esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Course of Sleep Inertia Dissipation in Memory Tasks
###### Abstract  
Sleep inertia (SI) refers to a complex psychophysiological phenomenon, observed after awakening, that can be described as the gradual recovery of waking-like status. The time course of cognitive performance dissipation in an everyday life condition is still unclear, especially in terms of the sleep stage at awakening (REM or NREM-stage 2) and the relative effects on performance. The present study aimed to investigate the SI dissipation in different memory performances upon spontaneous morning awakening after uninterrupted nighttime sleep. Eighteen young adults (7 females; mean age 24.9 \(\pm\) 3.14 years) spent seven non-consecutive nights (one baseline, three REM awakenings and three St2 awakenings) in the laboratory under standard polysomnographic (PSG) control. Participants were tested after three REM awakenings and three St2 awakenings, and three times at 11:00 a.m. as a control condition. In each testing session, participants filled in the Global Vigor and Affect Scale and carried out one memory task (episodic, semantic, or procedural task). For each condition, participants were tested every 10 min within a time window of 80 min. In accordance with previous studies, SI affected subjective alertness throughout the entire time window assessed. Moreover, SI significantly affected performance speed but not accuracy in the semantic task. With reference to this task, the SI effect dissipated within 30 min of awakening from REM, and within 20 min of awakening from St2. No significant SI effect was observed on episodic or procedural memory tasks.  
sleep inertia; memory systems; subjective alertness; time course; sleep 25 February 2021  
Accepted: 6 April 2021  
Submitted: 8 April 2021  
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**Copyright:** 0221 by the authors. License MDPT, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/)]([https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/))).
## 4 Experiment Results and Discussion
### Computational Complexity  
In addition to the qualitative and quantitative experiments described above, we also provide the computational complexity of the various methods in Table 3, specifically in terms of training time and inference time. To ensure a fair comparison, all models were trained and tested using the same settings.  
As shown in the table, ED\({}^{2}\)IF\({}^{2}\)-Net-T achieves the fastest training speed, with a training time of 47 h. Similarly, ED\({}^{2}\)IF\({}^{2}\)-Net-L has a relatively shorter training time of 66 h compared to most other models. In terms of inference time, both ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L outperform other methods, with inference times of 97.64 ms and 144.09 ms, respectively.  
The above comparison of computational complexity highlights the advantages of the proposed ED\({}^{2}\)IF\({}^{2}\)-Net in terms of faster training speed and shorter inference time.  
Figure 7: Visualization of the qualitative ablation studies of ED\({}^{2}\)IF\({}^{2}\)-Net-T. It is best viewed magnified on the screen.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
###### Abstract  
Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2 DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability.  
smart grids; AI; fault prediction; machine learning; deep learning Article
## 4 Experiments and Results  
In this section we present different experiments and their corresponding results, and they are divided into three sets. In the first set of experiments, we developed and applied different techniques plus PCA transformation in order to enhance the presence of fragmentation waves in the ECG. In the second set of experiments, we applied ICA with the same objective. Finally, we benchmarked these techniques from both groups over real fragmented signals.
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 1 Introduction  
Single event effects (SEEs) are unexpected electronic disturbances that occur in integrated circuits (ICs) due to the extra charges generated by ionizing particles or other forms of radiation (e.g., pulsed laser beam) [1, 2]. It is significant to locate the SEE sensitive areas for a certain IC device, especially for modern transistors with feature size of several nanometers. While accelerators are traditionally used as the SEE testing facility [3], pulsed laser, as research has indicated [4, 5], can not only work effectively as a complementary facility, but furthermore, provide more benefits like better spatial and temporal control, as well as being more cost-effective.  
There are two typical laser SEEs testing processes, single photon absorption (SPA) and two photon absorption (TPA), which are described in detail by several studies [5, 6, 7]. Due to the presence of metallayers on the top surface of most IC components, ultra-fast laser pulses are typically injected from the bottom side (i.e., the substrate side) and extra charges would be generated in desired areas by the TPA mechanism. With specifics of laser source, an approximate value of the number of generated charges can be calculated by a deduced equation [7], which is a benefit for SEE laser testing.  
Sensitivity mapping technology is a significant branch of SEE laser testing. There are plenty of studies related to this area, e.g., 2-D mapping technique [8; 9; 10] and 3-D mapping technique [11; 12]. However, most previous research is achieved with the region of interest (ROI) size in sub-millimeter scale and few of work focuses on the ROI with the size in about one micron scale which is even smaller than the spot size of the laser beam. Consequently, this research develops a SEE laser testing system that has high spatial resolution to generate a sensitivity map in a single SRAM cell of about \(1\times 1\)\(\upmu\)m and moreover, has high velocity to scan the entire area of an operational amplifier (op-amp) of about \(1\times 1\) mm in less than 2 min.  
