esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Greibach Normal Form Grammars Using Genetic Algorithms
## 3 Proposed Algorithm of Grammatical Evolution for Greibach Context-Free Grammars
### Data Requirements and Parameters Definition  
The proposed GE inference algorithm for GFN estimation requires a number of data strings and the definition of several parameters which affect the algorithm's convergence and efficiency.  
1. Two training datasets. The positive set of terminal sequences \(Sp\) is used to be effectively parsed by the GFN solution, i.e., \(Sp\subset L(GNF)\) and the negative set \(Sn\) of terminal sequences is used to avoid generalization of the candidate solutions, i.e., \(Sn\cap L(GNF)=\emptyset\);
2. Evolutionary parameters: The mutation rate is the probability rate of applying the mutation operator. The elitism rate is the percentage of best chromosomes being migrated to a new population. The size of the population is the number of candidate solutions at every generation. The convergence criterion in the form of a fitness value that is considered sufficient for a good candidate solution. The Parental portion is the percentage of the best chromosomes that will be assigned with a probability to generate offsprings.
3. GNF parameters: The Size of the set N (non-terminal symbols), denoted by \(n_{N}\). The Terminal symbols set T which is inferred by the positive and negative examples and its size is denoted by \(n_{T}\). The maximum length of the production rule (\(RL_{max}\)) and a maximum number of the production rules (\(NoR_{max}\)) in a GNF grammar to be used in the pool initialization step. The sub-parse parameter a (\(<\)1) that facilitates better estimation of a chromosome's fitness
## 1 Introduction  
Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, electric vehicles (EVs) have gained massive popularity due to their performances and efficiencies in recent decades [1; 2; 3]. Lithium-ion batteries (LIBs) are widely used in EVs for their high-energy density, long service life, and environmental friendliness [4; 5; 6]. In actual practice, LIBs need to be well monitored, diagnosed, and controlled by the battery management system (BMS). The accurate state estimation of a battery is one of the most fundamental functions of the BMS. The states of the battery include state-of-charge (SOC), state-of-health (SOH), and state-of-function(SOF) etc. [7; 8]. Among these states, SOC in BMS is considered as one of the critical and important factors [9; 10], which have been researched in recent decades. Accurate battery state estimation can contribute to reasonable management of batteries to fully exploit them and prolong their lifespan. However, the battery states are non-measurable variables, which can only be indirectly estimated via measurable external characteristic parameters (e.g., voltage, current, and temperature). Owing to the high nonlinearity of the battery system itself, accurate estimation of the battery state is fairly difficult.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Benchmark Methods
#### 4.5.2 MMSE Estimator  
The MMSE estimation is proposed as a solution to the noise-prone flaws of LS estimation. This method aims to reduce the MSE between the actual channel and its estimation. However, obtaining the auto-correlation function of the channel and the noise variance, which is necessary for this task, can be challenging in an underwater environment. It can also be computationally expensive, making it inappropriate for real-time implementation in underwater communication systems; however, it provides enhanced performance compared to LS estimation.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 4 Conclusions  
The mainstream boundary recognition algorithms are generally based on the physical and geometric characteristics of roads, or the processing of a large number of GPS trajectories. Differently from these existing algorithms, the work reported in this paper focused on solving the problem of identifying PCA boundaries without any physical and geometric characteristics or large amounts of GPS data. To this end, we proposed the BSW method for PCA boundary recognition, using GPS-RTK. Our simulation experiments showed that when the proposed BSW algorithm was used and the single-point positioning accuracy was at the centimeter level, PCA boundary recognition for straight polygons reached single-point positioning accuracy, and PCA boundary recognition for curved polygons reached decimeter-level accuracy.  
In future research, we will use GPS equipment to obtain pavement construction boundary data, adopt the algorithm proposed in this paper to find the pavement construction boundary function, and plan the paths that the articulated road roller should track.  
Conceptualization, T.X., S.C. and D.W.; methodology, T.X.; software, T.X.; validation, T.X. and S.C.; formal analysis, T.X.; writing--original draft preparation, T.X.; writing--review and editing, T.X.; supervision, D.W. and W.Z.; project administration, D.W. and W.Z.; and funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, grant number BK20170681.  
