esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
###### Abstract  
Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.  
DRFSA; task scheduling; rate of failure; makespan; energy consumption +
Footnote †: journal: Medical Computing  
PSO [4], GA [5], and ACO [6] algorithms, as task scheduling is an NP-Hard problem in cloud computing; these nature-inspired approaches generate schedules with respect to the given workload by considering different conditions, but tasks generated from various heterogeneous resources may still have different run-time capacities, and it is difficult to assign tasks to virtual resources in the cloud. Metaheuristic approaches which generate near-optimal solutions are not able to map tasks to precise VMs. Many tasks fail and single points of failure still occur in the cloud paradigm due to inappropriate assignment of tasks to VMs by schedulers. Therefore, in our research, we propose a novel fault-tolerant aware task scheduler using deep reinforcement learning which is integrated as a DQN model into the scheduler which should be able to assign tasks by itself, checking priorities of both tasks and VMs which are fed to the scheduler which generates schedules dynamically based on rewards generated from the evaluated reward function while minimizing the parameters of makespan, rate of failure, and energy consumption.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 4 Graphene-Based Composites  
Graphene is a 2D planar sheet and is an allotrope of carbon that is organized into a hexagonal lattice as shown in Figure 3. A single graphene sheet has a honeycomb structure, which forms due to the arrangement of a single layer of carbon atoms. When several sheets pile on each other, they form multi-layer graphene. The structure of graphene is such that each carbon atom is attached to the other three carbon atoms which provide better stability and high tensile strength [74; 75].  
Technically, it is a non-metal material but it is deemed a quasi-metal because it displays semi-conducting metal properties. Graphene possesses unique properties which do not exist in other non-metallic materials, therefore making it superlative material for electronic applications usage. The single-layer graphene sheet has 1.0 TPa Young's modulus and can bear stress up to 42 Nm\({}^{-1}\), that is why it is considered as one of the strongest materials available [76]. The electron mobility of graphene in comparison to silicon is 100 times faster and it conducts twice as much heat as diamond. The electrical conductivity of graphene in comparison to copper is 13 times better [77]. The graphene family that has been used as EMI shielding material are presented in Figure 4. The types of graphene are distinguished based on their structure. The modification has been brought into the material by altering the structural properties.  
Among metals, iron has gained much popularity due to its magnificent properties to deal with EMI. In recent years magnetic nanostructures are in demand as they provide good absorption when combined with graphene. In comparison to other metals for EMI  
Figure 4: Graphene family for EMI shielding.  
Figure 3: Graphene structure.  
shielding applications, iron is the most desirable material due to its high natural availability, low facile synthesis cost and high biocompatibility and biodegradability nature [65]. The various types of iron which have been used extensively in the field of EMI are presented in Figure 5. which is adapted from [65].
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 1 Introduction
Further improvements have been achieved with the emergence of bidirectional encoder representations from transformers (BERT), which is a language representation model based on transformers for pre-training deep bidirectional representations from unlabeled text [15]. BERT has been used in many natural language processing (NLP) use cases, including sentiment analysis [16], text summarization [17], question answering [18], and text classification [19]. In this paper, we address the problem of classifying patient comments written in Arabic using DL- and BERT-based models. A real-world data set obtained from the PX center at the Saudi MOH is utilized for this purpose [20]. We formulate the problem as a multi-label classification task and define it as follows: given a patient comment that is written in Arabic language, it must be classified into one or more of the 25 pre-defined classes corresponding to the Saudi Healthcare Complaints Taxonomy (SHCT) [21]. The contributions of this study are as follows: (1) we investigate the existing solutions for classifying PX data in the Arabic language; (2) we utilize real-world PX data obtained from the MOH of Saudi Arabia; and (3) we develop a total of 28 classifiers using deep learning models, bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU), in addition to BERT-based models, to help in classifying the newly received unannotated comments. One of the challenges faced in this research is the complexity of the used data set, which poses various difficulties in the classification process. The challenges associated with the data set include dealing with dialectal Arabic (DA) and modern standard Arabic (MSA) and the presence of misspelled words, which may pose a challenge in the classification process. Additionally, the PX data set suffers from an imbalanced distribution of labels, which is a very challenging matter to deal with in the context of multi-label classification [22]. Moreover, we intend to classify the comments with no consideration of their sentiment. Figure 1 depicts the challenges associated with the PX data set. To the best of our knowledge, the classification of PX comments has not previously been covered in the context of the Arabic language, nor Saudi dialects; however, a number of attempts have been conducted in other languages, revealing an acceptable rate of reliability. The results of the classifications will be in favor of reducing the burden on human resources and delivering insights in a timely manner to decision makers. The remainder of this paper is organized as follows: In Section 2, we discuss previous works related to text classification. Then, we describe our Arabic patient experience data set and detail the proposed deep learning models for PX classification in Section 3. Our results are presented and discussed in Sections 4 and 5, respectively. Finally, Section 6 concludes this paper.