In the laser system located at the University of Saskatchewan, a beam scan module (BSM) containing a pair of mirrors was used to project the laser beam into a certain ROI of a device under testing (DUT). The DUT was held stationary on a supporting stage with the laser scanning over it, while most of the other laser systems apply a different structure that keeps the laser beam still and moves the supporting stage. Two research results are presented in this study, (1) sensitivity maps of a one mm\({}^{2}\) scale op-amp can be attained in a short time; (2) sensitivity maps of a one \(\upmu\)m\({}^{2}\) scale SRAM cell can be attained with a step size of 0.18 \(\upmu\)m.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 4 Performance Evaluation and Lessons Learnt  
### Evaluation Metrics  
For fair comparison, traditional performance metrics, such as true positive (TP), true negative (TN), false negative (FN), false positive (FP), false alarm rate (FAR), precision, F1-measure, recall, area under receiver operating characteristics (AUC), executive time, and sensitivity, was used. Suffice it to say that a good detection system should have high level of accuracy, precision, recall and F1-measure with low false alarm rate. The accuracy is computed as (6):  
\[Accuracy=\frac{TP+TN}{(TP+TN+FN+FP)}, \tag{6}\]  
On the other hand, FAR is given as (7):  
\[FAR=\frac{FP}{(TN+FP)}, \tag{7}\]  
Precision, recall, F1- measures, and sensitivity are given as (8)-(11):  
\[Precision=\frac{TP}{(TP+FP)}, \tag{8}\]  
\[Recall=\frac{TP}{(TP+FN)}, \tag{9}\]  
\[F1-measure=\frac{2*(Precision*Recall)}{(Precision+Recall)}, \tag{10}\]  
\[sensitivity=100*Recall(\%). \tag{11}\]  
In addition, the Root Mean Squared Error (RMSE), defined as the difference between the actual output and predicted or trained output was used. It is given as  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(A_{i}-P_{i})^{2}}, \tag{12}\]  
Figure 9: Summary of the parameters of the proposed CNN.  
where \(A_{i}\) represents the actual or observed or known value, and \(P_{i}\) represents the predicted value. The RMSE compares the actual and predicted values to determine the level of closeness between them. The smaller the value of RMSE, the closer predicted and actual values.
## 5 Data Set
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(U\), \(n\) & Underlying population, \(U=\{1,\ldots,n\}\) with \(n\in\mathbb{N}\) being the size of the population \(U\) \\ \(S\), \(m\) & Sample population, i.e., CNM data set, \(S\subseteq U\) with \(S=\{1,\ldots,m\}\) and \(m\leq n\) \\ \(y_{U}\) & Values of population \(U\) (e.g., all download throughput values in \(U\)) \\ \(y_{S}\) & Values of all measurements in \(S\) (e.g., all measured download throughput values in \(S\)) \\ \(\Theta(y_{U})\) & Characteristic to be evaluated on \(U\) (e.g., mean value) \\ \(T(y_{S})\) & Estimator function of the given characteristic \(\Theta\) using \(y_{S}\) \\ \(\overline{y}_{U}\), \(\overline{y}_{S}\) & Population mean of \(U\), resp. \(S\) \\ \(p(S)\) & Measurement plan on \(S\), assigns a measurement probability to each sample, \(p:S\rightarrow[0,1]\) \\ \(\sigma(\Theta)\) & Standard deviation of a specific evaluation or characteristic \(\Theta\) \\ \(\sigma(\overline{y}_{U})\) & Standard error of the mean (SEM), see Equation (1) \\ \(s\) & Sample standard deviation, see Equation (2) \\ \(CI_{a}\) & Confidence interval with a significance level of \(\alpha\), see Equation (3) \\ \(z_{g}\) & Quantile function for probability \(\beta\) for a given distribution (e.g., Normal or Student’s \(t\) distribution) \\ \(t^{*}\) & Target precision (e.g., \(\delta^{*}=100\,\text{kbit/s}\) or \(\gamma^{*}=0.01\)) \\ \(\delta^{*}\) & Target precision as maximum absolute difference \\ \(\gamma^{*}\) & Target precision as maximum relative difference \\ \(n_{abs}^{min}(\delta^{*})\) & Minimum number of measurements to achieve an absolute precision of \(\delta^{*}\), see Equations (4) and (5) \\ \(n_{rel.}^{min}(\gamma^{*})\) & Minimum number of measurements to achieve a relative precision of \(\gamma^{*}\), see Equations (6) and (7) \\ \(q\) & Target precision type (\(q=abs.\) for absolute precision, resp. \(q=rel.\) for relative precision) \\ \(Val.\ Score_{prec.}(t^{*})\) & CNM Precision Validity Score for target precision \(t^{*}\), see Equation (10) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key variables and notations used in the paper.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Cloud Security Alliance (STAR)
#### 3.4.1 Strengths  
1. Standardized Assessment: The CSA STAR framework provides a standardized way to evaluate CSP security posture. It offers a standardized set of control objectives and criteria that organizations may use to evaluate and compare various cloud service providers.