The authors declare no conflict of interest.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 2 Materials and Methods
### Recurrent Neural Network (RNN)  
A Neural Network (NN) is also commonly known as an Artificial Neural Network (ANN). A recurrent neural network (RNN) is a type of recursive neural network that takes sequence data as input, recursion in the direction of sequence evolution, and all nodes (recursive neural network) are connected in a chain.  
An RNN is a special neural network structure, which is based on the idea that human cognition is based on past experience and memory. It is different from Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), in that it not only considers the input of the previous moment, but also gives the network a memory function of the previous content.  
RNNs are called circulatory neural networks, i.e., the current output of a sequence is also related to the previous output. The specific manifestation is that the network will remember the previous information and apply it to the calculation of the current output, i.e., the nodes between the hidden layers are no longer connected but connected, and the input of the hidden layer includes not only the output of the input layer, but also the output of the hidden layer at the previous moment.  
However, RNNs in VSR also suffer from vanishing gradients, like many other video processing tasks. To solve this problem, we propose a new recursive network called an RRN, which employs residual mapping between layers with identity skip connections. This design ensures a smooth flow of information and enables long-term preservation of texture information, making it easier for RNNs to handle longer sequences at the same time reduce the risk of gradient vanishing in training.
## 2 Datasets
### MODIS Data
#### 2.2.2 MCD12Q1 Land Cover  
Land cover plays a major role in the climate and biogeochemistry of the Earth's ecosystem. The MODIS land cover type product provides a suite of land cover types by using spectral and temporal information derived from the MODIS. It characterizes five global land cover classification systems--the International Geosphere Biosphere Program (_IGBP_), university of Maryland (UMD), Leaf Area Index/Fraction of Photosynthetically Active Radiation absorbed by vegetation (LAI/fPAR), New Patriotic Party (NPP), and Plant Functional Types (PFT)--at annual time steps and a 500 m spatial resolution. Land cover type assessment and quality control information are also included. Given that the land cover type may change over time, yearly MODIS land cover type data (MCD12Q1) are downloaded, and the IGBP land cover type is used.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 1 Introduction
Specifically, in marine atmospheric environments, the intricate interactions between the ocean and the atmosphere result in turbulence structures that differ markedly from those found on land, thereby augmenting the complexity of laser propagation. Consequently, accurately portraying and simulating the effects of marine atmospheric turbulence on laser transmission has emerged as a pivotal research focus. In this context, based on the marine atmospheric turbulence power spectrum and Maxwell's wave equation, we employ a numerical simulation method to systematically investigate the propagation characteristics of laser beams exhibiting several typical Zernike aberrations (including defocus, coma in the y-direction, spherical aberration, and secondary astigmatism in the y-direction) in various marine atmospheric turbulence condition. This study analyzes various parameters of the far-field beam spots, including the scintillation index, intensity probability density distribution, and statistical parameters such as mean, standard deviation, skewness, and kurtosis. Additionally, the average bit error rate <BER> of the system is calculated. Furthermore, we conduct a detailed analysis to compare the transmission outcomes of these aberrations with those in an aberration-free state.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 5 The \(D^{3}\)-Tree
### Single Queries with Node Failures
Then \(w_{f}\), using Lemma 1(ii) twice in succession, contacts its grandchild through its left and right adjacents and their grandchildren. In case the initial node \(v\) is a bucket node, if its range contains \(a\) the search terminates, otherwise the search is forwarded to the bucket representative. If the bucket representative has fallen, the bucket contacts its other representatives right or left, until it finds a representative that is reachable. The procedure continues as described above for the case of a binary node. The following lemma gives the amortized upper bound for the search cost in case of massive failures of \(O(N)\) nodes. Lemma 2: _The amortized search cost in case of massive node failures is \(O(\log N)\)._  
Proof: Let \(O(N)\) be the number of failed nodes in a network of \(N\) nodes and \(n\) elements. In general \(n\gg N\), and we may assume w.l.o.g. that \(n=\beta N\), for some (large) constant \(\beta\geq 1\) expressing the space capacity of each node (i.e., how many data elements can be stored in it). In the worst case, we want to execute \(O(n)\) search operations. If a node \(v\) cannot be contacted during the search procedure, the network initiates a node withdrawal procedure to remove \(v\). In case of massive failures, node withdrawals will not be negligible; hence the network will often activate the redistribution mechanism to avoid the structure's imbalance. The redistribution cost is bounded by the amortized bound \(O(\log N)\). In the worst case, the maximum number of redistributions will be \(O(N)\) (equal to the number of failed nodes), so the whole search process will require \(T(N)\) hops, where  
\[T(N)=O(N)\cdot O(\log N)\]  
The amortized cost \(\widetilde{T}(N)\) will then be  
\[\widetilde{T}(N)=\frac{T(N)}{O(n)}=\frac{O(N)\cdot O(\log N)}{O(n)}=c\cdot O( \log N)=O(\log N)\]  
Figure 3: Example of vertical search between \(u\) and unreachable \(w\). where \(c=O(1/\beta)\) is some constant with \(0<c\leq 1\).