## 4 Evaluation
### Computational Efficiency  
We conducted some benchmarks to evaluate the computational efficiency of the proposed method. For this, an OpenSSL library of the version 1.0.2f is modified by replacing its scatter-gather part with our Permute-SG implementation. We selected this version since it is vulnerable to fine-grained cache attacks [21]. The benchmarks were performed on a server equipped with a Xeon E5-2620v4 processor (Broadwell) and a PC with a Core i7-7820 HQ processor (Kaby Lake). We used a benchmarking tool included in the OpenSSL framework, and measured the speed of the RSA signing and verifying operations for each implementation.  
Table 1 and Figure 3 show the benchmarking results. The terms 'SG' and 'SG-Const' refer to the unmodified OpenSSL libraries of version 1.0.2f and 1.0.2g, respectively. Both have the scatter-gather implementation, of which the SG is vulnerable to fine-grained cache attacks while the SG-Const has a countermeasure with constant-time gather procedure (See Section 2.2.2). In Figure 3, the benchmarking results are illustrated in a relative manner to give an intuitive comparison. The SG shows the fastest performance result, which comes at the cost of lacking the countermeasure against the fine-grained cache attacks. Among the implementations with the countermeasure, the Permute-SG is the fastest in all the benchmarking cases. In Broadwell processor, the Permute-SG shows almost the same performance as the SG, and is 11% faster than the SG-Const for signing operation in RSA 4096-bits. Because of the microarchitectural difference, the Permute-SG shows a little performance degradation in Kaby Lake processor, in which it still outperforms the SG-Const. It is worth noting that in RSA 1024-bits, all the implementations show the same performance, because the scatter-gather is only applied to more than RSA 2048-bits in OpenSSL.
## 1 Introduction
For each link, which day of the week, which 30 min of the day, degree ratio, length ratio between target and adjacent link, speed expectation, speed standard variance among adjacent links, and weather information are regarded as artificial neural network (ANN) input. Travel time ratios between target and adjacent links are the ANN output. Experimental results show that the proposed neural network model can predict link travel time using the relationship between a target and adjacent links. At the same time, different constructed models were used to verify the influence of factors on link travel time prediction. The conclusion of this article is based on the analysis of our previous work [15]. However, this paper mainly predicts link travel time using sparse data incorporating multi-factors and researches their influence on link travel time prediction. The article is outlined as follows. In Section 2, the measurement of spatial correlation between target and adjacent links is detailed. In Section 3, we introduce factors influencing the correlations between target and adjacent links. In Section 4, the artificial neural network (ANN) is presented and discussed. In Section 5, we describe our experiments, including data description, model application, a comparison of models, and sensitivity analysis. A discussion of the results and some conclusions are outlined at the end.
## 1 Introduction
The rescue resource scheduling problem is one of time urgency, and the rescue resources should arrive at the accident location in time. Since time is critical in railway dangerous goods accidents and short response time can help save more properties and lives and reduce the impact of accidents, the rescue work should be started at the first time of incidence. Therefore, the earlier the rescue resources are transported to the accident location, the better the rescue effect. 2. Railway dangerous goods accident has continuous expansibility, and emergency rescue has time window requirements. Expansion of accident is the main feature of railway dangerous goods accidents, and it very easily affects other equipment or facilities and causes accident expansion. The characteristics of accident expansion require that rescue resources must be transported to the accident location within the time window before accident expansion. 3. The traffic environment is uncertain, and rescue time reliability is an important quality performance measure of the rescue resource scheduling problem. Road traffic conditions are more complex and uncertain, which requires that the arrival time of rescue vehicles must be reliable. 4. Railway dangerous goods accidents require coordinated dispatching of multi-rescue resources. The characteristics of railway dangerous goods determine the multiplicity of accident consequences. Therefore, accident rescues of railway dangerous goods generally require multi-rescue resources of fire control, medical and health. Therefore, in the case of a railway dangerous goods accident, transportation of rescue equipment (such as fire truck, fire sprinkler, fire water monitor, emergency lighting equipment, chemical rescue vehicle, special rescue vehicle, medical rescue vehicle, etc.) and resources (such as rescue medicine, air respirator, etc.) to the accident location is urgently required. At the same time, due to the influence of rescue capacity, location, road network, traffic environment and traffic flow of each rescue center, the appropriate rescue resource scheduling and routing plans need to be integrated and reasonably arranged. The remainder of the paper is organized as follows: Some mathematical notations are defined in Section 2.1 and the travel time and reliability of a rescue route are analyzed in Section 2.2. The constraints and objective function are discussed, and then a multi-objective scheduling model of rescue resources is established in Section 2.3. A multi-path algorithm with a bound constraint as the first stage of the two-stage algorithm is proposed in Section 3.1, and the NSGA-II algorithm as the second step is designed in Section 3.2. The proposed algorithm is tested on a regional road network in Section 4. Finally, the conclusions are provided in Section 5.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 5 Conclusions and Future Work  
