esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Model
### Ransomware Detection: ELM Model  
For ransomware detection, the ELM classifier is used. In various fields, the ELM method is used for pattern recognition, wind speed forecasting, and fault diagnosis. The ELM method involves three layers: input, output, and hidden layers [20]. Figure 2 showcases the framework of ELM. By using the backpropagation model, the feedforward neural network must upgrade the weight in the iteration method; however, the ELM mechanism has a random initial weight that does not need to be upgraded in the iterative method. Consider that \(T=\{(c_{i},\ e_{i})|c_{i}\in R^{n},\ e_{i}\in R^{n}\}\) denotes the training instances and \(F(.)\) indicates the activation function. The ELM method is formulated below:  
\[\begin{split}\sum_{i=1}^{N_{b}}\alpha_{j}F\big{(}\rho_{j}c_{1}+ \theta_{j}\big{)}&=e_{1}\\ \sum_{i=1}^{N_{b}}\alpha_{i}F(\rho_{i}c_{2}+\theta_{i})& =e_{2}\\ \sum_{i=1}^{N_{b}}\alpha_{i}F(\rho_{i}c_{N_{i}}+\theta_{i})& =e_{N_{i}}\end{split} \tag{15}\]  
Figure 2: Architecture of ELM.  
Here, \(\theta_{i}\) signifies the threshold of the hidden node; \(\alpha_{i}\) signifies the weight linking the output and hidden nodes; \(\rho_{i}\) shows the weight connecting the input and the hidden node; \(c_{i}\) signifies the input sample of the module; \(e_{i}\) denotes the output sample of models; and \(N_{s}\) characterizes the sample count:  
\[Y\alpha=e \tag{16}\]  
\[Y=\begin{bmatrix}F(\rho_{1}c_{1}+\theta_{1})&\dots&F\big{(}\rho_{N_{h}}c_{1}+ \theta_{N_{h}}\big{)}\\ \vdots&\ddots&\vdots\\ F(\rho_{1}c_{N_{h}}+\theta_{1})&\dots&F\big{(}\rho_{N_{h}}c_{N_{h}}+\theta_{N_ {h}}\big{)}\end{bmatrix} \tag{17}\]  
\[\alpha=[\alpha_{1},\ \cdots,\ \alpha_{N_{h}}]^{T} \tag{18}\]  
\[e=[e_{1},\dots,e_{N_{h}}]^{T} \tag{19}\]  
Here, \(e\) denotes the sample label matrix; \(Y\) indicates the hidden layer output matrixes; \(\alpha\) indicates the weight matrix; and \(N_{h}\) shows the node count in a hidden state. The hidden layer threshold and input weight of the ELM algorithm are determined randomly. In the iteration model, the weight vector does not need to be modified; only the output weight needs to be evaluated and is given below:  
\[\alpha=\left(Y^{T}Y\right)^{-1}Y^{T}e \tag{20}\]  
The ELM predictive method can be attained.  
\[e=\sum_{i=1}^{N_{h}}\alpha_{j}F(\rho_{i}c+\theta_{i}) \tag{21}\]  
The computation method of ELM is given as follows:  
1. Define the model sample;
2. Initialize the hidden layer threshold and input weight randomly;
3. Compute the output matrix;
4. Resolve the output weight of the hidden layer.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)
### Mathematical Formulation  
Let \(\mathcal{M}=(S,A,T,R,\gamma)\) be an MDP where  
* \(S\) is a finite state space;
* \(A\) is a finite action space;
* \(T:S\times A\times S\rightarrow[0,1]\) is the transition function;
* \(R:S\times A\times S\rightarrow\mathbb{R}\) is the reward function;
* \(\gamma\in[0,1)\) is the discount factor.  
The similarity function \(f:(S\times A)^{2}\rightarrow[0,1]\) satisfies  
\[f(s,a,s,a)=1\quad\forall(s,a)\in S\times A \tag{3}\]  
\[f(s_{t},a_{t},s,a)=f(s,a,s_{t},a_{t})\quad\text{(symmetry)} \tag{4}\]  
The similarity function can be implemented using various metrics (e.g., cosine similarity for feature vectors, Gaussian kernels for continuous spaces). The specific choice depends on the environment's characteristics and should reflect meaningful relationships between states.