2. Transparency and accountability: The framework encourages CSPs to give thorough information about their security, data handling methods, and regulatory compliance. Customers may thus make educated judgments when purchasing and using cloud services.
3. Third-Party Certification: The CSA STAR program provides a certification mechanism for CSPs to certify compliance with the CSA's security principles and best. This accreditation gives clients peace of mind about the security of their selected cloud services.  
Collaboration among Industry Experts: The CSA STAR framework is developed through collaboration among industry experts, ensuring that it contains a diverse variety of viewpoints and experiences. This contributes to the development of a comprehensive and strong framework for addressing numerous security risks in cloud computing.
## 4 Numerical Modeling and Methodology  
The ACPH, initially introduced by [PERSON] et al. [15], represents a modular steel assembly intended for bolted connections at the extremities of precast beams, thereby establishing a moment-resisting junction with the adjacent column. The fundamental concept underlying this apparatus is to localize inelastic deformation within a replaceable steel fuse region, consequently safeguarding the surrounding concrete and embedded reinforcement during seismic events.  
The ACPH is composed of two external Q345-grade steel plates that offer confinement and shear transfer capabilities, enclosing a pair of 3 mm-thick Q235-grade steel plates situated at the upper and lower sections of the beam cross-section. These thinner plates serve as sacrificial energy-dissipating fuses, experiencing yielding in both tension and compression under cyclic lateral loads. The plates are fastened through high-strengthbolts to end plates secured at the beam and column interfaces. The typical offset between the ACPH centerline and the column face is established at 300 mm, as confirmed in the experimental investigation conducted by [PERSON] et al. [15]. A schematic representation of the configuration is provided in Figure 2.  
The modular design facilitates the post-earthquake replacement of the steel plates by loosening a limited number of bolts, thereby preventing damage to the primary concrete framework. This connection methodology is congruent with resilience-based seismic design principles by promoting localized plasticity, enabling swift inspection, and minimizing repair durations. The overall dimensions of the ACPH subassembly in this research correspond to those employed in the experimental benchmark (ACPH-1-300), which are elaborated upon in the subsequent finite element modeling.  
The numerical analysis described in this section focuses on developing a sophisticated simulation framework that accurately represents both the macro and micro behaviors of precast beam-column connections subjected to substantial dynamic loads. The main goal is to assess the feasibility of a replaceable plastic hinge, referred to as the ACPH, across various precast frame designs. Although existing experimental research supports the effectiveness of hinge-based connections at the subassembly level, it is crucial to conduct advanced computational simulations to extrapolate these results to intricate multi-story or multi-bay structures.
## 4 Results and Discussion
### Quantitative Analysis
In addition to this, the numerical analysis of different feature engineering techniques with the Bi-GRU model on the Rossmann and Walmart datasets is presented in Table 5. By analyzing Table 5, we can see that the Bi-GRU model with feature engineering techniques (APSO+RFE+MRMR) obtains superior retail sales forecasting results compared to the other combinations with the Bi-GRU model by means of the NRMSE, ND, RMSSE, MAE, R2, and MSE. The active features selected from APCO+RFE+MRMR includes four major benefits: (i) improves Bi-GRU model's performance by learning meaningful patterns in the acquired datasets; (ii) reduces overfitting risk; (iii) improves computational efficiency, where the transformed features need fewer computational resources; and (iv) improves Bi-GRU model's interpretability. As mentioned in the earlier sections, the selection of active features diminishes the processing time of the Bi-GRU model to 20.11 s and 30.12 s, respectively, in the Rossmann and Walmart datasets and reduces the model's complexity to linear. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Citadel POS Dataset**} \\ \hline
**Forecasting Models** & **Measures** & **Without Feature Engineering** & **With Feature Engineering** \\ \hline \multirow{8}{*}{Bi-GRU} & NRMSE & 0.34 & 0.09 \\  & ND & 0.31 & 0.08 \\ \cline{1-1}  & RMSSE & 0.28 & 0.08 \\ \cline{1-1}  & MAE & 0.27 & 0.07 \\ \cline{1-1}  & MSE & 0.25 & 0.06 \\ \cline{1-1}  & R2 & 0.85 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feature Engineering Techniques**} & \multirow{2}{*}{**Measures**} & \multicolumn{2}{c}{**Rossmann**} & **Walmart** \\ \cline{3-4}  & & & **Bi-GRU** & \\ \hline \multirow{8}{*}{APSO} & NRMSE & 3.78 & 3.64 \\  & ND & 3.42 & 3.32 \\ \cline{1-1}  & RMSSE & 3.30 & 3.22 \\ \cline{1-1}  & MAE & 3.16 & 3.08 \\ \cline{1-1}  & MSE & 3.02 & 2.93 \\ \cline{1-1}  & R2 & 0.80 & 0.88 \\ \hline \multirow{8}{*}{RFE} & NRMSE & 2.80 & 2.72 \\ \cline{1-1}  & ND & 2.74 & 2.62 \\ \cline{1-1}  & RMSSE & 2.48 & 2.31 \\ \cline{1-1}  & MAE & 2.40 & 2.35 \\ \cline{1-1}  & MSE & 2.06 & 2.12 \\ \cline{1-1}  & R2 & 0.88 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Numerical analysis of different feature engineering techniques on the Rossmann and Walmart dataset.
