esa-sceva/satcom-chunk-collection huggingface dataset:
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 4 Experiment  
### Dataset  
The dataset used in the experiment comes from the public hard disk dataset on the BackBlaze official website. BackBlaze has been compiling and reviewing hard disk data since 2013, posting quarterly and year-end statistics on hard disk usage from various manufacturers and comparing them with previous years to draw conclusions. They also made the original hard disk dataset available for viewing and research.  
The data items included in the BackBlaze hard disk dataset are shown in Table 2. Among them, date, serial number, model, capacity and failure or not of a hard disk are always present and have the same meaning for all hard disks, while the following S.M.A.R.T. feature sets vary by the hard disk model. Because different hard disk manufacturers have different definitions and emphasis on their own hard disk S.M.A.R.T. features, it is normal that the same S.M.A.R.T. features have different meanings for hard disks of different manufacturers. Therefore, on the premise that the failed samples should not be too small and the disk failure rate should be within the normal range, this experiment selects the  
Figure 4: Blending ensemble learning method framework.  
ST4000 DM000 hard disk of the Seagate Company for more than six years from 2016 to 2022 as the experimental object. The statistical data of some years are shown in Table 3.  
Since the year 2022 has not yet ended, only the data of the first two quarters, namely from January to June, were collected in 2022, with a total of 268 failed samples, and the annual failure rate is not yet known.  
As can be seen from the statistics, the annual failure rate of ST4000 DM000 disk data in these six years is basically around 2%. According to statistics, the annual failure rate of hard disks under normal circumstances should be between 0.5% and 2%. Therefore, the reason for choosing this model of hard disk is that its failure rate is always at a high level, the failed samples are more abundant, and the number of data meets our requirements.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.4 Assumptions on the Cycles of the Streams  
Some of the trivial assumptions found in the literature are that all flows are cyclic, as in [120], or that the cycles of all the streams, among other stream information, are known precisely, as in [66]. Those assumptions are not explicitly mentioned in the rest of the works because, as shown in Table 3, TAS is mainly needed for HTS traffic which is by definition cyclic, and the streams information, including the cycles, is necessary to calculate a schedule. However, several assumptions are made regarding the cycle itself. Refs. [24, 37, 58, 72, 79, 86] assumed that all the streams have the same cycle. This assumption reduces the length of the GCL since it is built for a single cycle (equal to the cycle of all the streams) without the need to check the overlap of the frames' transmission in the subsequent cycles. Nevertheless, this assumption limits the applicability of the TSN scheduling algorithm for specific use cases, as in general, different flows may have different cycles. Ref. [124] aimed to reduce the length of the GCL differently by assuming that the cycles of the streams are all multiples of the GCL cycle. Another type of assumption is relating the cycles of the streams to their deadlines. For example, the authors of [24, 50, 102, 103] assumed that the deadlines of the streams are equal to their cycles, and the authors of [39, 74] assumed that the deadlines of the streams are smaller than their cycles. Although these assumptions are not always valid, authors use them to avoid the coexistence of different instances of the same stream in the network, as that could be a reason for intrastream overlapping.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 3: Performance Evaluation under High-Density Nodes Scenario (100 Nodes)
#### 5.3.1 Throughput Performance Analysis  
Figure 18 shows the throughput of 100 nodes as the first 5000 milliseconds represent the time at which the application starts. In the malicious scenario, the next 4972 milliseconds showed a positive like to 1.78 megabits/s in throughput followed by a steady decrease to 0.391 megabits/s at 68,851.81 milliseconds. The framework scenario showed a positive like to about 3.68 megabits/s in throughput followed by a steady increase to 4.79 megabits/s at 99,727.63 milliseconds. This is the highest recorded throughput throughout all the simulations conducted despite the high node density. The high jitter levels contribute to the late arrival of packets and low levels of throughput in the sinkhole scenario. The high node density environment means that many nodes compete for access to resources and the unavailability of queuing mechanisms would mean higher traffic congestion in the network. This does not mean that having fewer nodes in the network might be the solution as literature reveals that OLSR performs better in high node densities. The detection and isolation of the sinkhole attack in the network is the best solution to network performance as done with the framework scenario. The QoS mechanism implemented optimizes the use of the available bandwidth and traffic prioritization.  