# Graphene Nanoplatelets
## 1 Structure, Morphology, Properties and Behavior  
Graphene is regarded as the revolutionary material of the 21 st century. It is a single graphite monolayer, whose thickness is one atom (0.34 nm) while its lateral size could be several orders of magnitude larger. Its synthesis is complex and cannot be mass-produced yet. For this reason, graphene nanoplatelets (GNPs) have become an alternative, with a low cost and exciting properties, and the potential for large-scale production. GNPs have few graphite layers, varying in thickness from 0.7 to 100 nm [1].  
Their main properties are light weight, high aspect ratio with planar shape, good mechanical properties and excellent thermal and electrical conductivities, together with low cost and easy manufacture. GNPs have numerous applications as isolated materials, neat coatings and fillers of composites. This Special Issue is focused on the use of graphene nanoplatelets as nanofillers [2; 3; 4].
## 1 Introduction
### Critical Challenges  
The efficient and robust realization of such large, highly-dynamic, complex, non-conventional networking environments is _a challenging algorithmic and technological task_. Features including the huge number of sensor devices involved, the severe power, computational and memory limitations, their dense deployment and frequent failures, pose _new design, analysis and implementation aspects_ which are essentially different not only with respect to distributed computing and systems approaches but also to ad-hoc networking techniques.  
We emphasize the following characteristic differences between sensor networks and ad-hoc networks:  
* The number of sensor particles in a sensor network is extremely large compared to that in a typical ad-hoc network.
* Sensor networks are typically prone to faults.
* Because of faults as well as energy limitations, sensor nodes may (permanently or temporarily) join or leave the network. This leads to highly dynamic network topology changes.
* The density of deployed devices in sensor networks is much higher than in ad-hoc networks.
* The limitations in energy, computational power and memory are much more severe in sensor networks.  
Because of the above rather unique characteristics of sensor networks, efficient and robust distributed protocols and algorithms should exhibit the following critical properties:  
Scalability  
Distributed protocols for sensor networks should be highly scalable, in the sense that they should operate efficiently in extremely large networks composed of huge numbers of nodes. This feature calls for an urgent need to prove by analytical means and also validate (by large scale simulations) certain efficiency and robustness (and their trade-offs) guarantees for asymptotic network sizes.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Network Infrastructure
#### 3.1.4 Fault-Tolerant Networking  
The core components for improving fault tolerance include diversity, where some diverse fault-tolerance options result in the backup not having the same capacity level as the primary component. The redundancy can be imposed at a system level, which means an entire alternate computer system is in place in case a failure occurs. Replication involves multiple identical versions of systems and subsystems and ensures their functions always provide similar results.  
In fault-tolerant networking, the network must adapt to continue operating without interruption in the case of failures that involve a loss of connection between nodes or their shutdown. It can be addressed with two different approaches: one is to consider making nodes more robust to failures. The other is to design a network topology with redundant intermediate nodes and connections [39].
# Regularized Instance Embedding for Deep Multi-Instance Learning
## 2 Related Work
### Classical MIL Methods  
For MIL, many scholars carried out long-term research and made good progress. MIL was first proposed to solve the problem of drug molecular activity [3; 12]. Then, many machine-learning models have emerged to solve the multiple-instance-learning problem, such as linear support vector machines (SVM) [33] that are applied in MI-SVM and mi-SVM for bag- and instance-level classification, respectively. Citation-kNN [34] solves the MIL problem in a lazy way which simply uses a k-nearest neighbor (kNN) classifier and various distance metrics. Fretcit-kNN [35] employs the minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label in the task of web recommendation. G3P-MI [36] solves MIL from a perspective by utilizing grammar-guided genetic programming. MI-Kernel [37] introduces kernel methods into MIL. EM-DD combines the expectation-maximization algorithm with the diverse density (DD) algorithm for MIL [38]. Multi-instance Fisher Vector (miFV) [39] is the representative algorithm to solve multi-instance learning problems from the perspective of embedded space, mapping instance features into a high-dimensional space through a pretrained Gaussian model and Fisher Vector coding. Besides Fisher Vector coding, the vector of locally aggregated descriptors (VLAD) is also applied in [39] for MIL and the method is called miVLAD. The multi-instance dissimilarity (MInD) method [40] uses bag similarities to classify bags. [PERSON] et al. [8] proposed a classical multiple-instance learning work. They proposed a non-independent and identically distributed method to handle instance from the bag and utilize the relationship between instances, and a mi-Graph algorithm to provide better multi-instance learning performance. Inspired by this work, we aimed to improve the quality of instance embedding by using instance relationships. Unlike mi-Graph that directly compares bags without considering instance embedding, we could also use instance embedding as the backbone of multiple-instance learning, and further distinguish positive and negative instances in the bag. Besides, mi-Graph is a non-deep learning method while our method is based on deep neural networks.
