esa-sceva/satcom-chunk-collection huggingface dataset:
# An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge
## 4 Experiment
### Comparison with Public Models
#### 4.3.2 Results on the Dataset DUC 2004  
On the DUC 2004 dataset, we compared the model with the following publicly available models.  
* ABS [14]: a CNN encoder and an NNLM decoder are used to accomplish the summarization task.
* ABS + [14]: an enhanced version of the ABS model, which relies on a series of individually extracted summary features that are added as log-linear features in the secondary learning step.
* RAS-Elman [15]: using an attentive encoder and RNN-based decoder.
* SEASS [12]: a selection mechanism is used to control the information input to the decoder to remove redundant information.
* DEATS [39]: a dual-coding model is proposed that mutually benefits from the basic model and achieves state-of-the-art results compared to existing methods.  
Table 3 shows the results of comparing the model in this paper with existing models. As shown in the table, our models all outperformed all baseline models. It reached 29.98, 9.80, and 26.12 on Rouge-1, Rouge-2, and Rouge-L, respectively. Experiments on this dataset further demonstrate the performance of the model in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Rouge-1** & **Rouge-2** & **Rouge-L** \\ \hline TextRank & 34.11 & 12.78 & 22.5 \\ Seq2 seq + attention & 31.34 & 11.79 & 28.10 \\ PGN & 36.44 & 15.66 & 33.42 \\ PGN & 36.44 & 15.66 & 33.42 \\ PGN + Cov & 39.53 & 17.28 & 36.38 \\ FASum + FC & 40.38 & 17.67 & 37.23 \\ ML + RL, intra-attention & 39.87 & 15.82 & 36.9 \\ ML + RL Rouge + Novel, with LM & 40.19 & 17.38 & 37.52 \\ Lightweight Meta-Learning & 39.94 & 16.96 & 26.09 \\ DEATS & 40.85 & 18.08 & 37.13 \\ BERTSumABS & 41.72 & 19.39 & 38.76 \\ TransformerS2S + CS + RL & 42.71 & 19.94 & 39.31 \\ JPATS & 41.25 & 18.81 & 38.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rouge scores of JPATS on the dataset CNN/DailyMail.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Readability** & **General** \\ \hline Seq2 seq + attention & 3.52 & 3.44 \\ PGN + Cov & 3.66 & 3.39 \\ FASum + FC & 3.70 & 3.69 \\ DEATS & 3.73 & 3.71 \\ ML + RL, intra-attention & 3.79 & 3.76 \\ ML + RL Rouge + Novel, with LM & 3.85 & 3.80 \\ BERTSumABS & 3.97 & 3.92 \\ TransformerS2S + CS + RL & 4.03 & 4.03 \\ JPATS & 4.12 & 4.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Manual scoring of JPATS on the dataset CNN/DailyMail.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 1 Introduction
In [30], the modified tabu search algorithm was designed for the optimal path length in grid environments. In [31], path length and smoothness were enhanced by two multi-objective memetic algorithms. In [32], an intelligent water drop algorithm was proposed to increase the length and safety. An available path was found by the artificial bee colony algorithm in [33], then the length and smoothness of the obtained path were optimized by evolutionary programming. In [34], a model of path length and danger degree was solved by the improved particle swarm algorithm. The degree of risk and path length were minimized by particle swarm optimization (PSO) in unknown circumstances [35]. In [36], the chaotic particle swarm optimization algorithm was used to enhance the control points of the Bezier curve to reach a short and smooth route. The hierarchical global path planning method was introduced in [37]. The method designed a three-stage structure to obtain an optimal route. First, a free geometric configuration space was determined with the triangular decomposition method, and then based on the configuration space, the Dijkstra algorithm was utilized to obtain a viable route. Finally, constrained particle swarm optimization was designed to optimize length and smoothness. Similar hierarchical strategies could be found in [38]. Individuals within the initial population were generated by the surrounding point set algorithm. Then, the length and smoothness were enhanced by the particle swarm optimization algorithm. In this study, the multi-objective path planning is solved with the improved NSGA-II. The work is shown below:  
* The framework of the improved NSGA-II is introduced. Several practical evolutionary operators are presented to enhance the feasibility of the route and optimize three objectives (length, smoothness and safety). They can enhance the local search capabilities of the improved NSGA-II. * The parameters in the algorithm are systematically studied. The results show that larger population sizes, larger numbers of generations and high operator probabilities are indispensable in complex environments. * The improved NSGA-II is tested with an existing evolutionary algorithm and different quality metrics. The comparisons show that the non-dominated solutions received via the improved NSGA-II have good characteristics. * The path corresponding to the knee point of non-dominated solutions is shown. The route is shorter, smoother and safer. The rest of the work is designed below. The associated work is introduced in Section 2. Section 3 defines environmental modeling, representation of the path and the objectives that need to be optimized. Section 4 details NSGA-II and evolutionary operators. The parametric study is presented in Section 5. Section 6 exhibits the comparison results. Lastly, the conclusions are displayed in Section 7.