Figure 17: Network model of 100 nodes with two sinkhole nodes added.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods
### Data Description  
The data used in this study were provided by the Shandong Provincial Big Data Bureau of China and focused on power system load data for a regional power grid in Shandong Province, China, spanning almost three years. The raw dataset includes total active power, industry type (business, big industry, non-general industry, general industry), significant weather data, and more. The data were collected at a frequency of 15 s, resulting in 600,000 data samples. Of these, 520,000 samples were used as input for the DCC-EL framework for training, and 80,000 samples were reserved for validation. We re-encoded the weather data to better align with the model input, using a ten-day window length for prediction.
## 3 Methods and Materials
### Methods
#### 3.2.1 Crash Severity  
In addition to crash rates, crash hot spot zones are classified based on their severity index. The crash severity index determines the weight of a single crash. So, to accurately identify high or low clustered zones, it is important to incorporate crash severity index in hot spot analysis. The concept of crash severity index is accrediting the higher values to severer crashes based on the expenditures. Many past studies associated crash severity weights in crash hot spot analysis, for instance, [PERSON] et al. [51] used severity index developed by the Belgian government by giving values of 5, 3 and 1 for fatal, serious injury and slight injury, respectively. Because Ethiopia lacks a traffic crash costing platform for particular crash severity levels, the crash severity index developed by the Roads and Traffic Authority of New South Wales [52] was utilized in this study. In this system, each crash incident is provided a value of 3.0 for fatal, 1.8 for serious injury, 1.3 for slight injury and 1.0 for property damage only crashes. The crash severity index of each zone can be computed by Equation (1) [52].  
\[SI=3.0\cdot X_{1}+1.8\cdot X_{2}+1.3\cdot X_{3}+1.0\cdot X_{4} \tag{1}\]  
where \(X_{1}\) is fatal crashes, \(X_{2}\) is serious injury crashes, \(X_{3}\) is slight injury crashes, \(X_{4}\) is Property-damage-only crashes.
## 3 Results  
### Chemical Profile of EOs  
The chemical composition of our CEOs was evaluated by GC-MS, and their individual chemical substances are listed in Table 1. The amounts of volatiles in percentage for each class of compounds are presented in Table 2. From the findings, it is evident that the identified substances represented 98.9%, 94.8%, and 99.1% of the oils from _C. cassia_ bark, _C. verum_ bark, and _C. verum_ leaf, respectively. Non-terpenic compounds were abundant in the conception of the CCEO (94.8%) and CVBEO (51.0%), even though the CCLEO showed phenylpropanoids (72.9%) as the main constituents. Concretely, the major substances were shown to be (_E_)-cinnamaldehyde(77.1%), (_E_)-o-methoxy cinnamaldehyde (8.5%), and (_E_)-cinnamyl acetate (3.0%) in the CCEO; (_E_)-cinnamaldehyde (44.1%), eugenol (23.5%), and (_E_)-caryophyllene (3.9%) in the CVBEO; eugenol (70.8%), benzyl benzoate (3.9%), and (_E_)-caryophyllene (3.5%) in the CVLEO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound \({}^{a}\)**} & \multirow{2}{*}{**EO1 \({}^{b}\)**} & \multicolumn{2}{c}{**Sample**} & \multirow{2}{*}{**EO3 \({}^{d}\)**} & \multirow{2}{*}{**RI \({}^{e}\)**} & \multirow{2}{*}{**RI \({}^{f}\)**} \\  & & & & \% \% \% & & & \\ \hline