## 2 Previous Works
### OBIA-Deep Learning Features Fusion  
Feature fusion is another approach to integrating OBIA and deep learning. This method combines attributes of OBIA and deep features of deep learning after extracting each feature set separately. The additional deep features utilized in this technique offer advantages over the first method. This approach is often implemented as a two-branch computational network, which contains a processing chain to perform segmentation and OBIA feature extraction and a network to learn deep and abstract features from the data. After combining the two feature sets, a classifier such as tree-based models or support vector machines (SVM) is used to obtain class labels for the image pixels [8; 9] ([PERSON] 2017, [PERSON] 2019). [PERSON] et al. (2020) [10] combined SVM and CNN to perform the classification of high-resolution remote sensing images, aiming to improve classification accuracy. [PERSON] et al. (2020) [11] used the common multiscale segmentation algorithm to extract multiscale low-level image features and a CNN to obtain deep features from the low-level features at each scale, respectively. An approach to extract tree plantations from very high-resolution remote sensing images was proposed by [PERSON] et al. (2020) [12]. They used an integrated OBIA-CNN framework to achieve that. They performed image segmentation to obtain OBIA features and a fine-tuned CNN to obtain deep features. To reduce the computation time of the model, they conducted feature selection based on the Gini index. The tea objects were then classified by a random forest (RF). The basic problem of this integration method is the heavy computations and the requirements of hardware resources [13] ([PERSON] et al. 2021). Other problems associated with this integration method include duplication in some features extracted by OBIA and CNN, such as shape, texture, and color.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Datasets
#### 3.1.1 Undirected Networks  
The first undirected network dataset comes from a reality mining project at the Massachusetts Institute of Technology (MIT) in 2004, where the actors were tracked with the help of their personal smartphones to study interpersonal interaction. It contains human contact and interaction data among 100 students over nine months. In this undirected network, an actor in the network represents a person, and a link indicates physical contact between two persons. The second dataset comes from internal email communications among employees of a mid-sized manufacturing company where actors represent employees and links represent individual emails between two employees. This dataset was collected from Network Repository as an undirected dynamic network. The next dataset in this category contains undirected network data from a Facebook-like social network originating from an online community for students at the University of California, Irvine, where actors represent students within the community and links represent messages communicated among them. The last undirected network dataset is a very small subset of the total 'Facebook' friendship graph where an actor represents a Facebook user, and a link represents a friendship between two users. For the sake of brevity, we name these four networks as \(G_{MIT}\), \(G_{Email}\), \(G_{UCI}\), and \(G_{FF}\) to denote the network originated from the MIT reality project, a small manufacturing company, University of California Irvine and real Facebook Friendship, respectively, in the rest of the study. In these network datasets, links are date stamped with individual dates, and the smallest temporal granularity of these networks is a day. Therefore, three different sliding window sizes (i.e., \(\tau=1,7\) and 30 days) were considered for sampling the longitudinal networks. This will help this study to emulate daily, weekly and monthly dynamic networks.
## 2 Methodology
### Beddoes-Leishman Semi-Empirical Model
#### 2.3.2 Unsteady Separated Flow  
The non-linear effects of the trailing-edge separation are implemented using a Kirchhoff flow model. The movement of the unsteady flow separation point position is related to the static separation position via a deficiency function. The onset of the leading-edge separation, and hence the dynamic stall, is identified using a criterion based on the attainment of a critical local leading-edge pressure, which is further related to the normal force.  
