5 Experiments and Results
### Detection Performance
#### 5.5.2 Analysis of Results  
As illustrated in Table 4, the F1 scores of Fed-FN models are much higher than those of the local, at 0.9893 on average. In general, Fed-FN increases the recall scores significantly for all examined car models. It should be noticed that the recall metric is more important than the precision in the case of an intrusion detection system, as the consequence of false negative cases are worse than that of false positive cases. What stands out in the result is that transfer learning from the global model increases the F1 score of the BMW car's local model by approximately 1.0, which helps the model satisfy the accuracy constraint for deployment. The primary reason for the suboptimal performance of the local model in BMW cars is their unique electronic architecture, wherein data generation usually exhibits a proportional relationship between the number of ECUs and CAN IDs. In contrast to other car models, BMW has a higher number of CAN IDs and lower data generation frequency shown in Table 1. This indicates a low command frequency and a high command interval time, which could contribute to the less responsive system and the inferior local model performance of BMW compared to other car models. Though modifying federated deep learning hyperparameters can improve BMW's accuracy, it may lead to complexity. Using a global model instead of customizing hyperparameters can improve accuracy without adding complexity. Sharing ideas between local and global models can improve accuracy rates. In addition, the results of the Fed model are shown to demonstrate the essentials of the fine-tuning step. We can see that the Fed model produces the lowest F1 score due to the effect of data heterogeneity. Furthermore, the proposed system decreases the ER and FAR of local models significantly, especially in the case of the BMW (see Figures 7 and 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Car Brand** & **Model** & **Prec** & **Rec** & **F1** \\ \hline \multirow{4}{*}{Kia} & Local Model & 0.9996 & 0.9836 & 0.9915 \\  & Fed & 0.5782 & 0.9723 & 0.7252 \\  & Fed-FN & **0.9992** & **0.9912** & **0.9952** \\ \hline \multirow{4}{*}{Tesla} & Local Model & 0.9913 & 0.9655 & 0.9782 \\  & Fed & 0.7014 & 0.9462 & 0.8057 \\  & Fed-FN & **0.9915** & **0.9928** & **0.9922** \\ \hline \multirow{4}{*}{BMW} & Local Model & 0.8453 & 0.9041 & 0.8737 \\  & Fed & 0.5266 & 0.5897 & 0.5565 \\  & Fed-FN & **0.9907** & **0.9706** & **0.9806** \\ \hline \multirow{4}{*}{Average} & Local Model & 0.9454 & 0.9511 & 0.9478 \\  & Fed & 0.6021 & 0.8361 & 0.6958 \\ \cline{1-1}  & Fed-FN & **0.9938** & **0.9849** & **0.9893** \\ \hline \end{tabular}
\end{table}
Table 4: Personalized FL compared to other baselines.
## 7 Future Trends in Next-Generation Smart Grids (SGs) After the Integration of EV Market
### Intelligent Solutions for Demand-Side Management (DSM) of the EV Market in SG  
Demand-side management (DSM) is an actively researched field in the area of SG. The idea is for customers to vary their load profiles for the mutual benefit of the grid and themselves. Thus, its global objectives are electricity cost minimization, minimizing customer discomfort, maximizing the usage of local energy generation, minimizing peak demand, and minimizing the peak-to-average energy ratio (PAER). Many optimization algorithms have been proposed to tackle this problem [113]. For non-flexible loads, a fuzzy controller and integer linear programming technique have been applied [114]. Genetic algorithms for multi-objective optimization have been applied to situations to meet the different needs of residential and industrial customers. For optimum switching of flexible loads for highly fluctuating energy prices when integrating large-scale fluctuating RESs like solar and wind, a multistage stochastic mixed-integer linear programming (SMILP) is proposed [115]. The clonal selection algorithm (CSA) is also proposed for optimal day-ahead load scheduling for large-scale integration of PV plants by the projection of anticipated load and generated energy forecasting [116]. For the rapidly fluctuating load demands of the emerging EV market, several strategies, like covariance matrix adaption evolution strategy (CMAES)-based optimization algorithms, are proposed to maximize the EV charging capacity while minimizing the cost for owners and EV battery providers [117]. Currently, the problem could be tackled by ensuring consumers' acceptance and participation in DSM programs, as shown by a simulation study [118].
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 7 Simulation Results
### Simulation Results of Depth Distribution Characteristics of Convolutional Codes  
In this paper, the binary convolutional codes defined on \(GF(2)\) with n = 2, k = 1 and D = 6 are taken as the research objects to analyze the depth distribution characteristics of their codewords. The sender generates random data and encodes it using the generating matrix of convolutional codes (2,1,6). The receiver obtains the codeword matrix through the noisy transmission channel. When the estimation of the synchronization position parameter \(d_{e}\) is correct, the distribution of the convolutional codeword matrix corresponding to different code lengths \(n_{e}\) is shown in Figure 13.  