## 4 Experiment
### Entity Description Information Fusion  
Our model aggregates neighborhood information based on the graph attention mechanism, and the graph attention method is more sensitive to initial embedding. In order to improve the stability of the model, we extracted text description information as initialization vector representation. We conducted experiments to compare the impact of random initialization and text description initialization on model performance, then we conducted three sets of experiments to analyze the impact of fused text description information on model performance by changing the initial embedding method. We call the model that randomly initializes entity vectors KTAT-R. For the groups that integrate entity description information, two methods were used; the first method embeds the words of the entity description statement into the vector for average operation, and reduces the dimensionality to a fixed length vector through pooling, called KTAT-A. The second method concatenates the word vectors in the description statement into a matrix, and using the method mentioned earlier, we use two-layer convolution to treat the vector obtained from the convolution as the initial embedding of the entity, which is called KTAT-C. Our experimental results on the FB15K-237 dataset are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MR** & **MRR** & **Hits@10** & **Hits@3** & **Hits@1** \\ \hline TransE & 357 & 0.294 & 0.465 & - & - \\ ComplEx & 339 & 0.247 & 0.428 & 0.275 & 0.158 \\ DistMult & 254 & 0.241 & 0.419 & 0.263 & 0.155 \\ ConvE & 244 & 0.325 & 0.501 & 0.356 & 0.237 \\ RotatE & **177** & 0.338 & 0.533 & 0.375 & 0.241 \\ RGCN & 600 & 0.164 & 0.30 & 0.181 & 0.10 \\ KTAT & 182 & **0.498** & **0.625** & **0.424** & **0.355** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of link prediction on FB15k-237 datasets.  
From the graph, we find that using text description information to initialize the entity embedding vector can achieve better results than using random initialization embedding in Hit@1, Hit@3, and Hit@10, with the KTAT-C method outperforming the other two methods in all indicators. This indicates that integrating text description information can effectively improve the robustness and stability of the model, and the convolution methods outperform the average methods.
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 2 Background
### Cyber-Attacks and Security Risks
Man-in-the-middle (MITM), also known as eavesdropping, occurs when the intruders successfully include themselves inside a two-party transaction or communication. The most common entries for MITM attackers are:  
* Unsecured public WiFi, where intruders insert themselves between a visitor's device and the network. * If an attacker's malware successfully breaches the victim's system, they can install software to gain the victim's secure information. Denial-of-service (DDoS) is a type of attack that involves shutting down a network or service with a high volume of traffic to deplete resources and bandwidth, resulting in the system being unable to fulfill legitimate requests. DDoS attacks are frequently designed to target high-profile businesses' web servers, such as trading platforms, media, finance and government. SQL injection (SQLI) aims to employ malicious code to manipulate back-end database access information that was not intended for display. Intruders could carry out an SQL injection simply by submitting malicious code into a vulnerable website search box. A zero-day exploit attack refers to the threat of an unknown security vulnerability for which a fix has not yet been provided or about which the program developers are uninformed. To detect this threat, the developers require constant awareness. DNS tunneling uses the DNS protocol to communicate non-DNS traffic over port 53 by sending HTTP and other protocol traffic over DNS. Since using DNS tunneling is a common and legitimate process, its use for malicious reasons is very often overlooked. Attackers can disguise outbound traffic as DNS, concealing data that are shared through an internet connection.