esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Heart Rate Variability and Blood Glucose Monitoring  
In this section, we discuss the literature that investigated the association of HRV with BGLs with a focus on the use of HRV as a non-invasive measure of BGL.  
[PERSON] et al. combined HRV and pulse oximeter data, achieving a balanced accuracy of up to 82.1% in predicting high glucose levels. [PERSON] et al. documented inverse correlations between HRV parameters, such as SDNN and LF/HF ratio, and fasting glucose levels in diabetic populations [13]. [PERSON] et al. observed that prolonged hyperglycemia reduces HRV, suggesting its potential as a marker for long-term glucose regulation [14].  
[PERSON] et al. demonstrated that specific HRV parameters, such as SDRMSSD-3, achieved 86.52% accuracy in distinguishing between good and poor glucose regulation, providing evidence of HRV's utility for classifying glycemic control levels [17]. Also, [PERSON] et al. reported that higher glycemic fluctuations were associated with significantly lower HRV parameters, particularly SDNN, in patients with type 2 diabetes and coronary heart disease. These findings underscore the clinical implications of HRV for managing diabetes and cardiovascular complications [16].  
Furthermore, [PERSON] et al. used HRV-based artificial neural networks (ANN) and support vector machines (SVM) to classify diabetic conditions in rats, achieving classification accuracies above 95%. This study highlights the feasibility of HRV features for BGL estimation and diabetes prognosis in controlled environments [21].  
Although these studies highlight the promise of HRV for glucose monitoring, issues including individual variability, small dataset sizes, and the complex relationship between HRV and BGL levels still need to be addressed.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 3 Results  
In this section, the applicability of assembling winds during storms is assessed, and the validation of WW3-simulated waves against the products of SWIM is discussed. Fur thermore, a comparison between the WW3 result forced by assembling wind and ERA-5 wind is presented.
# Smart Digital Forensic Readiness Model for Shadow IoT Devices
## 3 Conceptual Model for Digital Forensic Readiness in Shadow IoT Devices  
Despite the existence of a number of IoT digital forensic readiness models, there is not yet any forensic readiness model for shadow-inclusive networks. Therefore, this paper proposes a conceptual digital forensic readiness model for organizations whose networks may become connected to and/or infected by shadow IoT devices. The proposed model presented here complies with the guidelines stipulated in the ISO/IEC 27043:2015. This ISO (International Standards Organization) standard presents incident investigation principles and processes, although not directed at shadow IoT devices.  
The proposed Shadow Internet of Things Digital Forensic Readiness (SIoTDFR) model for digital forensics is divided into six distinct stages (A-F) (see Figure 1).  
Stage A is the device connection stage, while stage B addresses device identification to detect when a shadow IoT device connects to an enterprise network. Stage C deals with the monitoring of the identified shadow IoT devices. Stage D presents the digital evidence collection process that guides how potential digital evidence can be gathered from shadow IoT devices. Stage E describes the potential digital evidence preservation stage for the SIoTDFR model. Lastly, stage F focuses on a secure storage of digital evidence to ensure the integrity of the gathered digital evidence for the purpose of forensic readiness. Each of the components is discussed in detail next.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis  
### Correctness Analysis  
#### 6.1.1 The Correctness of CSP Outsourcing Decryption  
After the CSP receives the outsourced decryption key from DU, it calculates  
\[\begin{split} CT^{\prime}&=\prod_{x}(C_{1,x}\cdot \frac{e(H(uid),C_{3,x})}{e(SK_{O},C_{2,x})}\cdot e(upk,C_{4,x}))^{t_{x}}\\ &=\prod_{x}(e(g,g)^{s_{p^{\prime}(x)}\cdot u_{x}}\cdot\frac{e(H(uid),g^{c_{x}} \cdot g^{s_{p^{\prime}(y)}\cdot u_{x}})}{e(UAK_{p^{\prime}(x),uid},g^{t_{x}})} \cdot e(g^{usk},g^{c_{x}}\cdot g^{y\cdot\lambda_{x}}))^{t_{x}}\\ &=\prod_{x}(\frac{e(g,g)^{s_{p^{\prime}(y)}\cdot u_{x}}\cdot e(H(uid),g ^{c_{x}})\cdot e(H(uid),g^{\beta_{p^{\prime}(y)}\cdot u_{x}})\cdot e(g^{usk},g^ {c_{x}})\cdot e(g^{usk},g^{y\cdot\lambda_{x}})}{e(g^{s_{p^{\prime}(y)}},g^{u_ {x}})\cdot e(H(uid)^{\beta_{p^{\prime}(x)}},g^{u_{x}})})^{t_{x}}\\ &=\prod_{x}(e(g,g)^{usk\cdot y\cdot\lambda_{x}}\cdot e(H(uid),g )^{c_{x}}\cdot e(g,g)^{usk\cdot c_{x}})^{t_{x}}\\ \end{split} \tag{7}\]  
\[\begin{split}\text{Since }\lambda_{x}=M_{x}v^{T},c_{x}=M_{x}h^{T},v^{T}=(s,r_{1},r_{2},\ldots,r_{m})^{T},h^{T}=(0,h_{1},h_{2},\ldots,h_{m})^{T }\\ &\lambda_{x}\cdot t_{x}=M_{x}t_{x}\cdot v^{T}=(1,0,\ldots,0) \cdot(s,r_{1},r_{2},\ldots,r_{m})^{T}=s;\\ & c_{x}\cdot t_{x}=M_{x}t_{x}\cdot h^{T}=(1,0,\ldots,0)\cdot(0,h _{1},h_{2},\ldots,h_{m})^{T}=0\end{split} \tag{8}\]  
Therefore, the outsourced decryption ciphertext \(CT^{\prime}=e(g,g)^{usk\cdot y\cdot s}\), and the CSP outsourcing decryption algorithm satisfies the correctness.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology
### Conflict-Free DODAG Construction (Algorithm 2)
#### 4.2.2 Greedy, Priority-Based, and Descending Reconfiguration  
A greedy, priority-based, and descending reconfiguration approach is proposed. The process begins at the BR and progresses rank by rank, configuring the necessary nodes in sequence (ranks of the 'new' DODAG are indicated in Figure 4). Initially, nodes without parents must be configured. This descending method ensures uninterrupted message transmission to the BR by maintaining an active path between the nodes in the \(old\) DODAG and the BR at each step.