Combined with the encoding parameters of the convolutional code (2, 1, 6), according to Equation (34), when the parameter estimation of the synchronous position is correct, the corresponding relationship between the normalized depth distribution function and the code length of the convolutional code is completely consistent with the simulation results in Figure 13a. However, in Figure 13b of the simulation results of the matrix rank algorithm, we can observe that the overall distribution changes are disorderly. This is because, although the error-resistant performance of convolutional codes is better than that of general linear block codes, noise still causes great damage to the code-word matrix, resulting in disordered rank changes. When the estimate of code length \(n_{e}\) is correct, the  
Figure 11: The distribution of \((15,5)\) BCH code. (**a**) results based on depth distribution algorithm, (**b**) results based on matrix rank algorithm.  
Figure 12: The distribution of different start bits. (**a**) results based on depth distribution algorithm, (**b**) results based on matrix rank algorithm.  
depth distribution of convolutional codewords corresponding to parameter \(d_{e}\) at different synchronous positions is shown in Figure 14.
## 4 Results of Probabilistic Stability and Performance Assessment
### The Impact of PLL Bandwidth on System Stability and Performance  
This section investigates the impact of PLL bandwidth on system stability and performance. It is worth remarking that the PLL bandwidth is usually a known and constant parameter, unlike the grid SCR and operating point conditions; hence, the stability assessment could be performed for the provided value of the PLL bandwidth, as presented in Sections 4.1 and 4.2 and as shown in Figures 6-9.  
However, additional functionalities were recently suggested for VSCs to support power grids, e.g., transient stability enhancement, virtual inertia implementation, and fault ride-through capability. To fulfil these objectives, a VSC might be equipped with a PLL designed with adaptive or gain scheduling techniques. In this respect, this subsection considers five different values for PLL bandwidth, and the probabilistic assessment is carried out on each of them. The results are presented in Figure 10. This also revealed the importance of an optimal and robust control system design in order to guarantee a stable operation over grid uncertainties. In this regard, the next research step would be a probabilistic control system design that minimizes the probability of the critical mode in the unsuitable area and considers the statistical properties of stability indices. This is highlighted in Section 6 as future work.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 3 Experiments and Results
### Aerospace Grid Indexing BigTable  
The experiment of grid indexing BigTable included four steps: (1) grid encoding, (2) data storing (in database), (3) creating index, and (4) grid querying. Based on the GeoSOT-3D model and its method, the trajectories of the 12,000 satellites of the simulated constellation during this period were mapped to the grid and stored in the table.  
The process was as follows:  
Step 1: Grid encoding: The trajectories of 12,000 satellites were mapped to the grids and grid codes were assigned. According to the TLE (Two-Line Element, which is a data format encoding a list of orbital elements of an Earth-orbiting object for a given point in time [39]), the spatial position information of satellites at each time and their corresponding grid codes were calculated. Then, the line object grid modeling and 3-dimensional path-filling algorithm were used to achieve grid mapping of the entire satellite trajectory during the simulation period. The Walker simulation constellation and GeoSOT-3D grid were visualized in CesiumJS (an open source JavaScript library, shown in Figure 9a).  
Figure 9: System interfaces: (**a**) satellite with codes in CesiumJS; (**b**) data stored in MongoDB.  
Step 2: Data storing (in database): the calculated data results were generated in the JSON format, imported into the database in batches, and associated with the primary key of the aerospace grid indexing BigTable with codes. Taking the MongoDB database as an example, the _id column is the primary key, which is unique and stores the grid code; the data column stores the data attributes; the time column is the time code (Figure 9b).  
Step 3: Creating indices: a spatio-temporal secondary index was created on the S-Code and T-Code columns, and a joint ascending index was created for latitude, longitude, and elevation.  
Step 4: Grid querying: queries were carried out with the conditions of \"grid code\" and \"latitude, longitude and height\" representing the same area, and the number and costing time of returned items was recorded.  
In our experiment, when the ninth grid level was chosen, there were 345,720,000 data in database, and for the 15 th level, there were 518,400,000 data. Subsequently, we created indices on the code column and the three columns of latitude, longitude, and height separately. After creating the indices, the 15 th level was selected for querying. The query results are listed in Table 3. It can be seen that the efficiency of the code method is significantly better than the combined query method of longitude, latitude, and height, with an average increase of 8.82 times.