## 4 The Proposed Genetic Algorithm
### Problems of Applying Basic GA for DNN Partitioning  
The chromosome coding scheme is the fundamental element of GA. In solving the above DNN partitioning problem, assume that each chromosome represents an actual distributed deployment solution. Suppose the required DNN model has \(l\) layers, which will be divided into \(n(n\leq l)\) pieces and deployed to \(n\) different edge devices. To ensure that each sub-model contains only continuous layers to avoid extra data transmission costs, this study constructs a matrix of \(n\) rows and \(l\) columns to represent a specific deployment scheme, i.e., a chromosome in the GA. For example, the following matrix denotes the deployment scheme that partitions a DNN model with seven layers into three parts and distributes it to three different devices.  
\[c=\begin{bmatrix}1&1&0&0&0&0&0\\ 0&0&1&1&1&0&0\\ 0&0&0&0&0&1&1\end{bmatrix} \tag{5}\]  
If \(L_{i}\) is the \(i\)th (\(i=1,2,\ldots,l\)) layer in a given DNN model, this chromosome represents that \(L_{1}\) and \(L_{2}\) will be divided into a group and deployed on device \(d_{1}\), \(L_{3}\), \(L_{4}\) and \(L_{5}\) will be divided into a group and deployed on device \(d_{2}\), and \(L_{6}\) and \(L_{7}\) will be divided into a group and deployed on device \(d_{3}\).  
The basic process of GA starts with generating an initial population, i.e., a set of chromosomes following the above coding scheme. Then, it will run through the loop, including individual evaluation, selection, crossover, and variation, until satisfying the given termination condition. The cross operation plays a core role in GA, which acts on a group of chromosomes and generates new individuals by replacing part of the chromosomes of two father-generation individuals. Figure 2 shows a simple example of computation in a partially mapped crossover operator.  
In Figure 2, \(C_{1}\) and \(C_{2}\) are the two father individuals, while \(C_{11}\) and \(C_{21}\) are two new individuals generated by swapping the subsections in each father individual included in the rectangles. It is not difficult to find that the assumption that each sub-model only contains continuous layers is broken during the above crossover operation. For example, layers \(L_{2}\) and \(L_{5}\) are grouped together and deployed to device \(d_{2}\) in the left new individual \(C_{11}\), while layers \(L_{2}\), \(L_{5}\), and \(L_{6}\) are grouped together and \(L_{1}\), \(L_{3}\) and \(L
## 4 Results and Discussion  
### Evaluation of Generated Data  
#### 4.1.1 Diversity  
In validating the diversity of generated samples, this study employed PCA and t-SNE for dimensionality reduction and visualization, as depicted in Figure 11. The blue dots represent generated samples, while the red dots represent original samples. Since the original scenario is a roundabout, which is a complex scenario with four entrance directions and a large gap between the traffic flows in different directions, the original data suffered from a variety of categories and sample imbalance. This distribution can be clearly found in PCA dimensionality reduction, which is proficient at retaining the global structure of the data. Figure 11a indicates that the overall distribution of generated data was in line with the actual data, but this also implies that PCA's dimensionality reduction method may not effectively showcase the diversity of the data. On the other hand, t-SNE can preserve the local structure and distance relationships between data samples. As seen in Figure 11b, although the generated data did not completely overlap with the  
Figure 10: Roundabout scene in CARLA simulation.  