The concept of security contained within the QoS model is based directly upon the probabilities that adversarial listeners and disruptors are able to gain access to or change the original message. Probabilistic performance for data leakage and data corruption, as defined in the QoS model, provides a foundation to compare the confidentiality and integrity available in a communication network. By combining those probabilistic characteristics and using QoS metrics as a baseline for availability in a network, the QoS model was derived to describe static security characteristics of a point-to-point network. In this paper, those static security characteristics are extended to include complex, multi-hop and multi-channel networks, thus expanding the fidelity of the QoS model to more complex and realistic network architectures. The expanded QoS model allows the repeatable quantification and comparison of the security of a variety of communication system architectures under static configurations. Message fragmentation and duplication, as available features of multi-channel communication, provides a demonstrably improved theoretical performance to counteract the possible security concerns created by multi-hop architectures, thus improving the overall QoS performance. A robust model such as this expanded QoS model requires a fully developed simulation environment that would confirm the modeled results. (Such a simulation environment featuring a real network with all the aspects of the QoS model is currently in development and will be the subject of a follow on paper). This tool must include the specific data-handling protocols, adjustable network characteristics,and performance monitors that display how the network end-points respond. Upon completion of that simulation, the extent of an adversary's influence and the performance of fragmentation and duplication within a multi-channel network may be evaluated.  
Conceptualization, P.M.S. and S.G.; refinement, P.M.S. and S.G.; methodology, P.M.S. and S.G.; software, P.M.S.; validation, P.M.S.; investigation, P.M.S.; resources, P.M.S.; writing--original draft preparation, P.M.S.; writing--review and editing, P.M.S. and S.G.; All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Air Force Institute of Technology, Center for Cyberspace Research (CCR).  
The views expressed in this paper are those of the authors, and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government. This document has been approved for public release.  
All authors declare that they have no conflict of interest.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 3 System Model
### Relay Assisted Models
#### 3.5.2 Analytical Performance of Selective DF Relaying
For example at a \(BER=10^{-3}\) the power penalties are 0.8 dB and 0.75 dB for \(\theta=30^{\circ}\) and \(50^{\circ}\), respectively.
###### Abstract  
In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, multi-user detection (MUD) algorithms play an important role in reducing the effect of multi-access interference (MAI). A combination of the estimation of channel and multi-user detection is proposed for eliminating various interferences and reduce the bit error rate (BER). First, a novel sparse based \(k\)-nearest neighbor classifier is proposed to estimate the unknown activity factor at a high data rate. The active users are continuously detected and their data are decoded at the base station (BS) receiver. The activity detection considers both the pilot and data symbols. Second, an optimal pilot allocation method is suggested to select the minimum mutual coherence in the measurement matrix for optimal pilot placement. The suggested algorithm for designing pilot patterns significantly improves the results in terms of mean square error (MSE), symbol error rate (SER) and bit error rate for channel detection. An optimal pilot placement reduces the computational complexity and maximizes the accuracy of the system. The performance of the channel estimation (CE) and MUD for the proposed scheme was good as it provided significant results, which were validated through simulations.  
channel estimation; evolutionary algorithm; machine learning; multiuser detection +
Footnote †: journal: _technologies_
## 4 Calculation of Various Reliability Indices  
### Reliability of the WSN  
The reliability of the system is the probability that the system will perform adequately under given conditions and for a specified period. Mathematically, it is a probability function \(R:t\rightarrow[0,1]\), which gives the probability of a system that it will work failure-free at a time \(t\). Here, the reliability expression of the WSN is obtained, by using Equation (18), and is shown in Equation (20). Also, the time-dependent reliability expression of the WSN corresponding to a particular value of the failure rate \(\lambda_{su}=0.22\), \(\lambda_{ts}=0.15\), \(\lambda_{m}=0.35\), \(\lambda_{p}=0.03\), \(\lambda_{sp}=0.02\)[17] is given by Equation (21).  