1 & \(\alpha\)-thujene & tr \({}^{h}\) & / j & tr & 926 & 930 \\
2 & \(\alpha\)-pinene & 0.6 & 0.8 & 1.8 & 938 & 939 \\
3 & camphene & 0.1 & 0.2 & 0.2 & 948 & 954 \\
4 & benzaldehyde & 0.9 & 0.2 & 0.2 & 958 & 960 \\
5 & sabinene & tr & tr & 0.2 & 977 & 975 \\
6 & \(\beta\)-pinene & 0.3 & 0.3 & 0.4 & 980 & 979 \\
7 & \(\beta\)-myrcene & tr & 0.2 & 0.3 & 992 & 990 \\
8 & \(\alpha\)-phellandrene & / & 0.2 & 0.6 & 1004 & 1002 \\
9 & \(\delta\)-3-carene & / & / & tr & 1009 & 1011 \\
10 & \(\alpha\)-terpinene & / & 0.4 & 0.1 & 1016 & 1017 \\
11 & \(p\)-cymene & 0.3 & / & / & 1023 & 1024 \\
12 & \(\alpha\)-cymene & / & 2.2 & 1.7 & 1026 & 1026 \\
13 & \(\alpha\)-limuence & 0.7 & 1.0 & 1.1 & 1028 & 1029 \\
14 & 1,8-cineole & 2.3 & 2.9 & 3.0 & 1033 & 1031 \\
15 & salicylic aldehyde & 0.4 & / & / & 1043 & 1044 \\
16 & (_E_)-\(\beta\)-ocimene & / & / & tr & 1047 & 1050 \\
17 & \(\gamma\)-terpinene & 0.2 & 0.3 & 0.3 & 1060 & 1059 \\
18 & acetophenone & tr & / & / & 1063 & 1065 \\
19 & \(\alpha\)-terpionlene & tr & 0.1 & tr & 1088 & 1088 \\
20 & linalool & / & 2.1 & 2.5 & 1098 & 1096 \\
21 & \(\alpha\)-thujene & tr & / & / & 1101 & 1102 \\
22 & phenyl ethyl alcohol & 0.7 & / & / & 1110 & 1108 \\
23 & camphor & 0.2 & tr & tr & 1148 & 1146 \\
24 & benzyl acetate & / & tr & tr & 1160 & 1162 \\
25 & iso-menthone & / & / & tr & 1162 & 1162 \\
26 & benzenepropanal & 0.5 & / & / & 1165 & 1163 \\
27 & borneol & tr & tr & / & 1170 & 1169 \\
28 & 4-terpinenol & tr & 0.3 & 0.2 & 1178 & 1177 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical profile of tested EOs.
# Semantic Scene Graph Generation Using RDF Model and Deep Learning
## 3 Proposed System
### Scene Graph Expansion Using Inference  
The existing RDF graph introduced in the RDFS has the RDF entailment rule [21] as a constraint. Essentially, it has a structure that infers a sentence from the presence of another sentence. The entailment rule considered in this study was the RDF entailment rule derived from the RDF semantics, as shown in Table 1. For example, Rule 11 shows the subClassOf relation as a transitive closure. If resource U is a subclass of resource V, and resource V is a subclass relation of resource X, automatically resource U has a subclass relation of resource X. Although this extended entailment rule and data type entailment rule are provided, additional entailment rules are not considered in this study.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Rule** & **If E Contains** & **Then Add:** \\ \hline
1. & X A Y & A rdf:type rdf:Property \\ \hline
2. & A rdfs:domain X & \\  & U A Y & U rdf:type X \\ \hline
3. & A rdfs:range X & V rdf:type X \\  & Y A V & V rdf:type X \\ \hline
4. & U A B & U rdf:type rdfs:Resource \\  & B A U & U rdf:type rdfs:Resource \\ \hline
5. & U rdfs:subPropertyOf V & U rdfs:subPropertyOf X \\  & V rdfs:subPropertyOf X & \\ \hline
6. & U rdf:type rdf:PropertyOf & U rdfs:subPropertyOf U \\ \hline
7. & A rdfs:subPropertyOf B & U B Y \\  & U A Y & U B Y \\ \hline
8. & U rdf:type rdfs:Class & U rdfs:subClassOf rdfs:Resource \\ \hline
9 & U rdfs:subClassOf X & \\  & V rdf:type U & V rdf:type X \\ \hline
10. & U rdf:type rdfs:Class & U rdfs:subClassOf U \\ \hline
11. & U rdfs:subClassOf V & U rdfs:subClassOf X \\ \hline
12. & U rdf:type & \\  & rdfs:ContainermembershipProperty & U rdfs:subProperty rdfs:member \\ \hline
13. & U rdf:type rdfs:Datatype & U rdfs:subClassOf rdfs:Literal \\ \hline \hline \end{tabular}
\end{table}
Table 1: RDFS entailment rules.  