# Design and Implementation of a Passive Optical Network for a Small Town +
## 3 Methodology  
### Service Requirements and Bandwidth Estimation  
To design the GPON-based FTTH network, we first identified the target user services and their bandwidth needs. In a typical small-town deployment, residential subscribers demand a mix of high-speed Internet, Internet Protocol television (IPTV), and Voice over IP (VoIP) telephone services (i.e., a triple-play offering). We assumed each household may utilize multiple IPTV streams, one or more VoIP lines, and general internet access simultaneously during peak hours. Table 1 summarizes the service profile considered per user, and the estimated bandwidth requirements for each service, based on industry guidelines and standards.  
Each IPTV stream (e.g., a live HD channel) was budgeted at around 5-8 Mbit/s, and we allowed for up to 2 simultaneous HD streams per home (for multiple TVs). For 4K UHD content, a single stream can demand \(\sim\)20 Mbit/s, so homes with 4K service would consume more bandwidth (we assumed such cases are a smaller fraction of users). VoIP lines consume relatively negligible bandwidth (\(\approx\)0.1 Mbit/s per call) but were included to account for phone service. The remaining bandwidth is for general internet usage (web, video streaming, gaming, etc.), which we dimensioned according to the subscriber's service tier (e.g., 50 Mbps or 100 Mbps plan). In summary, a typical peak usage scenario per household might involve two HD IPTV streams (\(\sim\)10-12 Mbps total), one active VoIP call (\(\sim\)0.1 Mbps), and concurrent internet use (which could range up to the subscribed downstream rate). This yields an aggregate peak demand per user on the order of tens of Mbps (e.g., 50+ Mbps in the worst case for a heavy-use household). These service demand values are in line with real-world FTTH triple-play deployments and provide the basis for network dimensioning.
## 3 Results
### Fine Dead Fuel Load Estimation and Model Selection  
Table 3 summarizes the performance metrics of each model to estimate the 1-h fuel load (t/ha) in the field plots. Based on the performance metrics, RF showed better performances in fine dead fuel load estimation compared to the MLR and SVM models. No substantial differences in prediction accuracy were observed between the MLR and SVM models. For RF, Pearson's correlation coefficient (\(r\)) was 0.71 and the R\({}^{2}\) value was 0.50, while for MLR, both values were 0.63 and 0.40, respectively. The highest RMSE value (0.11) was observed in the MLR model, while the lowest value (0.09) was found in the RF model. The cross-validations confirmed the robustness of the RF model (Table 4). The results calculated for 10 subsamples of training and test sets showed similar metrics values, demonstrating no overfitting. Thus, the RF model was chosen to predict the 1-h fuel load in a pilot area of Apulia.