Under unsteady conditions, there is a lag in the leading-edge pressure with the increasing AOA, which can be expressed as a first-order lag as given by the following:  
\[C_{n}^{\prime}=C_{n}^{p}-D_{p} \tag{8}\]  
where  
\[C_{n}^{p}=C_{n}^{c}+C_{n}^{\rm nc} \tag{9}\]  
The constant \(T_{p}\) can be determined empirically from unsteady airfoil data. The effective AOA \(\alpha_{f}=C_{n}^{\prime}/C_{n\alpha}+\alpha_{0}\) after incorporating the unsteady pressure response is used to obtain the effective flow separation point \(f^{\prime}\). In this study, \(f^{\prime}\) is determined by look-up-table interpolation from the static airfoil data. The additional effects of a boundary layer response are considered as a first-order lag as given by the following:  
\[f^{\prime\prime}=f^{\prime}-D_{f} \tag{10}\]  
where  
\[D_{f}(s)=D_{f}(s-\Delta s)\exp\Bigl{(}-\Delta s/T_{f}\Bigr{)}+\bigl{[}f^{ \prime}(s)-f^{\prime}(s-\Delta s)\bigr{]}\exp\Bigl{(}-\Delta s/2T_{f}\Bigr{)}. \tag{11}\]  
The constant \(T_{f}\) can be determined empirically from unsteady airfoil data. Then, the non-linear normal force coefficient is obtained by the Kirchhoff flow theory  
\[C_{n}^{f}=C_{n\alpha}\Biggl{(}\frac{1+\sqrt{f^{\prime\prime}}}{2}\Biggr{)}^{2 }(\alpha-\alpha_{0}) \tag{12}\]  
where \(\alpha_{0}\) is the zero-lift AOA. Afterwards, the chordwise force coefficient is obtained by [PERSON] and [PERSON]'s approximation [11] as given by the following:  
\[C_{n}^{\prime}\leq C_{n\lambda_{0}f}=\left\{\begin{array}{cc}C_{n\alpha} \sqrt{f^{\prime\prime}}\alpha\sin\alpha\\ K_{1}+C_{n\alpha}\sqrt{f^{\prime\prime}}f^{\prime\prime}{}^{\Phi}\alpha\sin \alpha\end{array}\right.\begin{array}{cc}C_{n}^{\prime}>C_{n1}\end{array} \tag{13}\]  
where \(C_{n1}\) is the critical normal force value and the leading-edge separation is initiated when \(C_{n}^{\prime}>C_{n1}\). The parameter \(K_{1}\) and \(\Phi\) are obtained from the static test data [11]. The pitching moment coefficient is obtained by fitting the ratio \(C_{m}/C_{n}\) in a least-square method from the static airfoil data as given by the following:  
\[C_{m}^{f}=C_{m0}+[k_{0}+k_{1}(1-f^{\prime\prime})+k_{2}\sin(\pi f^{\prime \prime}{}^{\Phi})]C_{n}^{f} \tag{14}\]  
where \(C_{m0}\) is the zero-lift pitching moment coefficient. The values of \(k_{0}\), \(k_{1}\), \(k_{2}\), and \(\mu\) can be adjusted for different airfoils.
# A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
## 1 Introduction
The most substantial breakthrough in deep learning was in 2006, when [PERSON] and [PERSON] [18] introduced the Deep Belief Network (DBN) with multiple \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **Shape** & **Sound** & **Unicode** \\ \hline Fatha & \(\sim\) & /a/ & 064E \\ Damma & \(\sim\) & /u/ & 064F \\ Kasra & \(\sim\) & /i/ & 0650 \\ Fathatan & \(\sim\) & /an/ & 064B \\ Dammatan & \(\sim\) & /un/ & 064C \\ Kasratan & \(\sim\) & /in/ & 064D \\ Sukoun & \(\sim\) & None & 0652 \\ Shadda & \(\sim\) & & Doubling & 0651 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Primary Arabic diacrities. layers of Restricted Boltzmann Machines (RBMs). In general, a DBN is a probabilistic generative model that is pretrained using a greedy layer-wise method. In this greedy layer-wise algorithm, the DBN learns one layer at a time in an unsupervised way, and then undergoes fine-tuning via supervised learning with backpropagation. DBNs were introduced as a solution for the dilemmas encountered when using traditional neural networks trained in deep architectures, such as slow-paced learning, becoming stuck in local minima, and demanding a lot of training data for ameliorate learning. Deep learning is a rich family of methods, encompassing Convolution Neural Network (CNN), Auto-Encoders (AE), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Generative Adversarial Nets (GAN). While deep learning is extensively used in NLP, only few studies have applied it to Arabic text diacritization. These studies mostly use a hybrid system of deep learning and statistical or rule-based approaches. For example, in [19], which is recently published research, a multi-components system for automatic Arabic diacritization was proposed, the system uses an LSTM with rule-based corrector. This work provides the first attempt to implement and examine the performance of the DBN to automatically diacritize Arabic text. Unlike previous studies, this work does not employ any prior morphological or contextual analysis nor requires post-processing of data. The remainder of this paper is organized as follows: Section 2 provides a review of related work from literature on Arabic text diacritization. Section 3 provides a detailed background on the main concepts addressed in this work. Section 4 describes the dataset used. The proposed methodology is detailed in Section 5. Section 6 outlines the experimental settings and the results. Finally, Section 7 summarize the findings of this work.