In this study, we exploited the RDFS entailment rule to generate a semantic scene graph. Additionally, user-defined rules were applied for inference in this study. Although user-defined rules were not defined in the RDFS entailment rules, they enable users to create their own rules and apply them to image annotations. However, inference based on the existing RDFS entailment rules is not well-adapted to context-specific inference because the predefined rules are a limitation. Therefore, the user can infer a triple that represents an image annotation after defining the inference suitable to the situation in advance. For example, if the annotation information on the location of the image taken using a mobile device is \"Seoul\" and there is a triple <Seoul, isPartOf, Korea>, the location of the image includes \"Korea.\" Additionally, user-defined rules that can represent general situations were applied in this study, as shown in Table 2. Although [22] also proposed semantic inference rules in the domain of fuel cell microscopy, [22] assists users in constructing rules with domain-specific features, such as colors and shapes.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 1 Introduction
Furthermore, the exploration of quantum machine learning [41; 42; 43; 44; 45; 46] has opened a new path for processing and analyzing data, harnessing the unique computational capabilities of quantum systems. Among the studies, the study by [PERSON] et al. [47; 48] innovated in the quantum machine learning domain by introducing Quantum LSTM (QLSTM) and Quantum GRU (QGRU), integrating variational quantum circuits (VQCs) with recurrent neural network architectures. Designed for Noisy Intermediate-Scale Quantum (NISQ) devices, these quantum models utilize quantum entanglement to enhance learning efficiency and stability. Their research showcases that QLSTM and QGRU outperform traditional LSTM models in terms of learning efficiency and convergence stability, marking a significant step in sequential data learning within the quantum computing sphere. However, the scenarios examined in [PERSON]'s studies [47; 48] only involved singular ODEs, which prompted us to explore the application of QRNN to systems of ODEs. In this research, we explore the application of Quantum Long Short-Term Memory (QLSTM) and Quantum Gated Recurrent Unit (QGRU) models, based on the Variational Quantum Circuits (VQCs) developed by [PERSON] et al. [47; 48], to complex dynamical systems described by ODEs. Our approach involves a nuanced modification to the VQC, thereby refining its compatibility with QLSTM and QGRU models for their application in this context. Our focus is on the Van der Pol oscillator, two coupled damped harmonic oscillators, and the Lorenz equations. We conduct a comparative analysis of these quantum models against their classical counterparts, LSTM and GRU, focusing on error metrics and loss evolution across epochs. This comparison aims to evaluate their effectiveness in modeling and predicting the dynamics of nonlinear and chaotic systems. The structure of this paper is organized as follows: Section 2 presents an overview of fundamental concepts in quantum computing, including qubits, superposition, quantum gates, and entanglement. In Section 3, we introduce the computational models under investigation, namely variational quantum circuits, classical Long Short-Term Memory (LSTM), classical Gated Recurrent Unit (GRU), Quantum Long Short-Term Memory (QLSTM), and Quantum Gated Recurrent Unit (QGRU). Section 4 introduces three numerical experiments conducted to evaluate these models, including the Van der Pol oscillator, two coupled damped harmonic oscillators, and the Lorenz equations system. Section 5 is the discussion and Section 6 is the conclusion.
# Ventilation Strategies to Ensure Thermal Comfort for Users in School Buildings: A Critical Review
## 1 Introduction
Therefore, it is essential to identify ventilation strategies, whether natural or mechanical, to ensure both air quality and user thermal comfort. In this study, **indoor air quality** is assessed through **carbon dioxide (CO\({}_{2}\)) concentration**, which is commonly used as a representative indicator of ventilation effectiveness and overall air quality in occupied spaces [30; 31]. Elevated CO\({}_{2}\) levels are associated with insufficient ventilation, and controlling this parameter indirectly helps maintain adequate levels of other indoor pollutants. Furthermore, since proper ventilation is a key strategy to reduce airborne virus transmission, monitoring and limiting CO\({}_{2}\) concentrations also contributes to minimizing this risk [32; 33]. As for thermal comfort, it is assessed based on temperature and relative humidity, following the criteria established in the ISO 7730 standard [34]. Reviews jointly addressing these topics--ventilation strategies in schools and thermal comfort--have been sought. However, despite the extent of this problem, few reviews have been found which present a set of ventilation strategies and their results regarding thermal comfort. In the Scopus and WoS databases, only seven reviews have been found discussing both topics combined, compared to 40 reviews found addressing the topics separately; 29 of these focus on ventilation strategies, albeit only guaranteeing CO\({}_{2}\) levels and air quality rather than thermal comfort, while 11 emphasized thermal comfort analysis without establishing or quantifying ventilation strategies, only analyzing the causes. As we have seen previously, this lack of reviews addressing both topics jointly may be due to the current context following the COVID-19 pandemic, where studies have primarily focused on air quality to safeguard human health by preventing disease transmission and exposure to pollutants. In addition to showing us the lack of reviews to conduct an umbrella review study, this initial search and analysis of studies highlights the need for a review of articles addressing these topics to search for ventilation strategies and analyze their results, strengths, limitations, and weaknesses in order to create protocols for use and evaluation in schools.