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
###### Abstract  
An eight-element antenna system operating at sub 6 GHz is presented in this work for a future multiple-input multiple-output (MIMO) system based on a modified E-slot on the ground. The modified E-slot significantly lowers the coupling among the antenna components by suppressing the ground current effect. The design concept is validated by accurately measuring and carefully fabricating an eight-element MIMO antenna. The experimentation yields higher element isolation greater than \(-21\) dB in the 3.5 GHz band and the desired band is achieved at \(-6\) dB impedance bandwidth. The E-shape slot occupies an area of 17.8 mm \(\times\) 5.6 mm designed on an FR-4 substrate with dimensions of 150 mm \(\times\) 75 mm \(\times\) 0.8 mm. We fed the I-antenna element with an L-shape micro-strip feedline, the size of the I-antenna is 20.4 \(\times\) 5.2 mm\({}^{2}\), which operates in the (3.4-3.65 GHz) band. Moreover, our method obtained an envelope correlation coefficient (ECC) of \(<\)0.01 and an ergodic channel capacity of 43.50 bps/Hz. The ECC and ergodic channel capacity are important metrics for evaluating MIMO system performance. Results indicate that the proposed antenna system is a good option to be used in 5G mobile phone applications.  
antenna systems; 5G; MIMO; ECC +
Footnote †: journal: Electronics Letters  
13 December 2022  
1
## 3 Physical Model Test
### 3.1. Similitude Modeling and Design of Shaft Lining Models
#### 3.1.2 Model Geometric Parameters  
In this study, the prototype shaft lining corresponds to the D4A-D4A cross-section of the main inclined shaft in Macheng Iron Mine, Tangshan City, Hebei Province, China. The prototype lining length is set to 6 m. To investigate its stress-deformation behavior, strain gauges are installed on the lining's inner surface. Considering experimental practicality and model size constraints, the outer half-span (B) of the lining is determined as 120 mm. The dimensional parameters of both prototype and model linings are summarized in Table 1.  
In practical engineering, the surrounding rock thickness of prototype shaft linings is substantial. Therefore, thicker model surrounding rock better replicates actual prototype conditions. Considering practical constraints and experimental setup limitations, numerical simulations using ANSYS software were conducted to analyze the errors in inner and outer edge strains of the shaft lining relative to reference values when the ratio of surrounding rock radius (r) to inner diameter of the lining crown (d) varies between 1 and 10. The results are shown in Figure 4. Typically, a 10-fold surrounding rock model approximates real-world conditions more closely and was thus used as the reference. The analysis reveals that larger surrounding rock ranges reduce errors in circumferential strains at the lining edges. At r/d = 1, significant discrepancies exist between measured and reference strains. When r/d = 1.5, these errors decrease markedly. Consequently, an r/d ratio of 1.5 was selected for the shaft lining model tests.
## 2 Materials and Methods
### Rotational Axis Detection Module
#### 2.3.1 Dataset  
We created a custom dataset to train the network for rotational axis segmentation. This dataset consists of images containing rotation fronts, along with rotation axis masks, handle masks, and rotation front masks. Because no publicly available dataset provides such data, it was necessary to create a new dataset for the training, validation, and testing of the neural networks. Ambiguous rotational axes were omitted during dataset creation. Therefore, the module does not estimate the axes of rotating objects with horizontal axes. Some of the images were obtained directly from the handle and front detection datasets. An overview of the dataset is presented in Table 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Number of Images** & \
\begin{tabular}{c} **Number of** \\ **Rotational Fronts** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Translational Fronts** \\ \end{tabular} \\ \hline Train & 768 & 951 & 1560 \\ Validation & 181 & 243 & 388 \\ Test & 100 & 356 & 194 \\ Sum & 1049 & 1550 & 2142 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the dataset used to train and verify neural networks for front detection and segmentation.