original data, the distribution distance with the original data was close to the original data, indicating good similarity between the two. The distribution range of the generated data demonstrated the existence of diversity among samples. For random events like vehicle interactions, each occurrence varied, and the number of samples that can be collected was limited. Generating samples in this way not only learns the characteristics of the interaction but also generates conflict samples that have not been collected, which provides a new perspective for the establishment of a hazardous scenario library.  
We randomly selected a sample from both the generated and original datasets originating from the same entrance of the scenario. By simply visualizing the trajectories, Figure 11(a),b show a generated interaction and the original interaction scenario, respectively. It can be seen that the generated scenario bore a high resemblance to the original one, albeit with its own distinct characteristics. The generated data also conformed to the actual driving pattern and met the dynamic requirements. This indicates that augmenting the dataset through data generation methods can expand the scenario library and enhance diversity.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 3 A Security-Oriented Data-Sharing Scheme Based on Blockchain
### Formal Model
#### 3.2.3 Shared Data Flow History Tracking Mechanism Based on NFT  
In a blockchain, the data owner \(u_{i}\) and the data demander \(u_{j}\) transfer information about the shared data \(d_{i}^{m}\) via an NFT transaction flow. Therefore, by keeping a record of the NFT flow, a more intuitive understanding of the whole event process can be provided, and the traceability of the identities of the parties exchanging data can be achieved.  
We use the hash value of the shared data to create the NFT as proof of ownership for the data. When a data owner \(u_{i}\) uploads shared data \(d_{i}^{m}\) to a data-sharing platform, the encrypted hash value of the data storage address for that user is uploaded and minted into an NFT asset warrant, providing proof of ownership of the data owner \(u_{i}\). Smart contracts, functioning as gateways for blockchain interactions, embody automatic and immutable execution mechanisms. These features facilitate the secure storage and efficient querying of data exchange records. Consequently, this paper incorporates a tracking framework within the smart contract design for NFTs, as outlined in Table 1, to enhance traceability and transparency. The smart contract initially facilitates the creation of NFT assets on the blockchain through the initLedger() function. The mine() function is then used to mint new NFT instances, each securely embedding the encrypted hash value of the shared data's storage address provided by the data owner \(u_{i}\). These NFTs also contain metadata with relevant details, including document identifiers and exact transaction timestamps. Data sharing begins with the authorization of eligible recipients. The transfer of NFT assets is initiated only after authorization is confirmed, ensuring secure delivery to the authorized data demander \(u_{j}\). The data-sharing record is stored in the NFT via the smart contract. Both the data owner \(u_{i}\) and the data demander \(u_{j}\) can query the data-sharing flow record \(d_{i}^{m}\) in the blockchain by searching the short file name. This process is illustrated in Figure 4.  
Initially, the data owner \(u_{i}\) and the data demander \(u_{j}\) register their identity information in the system and receive the key. Then, after uploading the data \(d_{i}^{m}\) to IPFS and obtaining the corresponding hash value, the data owner \(u_{i}\) obtains the ciphertext of the data hash value using the lightweight cryptography algorithm proposed in this paper and saves the ciphertext of the hash value into the NFT. When the data owner \(u_{i}\) and the data demander \(u_{j}\) initiate data sharing, the NFT records the identity information of both parties and the transaction timestamps to save the flow record. Finally, data owner \(u_{i}\) and data demander \(u_{j}\) can query the records related to this shared data \(d_{i}^{m}\) from the blockchain based on the short name of the file.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 2 Polar Codes  
Polar codes are obtained through channel polarization processing, which aggregates and separates bits on the coding side to present different reliabilities of each sub-channel. As the code length approaches infinity, some channels tend to be noise-free channels with a channel capacity of one for transmitting information bits, while the other channels tend to be all-noise channels with a channel capacity of zero for transmitting frozen bits and auxiliary information bits. On the decoding side, the polarized channels can be decodedwith suitable decoding methods at lower complexity to achieve performance close to that of maximum likelihood decoding.
# Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots
## 2 Biologically-Inspired Kalman Filter Based RBFNN Control  
### Kalman Filter Algorithm  
This section aims to describe the Kalman filter algorithm by which measurements are taken, and the state is estimated at discrete time points. The Kalman filter deals with the general problemsencountered in estimating the state of a discrete-time controlled process, which is governed by the following state-space Equation (1) at time index \(k\):  
\[\begin{array}{l}x(k)=Ax(k-1)+BU(k-1)+w(k)\\ y(k)=Cx(k)\\ z(k)=Hy(k)+v(k)\end{array} \tag{1}\]  
where \(A\), \(B\), and \(C\) are matrices in the state-space Equation (2). \(w(k)\) is the process noise and \(v(k)\) is the measurement noise. \(z(k)\) is the measured signal and \(H\) is the sensor matrix. The probability of the process noise \(w(k)\) is \(p(w)\) and the probability of measurement noise \(v(k)\) is \(p(v)\). The process noise covariance of \(p(w)\) is \(Q\) and the measured noise covariance of \(p(v)\) is \(R\). In Kalman filtering, \(p(w)\) and \(p(v)\) are independent white noises with normal probability distributions, expressed by:  
\[\begin{array}{l}p(w)\sim N(0,Q)\\ p(v)\sim N(0,R)\end{array} \tag{2}\]  
Figure 1 presents the structure of Kalman filter algorithm in which the estimated state \(\hat{x}(k-1)\) and the error covariance \(P(k-1)\) are included [13]. As shown in Figure 1, The Kalman filter algorithm consists of two phases: time update phase (predictor) and measurement update phase (corrector). The following summarizes the two important phases in classical Kalman filter algorithm.  
1. Time update phase: 1. At time step \(k-1\), calculate \(\hat{x}(k-1)\) and \(P(k-1)\). 2. Update the estimation of state vector \(\hat{x}^{-}(k)\) and the estimation of error covariance matrix \(\hat{P}^{-}(k)\).
2. Measurement update phase: 1. Update the optimal gain \(K(k)\) of Kalman filter. 2. Update the estimation of state vector \(\hat{x}(k)\) using \(z(k)\), \(\hat{x}^{-}(k)\) and \(K(k)\). 3. Update the estimation of error covariance matrix \(p(k)\) by utilizing \(K(k)\) and \(P^{-}(k)\) for next iteration in the Kalman filter algorithm process.  
Figure 1: Structure of the classical Kalman filter.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### Miscellaneous Methods
A CR-based IoT network is considered as a scenario with the aim of optimal end-to-end routing for the SUs, in [86]. The SUs are modeled as vertices of a graph, with the links between them being the edges and the weights being the SINR per link. A genetic algorithm obtains the optimal channel allocations, with the power being fixed. The IoT network throughput is shown to decline exponentially with the increase in spectrum utilization (longer routes among the SUs). Optimizing EE and throughput in a CR sensor network comprised of CAPs and SU sensors is considered in [87]. The network is modeled via a hypergraph, the vertices of which are the SU sensors, and its hyperedges encompass ordered subsets of both the interfering and interfered SUs, their received powers constituting the edges' weights. Then, the task is formulated as channel allocations, such that the utility function\(\Omega_{U}\) (comprised of both the EE and throughput) is maximized. Fast convergence is achieved. RA in a CR network is studied by the authors of [88]. A graph describes the conflicts (of associations with the same CAP, and on the same channel) between the SUs, with the edges' weights representing the potential mean throughput of a particular allocation and association. Two variants of the algorithm, with either assumed mean throughput per connection or its estimation, are developed. When they are compared in the simulations, the latter variant exhibits logarithmic decline in its performance, as the numbers of SUs, CAPs, and channels increase.