## 4 Theoretical Background and Research Model
### Coping-Appraisal  
The process of coping-appraisal assesses the capability to cope with a threat and prevent it. Factors encompassing the process of coping are efficacy variables (self-efficacy and perceived response efficacy) and perceived response cost [52].  
Self-efficacy is the person's perceived capability to perform the adaptive response. Self-efficacy in the context of our study is defined as users' belief in their ability to protect data stored in their smartphones from unauthorized disclosure, erasure, modification, destruction, and lack of availability. Self-efficacy has been discovered to have a significant illustrative impact on both technological usage and cybersecurity behavior in respect to personal information security practices [70]. Considering the above, we hypothesize that:  
**Hypothesis 3 (H3).**_Self-efficacy positively affects users' intention to use BBCA technology._  
Perceived Response efficacy refers to the perception that the adaptive response will be effective. In the context of our research, Perceived Response efficacy refers to the users' belief that using BBCA technology will effectively protect their data. Thus, we hypothesize that:  
**Hypothesis 4 (H4).**_Perceived Response efficacy positively affects users' intention to use BBCA technology._  
As mentioned above, some users are not comfortable with the concept of continuous monitoring. Another common issue raised against the use of continuous authentication technologies concerns power consumption. In the context of our research, we consider that users may perceive as a response cost the possibility to get lower quality of services by their smartphones if they decide to adopt BBCA. Thus, perceived response costs are all costs regarding the adoption of adaptive coping response (e.g., financial, time, effort). In this research, we argue that users would express stronger intentions to adopt BBCA, if they consider the costs of performing protective behaviors to be low.  
Therefore, we posit the following hypothesis:  
**Hypothesis 5 (H5).**_Response costs negatively affect users' intention to use BBCA technology._
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 5 Evaluation
### Performance Evaluation  
#### 5.2.1 Intrusion Detection in Different Scenarios  
In order to confirm that the performance of RDFID is independent of scenarios, we first evaluate the system in different rooms. In addition, we compare the system with two other device-free human  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Volunteers** & **Gender** & **Height (cm)** & **Weight (kg)** & **Age** \\ \hline
1 & male & 174 & 63 & 30 \\
2 & male & 175 & 70 & 27 \\
3 & male & 170 & 62 & 27 \\
4 & female & 163 & 51 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of volunteers.  
Figure 6: The modified receiver.  
Figure 7: Confusion matrix of intrusion detection.  
detection systems, FRID and PADS. When constructing the training set, we use the combination of the data from the three scenarios to form six groups of training set and we name them a, b, c, ab, ac, and bc, respectively, according to Figure 7, and all training sets contain the three moving patterns. Datasets that are opposite to the training sets are used as test sets, which are bc, ac, ab, c, b, and a, respectively. To ensure the reliability of the evaluation, each training set is equally divided into five parts, and five experiments are conducted in which the classifier is trained using each part respectively. The result is the mean of the five experiments. The window size in these experiments is 5 s. The FN and FP rate of the three approaches is shown in Table 2. As indicated in the table, the FN rate of RDFID in different scenarios is around 2%, which is the lowest among the three approaches. The FN rate of PADS is affected more significantly by the selection of the training set because it uses SVM as its classifier, the support vectors in different scenarios are not the same. As a result, the FN rate of PADS is higher. As FRID does not need training data, the estimation of the parameters has particular influence on the performance of human detection.  
The FP rate of RDFID is lower than the other two approaches. Most of the FP rate is around 2%, which indicates RDFID generates less false alarms when detecting intruders. PADS uses phase information in CSIs that is more sensitive to environmental changes; therefore, it achieves the highest FP rate among the three approaches.  
Figure 8 indicates the PD of the approaches in different scenarios. It can be seen from the figure that RDFID achieves the most stable and lowest probability of detection.  
It can be seen from the results that the detection performance of RDFID is independent of scenarios. The detection model trained in one scenario can be adapted to other scenarios directly in a relative high detection performance.