## 1 Introduction
They also conclude that the Levelized Cost of Electricity (LCOE) of a SGHEPP is 20% less than a stand-alone GEPP, under the assumptions of a supercritical binary cycle and a 150 degC reservoir temperature. In the present paper, for the Rankin cycle, the working fluid of R134a and several low-GWP fluids (R113, R114, R132, R236a) were investigated, among which R132 and R114 corresponded to the conditions of the cycle and had a positive result. The three working fluids of R114 and R123 were at the same level of exergetic, economic and environmental performance, but the R114 fluid was thermodynamically appropriate, thus it was selected as the working fluid. In the following, according to the table Appendix A, both conventional and advanced exergy analyses were performed on a hybrid geothermal-solar power plant to reveal thermodynamic efficiencies within this coupled system. With the help of conventional exergy analysis, the components with the highest exergy destruction and irreversibility were determined. Moreover, the true improvement potential of each unit in the system was determined by splitting exergy destructions into avoidable and unavoidable parts via advanced exergy analysis. Generally, in previous researches, a system is only evaluated economically or environmentally. The advantage of this paper is that the two systems, namely the standalone geothermal system and hybrid geothermal-solar system, were evaluated economically and environmentally using conventional and advanced analysis; then, equipment was prioritized for economic and environmental optimization. Therefore, the present study attempts to explain the system conditions, the first and the second law of thermodynamics and then to model systems. It is then followed by analyzing and discussing the results of the potential of each component to find and suggest some ways of decreasing the total exergy destruction rate in order to have the best design.
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 5 Simulations and Analysis
### Data Split and Metrics  
The dataset for the DJI Phantom 4 was split into a 80-20 configuration with 80% used for training and the remainder 20% for testing of the trained models. The splitting method chosen was based on the Pareto principle wherein an 80/20 rule is adopted to split the dataset into training and testing samples. The following performance metrics were considered, and associated values were generated by the simulator:  
* Accuracy: \(\dfrac{TP+TN}{TP+FP+TN+FN}\) is the number of instances correctly classified given all the instance predictions, where true positive is \(TP\), true negative is \(TN\), false positive is \(FP\) and false negative is \(FN\).
* Recall: \(\dfrac{TP}{TP+FN}\) indicates the number of \(TP\)s compared to the positive class instances. A low recall value represents a high number of attack instances missclassified as normal.
* Precision: \(\dfrac{TP}{TP+FP}\) indicates the number of TPs predicted correctly. A low value of precision represents a high proportion of false positives whereby normal instances are marked as attack instances.
* Area under the curve (AUC): \[\int_{0}^{1}TPR(x)\,dx\] where TPR is the true positive rate and \(x\) is the false positive rate. Values range between 0 and 1.  
Furthermore, the performance of the random forest classifier was evaluated for various values of the max-depth parameter and variable number of estimators. Max-depth and number of estimators are hyperparameters of a random forest classifier that can tuned for identifying the best performing settings. Max-depth controls the tree depth of the random forest, which, in turn, controls the complexity of the generated decision tree. Increasing the tree depth can increase the complexity of the decision tree and can also impact training time. A high value of max-depth can cause the random forest to over-fit it and, hence, it is not a recommended practice [29]. Through our studies, we explored the values of max-depth in the range of two to six and considered the number of estimators to control the number of trees generated for the random forest. The value explored was between 5 and 15.
###### Abstract
However, these attempts have been performed empirically and have not been theoretically verified. Moreover, there are concerns that reducing the number of ray-paths reduces the number of observation equations in the identification and degrades the accuracy of the identified elastic wave velocity distribution. Therefore, we attempt to improve the identification accuracy of the elastic wave velocity distribution by weighting the observation equations based on the potential excitation times instead of reducing the number of ray-paths. This attempt challenges the effective use of unreliable arrival times by determining its reliability based on the potential excitation times and tries to raise the accuracy of the identified elastic wave velocity distribution as much as possible. The elastic wave velocity distribution is expected to be accurately identified via AET with arrival times observed under undesirable conditions, e.g., severely deterioratedmaterials or an observation environment with high-level noise, if the attempt is successfully accomplished.
# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 3 Approach
### Embedding Attention Layer  
Word embedding treats words as the smallest unit and disregards any morphological resemblances between various words, thereby leading to the OOV problem. By contrast, character embedding can operate over individual characters in each word and can, therefore, be useful for  
Figure 1: Main architecture of the network. Word embedding is provided to a Bi-LSTM/GRU. The forward unit represents the word \(X_{t}\) and its left context, whereas the backward unit represents the word \(X_{t}\) and its right context. Concatenating the two vectors yields a representation of the word \(X_{t}\) and its context, and this embedded representation is fed to the classification layer.  
handling OOV. However, research on character embedding is still in the initial phase, and systems that work solely on characters are not superior to those based on words on most tasks [28]. Therefore, character and word embeddings can be integrated to fully utilize their advantages. However, we adopt an embedding attention layer that works as a gate mechanism, can learn similar representations, and allows the model to determine how to consolidate the information for each word. After getting the character feature of each word, we calculate the attention matrix through a two-layer perceptron and combine the two levels of features by a weighted sum as follows:  
\[z=\sigma(U_{a}\text{tanh}(V_{a}x+W_{a}m)), \tag{12}\]  
\[\widetilde{x}=z\cdot x+(1-z)\cdot m \tag{13}\]  
where \(U_{a}\), \(V_{a}\), and \(W_{a}\) are the weight matrices for computing the attention matrix \(z\) and \(\sigma()\) is the sigmoid logistic function with values between 0 and 1. \(x\) and \(m\) are sequence representations of word and character embeddings, respectively. The dimensions of vector \(z\) are the same as those of \(x\) or \(m\) and act as the weight between the two vectors. Accordingly, the model can dynamically determine the amount of information to use from each of the embeddings (character or word).
###### Contents
MPC was introduced as EMS to tackle the issue of the DP algorithm based on a receding-horizon control strategy instead of considering all future information about the driving cycle. MPC works as follows: (1) it calculates the optimal inputs over a prediction horizon to minimize the objective function subject to the constraints; then, (2) it implements the first element of the derived optimal inputs to the physical plant; finally, (3) it moves the entire prediction horizon forward and repeats the first step. Another category that that should be mentioned, apart from rule-based and optimization-based strategies, is learning-based strategies. They use recent advanced data mining schemes for massive historical and real-time information to derive the optimal solution of the control problem. Learning-based algorithms can be incorporated into model-based approaches to tune the control parameters, derive the thresholds for RB EMSs or recognize driver patterns. Some interesting sub-categories of learning-based strategies are reinforcement learning (RL) and neural network learning (NNL). This category is evolving quickly with the development of machine learning (ML).
# A Control-Oriented Linear Parameter-Varying Model of a Commercial Vehicle Air Brake System
## 6 LQR Controller Design
### LQR Controller  
Consider the linear continuous time-invariant system, obtained by fixing the scheduling parameter vector \(\theta\), in the form of  
\[\begin{split}\dot{x}(t)&=Ax(t)+Bu(t)\\ y(t)&=Cx(t)\end{split} \tag{37}\]  
where \(x\in R^{n}\) is the state vector; \(u\in R^{m}\) the control vector; \(A\) the system matrix, \(B\) the input matrix, \(C\) is the output matrix.  
Figure 11: Comparison results between the Linear Parameter-Varying (LPV) model and nonlinear model.  
The LQR control law \(u(t)\) minimizes the following performance cost function Equation (38).  
\[J=\frac{1}{2}\int\limits_{0}^{\infty}[x^{T}(t)Qx(t)+u^{T}(t)Ru(t)]dt \tag{38}\]  
where \(Q=Q^{T}\geq 0\) is the state weighting matrix and \(R=R^{T}>0\) is the control weighting matrix.  
To obtain the optimal feedback gain matrix, the matrix Riccati Equation (39) is solved.  
\[PA+A^{T}P-PBR^{-1}B^{T}P+Q=0 \tag{39}\]  
The optimal control gain can be obtained using Equation (40).  
\[u(t)=-Kx(t)K=R^{-1}B^{T}P \tag{40}\]