# Landscape Ecology Analysis of Traditional Villages: A Case Study of Ganjiang River Basin
## 3 Results
### Extraction of Environmental Elements in Traditional Villages
#### 3.1.2 Terrain Elements and Distribution Characteristics  
Utilizing the buffer tool in ArcGIS to delineate a 1 km radius buffer for the 161 traditional villages in the study area and calculating the average elevation within each village buffer area using statistical tools to represent the overall terrain features surrounding the villages (Figure 3d-f). Within the river basin, there are 87 traditional villages located at a distance of 0-2000 m from rivers, accounting for 54.04% of the total. These villages have a close relationship with the rivers (Figure 4a). The highest average elevation of all villages is 707.26 m, and the lowest is 13.86 m. There are 80 villages with an average elevation below 100 m, accounting for 32.92%; 41 villages with an average elevation between 100 and 200 m, accounting for 26.71%; 36 villages with an average elevation between 200 and 500 m, accounting for 34.78%; and 4 villages with an average elevation above 500 m, accounting for 5.59% (Figure 4b). The highest average slope across all villages is 24.65\({}^{\circ}\), and the lowest is 1.99\({}^{\circ}\). There are 64 villages with an average slope below 5\({}^{\circ}\), accounting for 39.75%; 65 villages with an average slope between 5 and 10\({}^{\circ}\), accounting for 40.37%; 25 villages with an average slope between 10 and 15\({}^{\circ}\), accounting for 15.53%; and 7 villages with a slope above 15\({}^{\circ}\), accounting for 4.35% (Figure 4c). The average undulation for all villages is 8.91\({}^{\circ}\), with the highest value being 13.64\({}^{\circ}\) and the lowest value being 2.47\({}^{\circ}\). Among these, 49 villages have an average undulation below 5\({}^{\circ}\), accounting for 30.43%; 89 villages have an average undulation between 5 and 10\({}^{\circ}\), accounting for 55.28%; and 23 villages have an average undulation above 10\({}^{\circ}\), accounting for 14.29% (Figure 4d). The terrain features of traditional villages in the Ganjiang River Basin are closely related to the spatial heterogeneity of the area. Villages located in the central plain region of the basin tend to  
Figure 3: Collection of environmental elements of traditional villages: (**a**) rivers and basin range; (**b**) river buffer range; (**c**) land use distribution; (**d**) elevation distribution; (**e**) slope distribution; (**f**) terrain roughness distribution.  
have lower elevations and gentler terrains, while traditional villages in the southern hilly area have higher average elevations and steeper slopes.
## 2 Background and Related Works
### Related Works
#### 2.2.2 Explainability for Output/Reward Components  
When a reward function considers multiple factors, the reward value can be decomposed by exposing its calculation [7]. [PERSON] et al. decomposed the reward while disentangling the representation without domain knowledge [19]. These methods did not consider the contribution of every input feature, while our work connects it to the reward components. [PERSON] et al. implemented reward decomposition and used the difference between Q-values of the contrasted actions to explain the agent's decision [8]. They introduced the concept of minimal sufficient explanation (MSX) to compress the contrastive explanation. [PERSON] et al. in [20] used this concept to generate contrastive explanations by implementing an embedded self-prediction model via general value functions (GVFs) [21] that are located between two NNs used by the agent. In the above methods, all components are treated as the same (having equal weights, i.e., 1) and only final reward components values are obtained. Our method decouples the reward weights and Q-functions approximators, allowing each component to be treated equally and shows the weights explicitly. It uses the contributionsof the input features to each reward component to explain why an action is preferred over the other. Since our method generates a more granular explanation than earlier methods, we also propose a method to compress it. Another work by [PERSON] et al. proposed a method to learn the reward weight using the counterfactual inverse RL algorithm [22] while our method maintains a static weight that encourages transparency during training.