\[R(t)=\frac{(-e^{-(\lambda_{m}+\lambda_{\text{sa}}+\lambda_{\text{bs}}+\lambda_ {\text{m}})t}\lambda_{sp}\ +e^{-\left(\lambda_{sp}+\lambda_{\text{sa}}+\lambda_{\text{bs}}+\lambda_{ \text{m}}\right)t}\lambda_{p})\ (t\ \lambda_{m}+1)}{-\lambda_{sp}+\lambda_{p}} \tag{20}\]  
\[R(t)=100(-0.02\ e^{-0.75t}+0.03e^{-0.74t})(0.35t+1) \tag{21}\]  
The graphical representation of the reliability of the WSN can be obtained by varying the \(t\) in Equation (21). It is shown in Figure 4.  
For more insight about the reliability of the WSN with respect to different failures, the author investigated the component-wise reliability of the WSN.
# Does Two-Class Training Extract Real Features? A COVID-19 Case Study
## 2 Materials and Methods
### Works and Datasets
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Work** & **Method** & **Dataset(s)** & **Data Type** & **Model(s)** & **Classes** & **Accuracy** \\ \hline [PERSON] [17] & DTL & COVID-CT & CCT & ResNet50, Shufflenet, Mobilenet & 2: COVID-19, non-COVID-19 & 85.33\% \\ \hline [PERSON] [8] & DL & COVID-CT & CCT & DeCovNet & 2: COVID-19, non-COVID-19 & 90.10\% \\ \hline [PERSON] [18] & DL & COVID-chestXray & CCT & MODE-based CNN & 2: COVID-19, non-COVID-19 & 93.30\% \\ \hline [PERSON] [19] & DL & COVID-chestXray & CCT & ResNet-18 & 2: COVID-19, non-COVID-19 & 99.40\% \\ \hline [PERSON], [PERSON] [20] & DL & COVID-chestXray & CXR & ResNet152 & 2: COVID-19, other chest diseases & 87.00\% \\ \hline [PERSON], [PERSON] [21] & DL & COVID-chestXray & CXR & nCOVnet using VGG16 & 2: COVID-19, Other & 88.10\% \\ \hline [PERSON], [PERSON] [22] & DL & BIMCV COVID-19+ & CXR & Custom & 2: COVID-19, non-COVID-19 & 91–97\% \\ \hline [PERSON], [PERSON] [6] & DTL & COVID-chestXray & CXR & ResNet18 and SqueezeNet & 2: COVID-19, non-COVID-19 & 92.05\% \\ \hline [PERSON], [PERSON] [23] & DL & COVID-chestXray & CXR & ResNet152, VGG19, DenseNet & 2: COVID-19, non-COVID-19 & 93\% \\ \hline [PERSON] [7] & DL & COVID-chestXray & CXR & ResNet-18 & 2: COVID-19, tuberculosis & 95.00\% \\ \hline [PERSON] [24] & DL & COVID-chestXray, COVID-CRX & CXR & ResNet50 + SVM & 2: COVID-19, non-COVID-19 & 95.40\% \\ \hline [PERSON] [PERSON] [9] & DTL & COVID-CRX & CXR & VGG16 & 3: COVID-19, pneumonia, normal & 86.00\% \\ \hline [PERSON] [25] & DL & COVID-chestXray & CXR & DarkCovidNet & 3: COVID-19, pneumonia, no-findings & 87.00\% \\ \hline [PERSON] [10] & DL & COVID-CRX & CXR & \
\begin{tabular}{c} Inception V3, XceptionNet, ResNeXt \\ \end{tabular} & 3: COVID-19, pneumonia, normal & 93–97\% \\ \hline [PERSON] [26] & DTL & COVID-chestXray & CXR & VGG19 and MobileNet & 3: COVID-19, pneumonia, normal & 97.80\% \\ \hline [PERSON] [27] & DTL & COVID-chestXray & CXR &
\begin{tabular}{c} AlexNet, GoogleNet, ResNet50, DenseNet12,... \\ \end{tabular} & 4: COVID-19, normal, bacterial pneumonia, other viral pneumonia & 95.56\% \\ \hline [PERSON] [10] & DTL & COVID-chestXray, COVID-CRX & CXR & ResNet50 and ResNet101 & 4: COVID-19, normal, bacterial pneumonia, other viral pneumonia & 97.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep Learning based CXR and CCT COVID-19 classification works. After presenting the most widely used datasets and some of the most relevant works, the methodology used in this work to test the quality of the COVID-19 classification systems that only use two classes is described.