## 3 Numerical Analysis of the Thermal Performance
### Approximation of the Temperature Fields  
Calculating the temperature fields on the rake face, the main flank face and the minor flank face is indispensable for evaluating the quantity of heat that dissipates into the air. In this work, the temperature fields of these surfaces were obtained via a numerical simulation platform of Ansys CFX. In order to acquire the temperature field distribution, finite element orthogonal simulation is performed to acquire the relationship of cutting depth and contact area temperature when cooling liquid velocity was fixed in 80 mL/min. Assume that the generated heats in cutting process are all dissipated through chip, tool and workpiece. Select the working fluid as water. The interface between internal channel and tool-holder is set as the \"Fluid-Solid Interface\". The interface between insert and tool-holder is set as the \"Solid-Solid Interface\". The simulation parameters are listed in Table 2. Figure 3 illustrates the tool temperature contours at one experimental point. The contact area was set as 1.0 mm \(\times\) 0.1 mm. The temperature on the contact area was 1000 \({}^{\circ}\)C. It can be inferred from Figure 3 that the maximum value and the greatest gradient of the temperature fields on the three surfaces occur simultaneously at the tool nose. In order to summarize the temperature field mathematically, fitting methods will be used in this study.  
\begin{table}
\begin{tabular}{c l} \hline
**Variables** & **Meaning** \\ \hline \(c_{t},\rho_{t},V_{t}\) & the heat capacity, mass density, and volume of the insert \\ \(c_{t},\rho_{l},V_{l}\) & the heat capacity mass density and volume of the cooling liquid \\ \(T_{insert}\) & the averaged temperature of the insert \\ \(q\) & the heat flux on contact area \\ \(A_{c}\) & the area of the tool-chip contact surface \\ \(q_{rf},q_{f_{l}},q_{f_{2}}\) & the heat that dissipates into air through the rake face, the main flank face and the minor \\ \(q_{tl},q_{th}\) & the heat that conduct into the channel and the tool-holder, respectively \\ \(T_{o}\) & the temperature of outlet of the internal cooling channel \\ \hline \end{tabular}
\end{table}
Table 1: Physical meaning of the variables in Equation (1).
## 2 Radiation in Space and Its Effects on Electronic Components
### COTS Electronic Components in Space  
Space exploration has historically been associated with substantial development costs and lengthy timelines. However, the integration of commercial off-the-shelf components has emerged as a transformative approach to mitigate these challenges, showcasing their significance in enabling cost-efficient and rapid mission execution. The integration of commercial off-the-shelf components has reshaped the landscape of space exploration. Notable missions, spanning planetary exploration and ISS resupply, have harnessed COTS technology to reduce development costs and accelerate mission timelines. However, it is imperative to acknowledge the technical and reliability challenges associated with adapting commercial components for the space environment. The Mars Science Laboratory mission (2012) in the propulsion and parachute systems included COTS components, ensuring a precise and safe landing on the Martian surface using a Doppler velocity sensor. Aboard the ISS, we can find COTS stand-alone sensors that measure H\({}_{2}\)O, CO\({}_{2}\), and O\({}_{2}\) levels in the station (2023). The Ingenuity helicopter (2021), which operates on the surface of Mars, is also equipped with two cameras and a Telecom module (TCB) that relies on commercial off-the-shelf components. In this instance, the selection of COTS components is grounded in their compatibility with military, automotive, or industrial operating temperature ranges.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 2 Evolution of the Proposed Modified Quadratic Boost Converter
### Proposed Modified Quadratic Boost Converter with Integrated Dual Voltage Lift Cell  
The generalized circuit diagram of the proposed converter is depicted in Figure 5. The studied high gain boost converter consists of two inductors, L1 and L2, five capacitors C1, C2, C3, C4, C0, and six diodes D1, D2, D3, D4, D5, D6, and most importantly, a single power MOSFET switch S1. The first voltage boost cell consists of four components, i.e., L1, C1, D1, D1, whereas the second integrated voltage boost cell consists of five components, i.e., L2, C2, C4, D3, and D4. Both the configuration during two-mode of operation is discussed in a later section. In order to calculate the ideal voltage gain of the proposed converter, it was assumed that all the passive components were lossless and ideal. Additionally, the diode forward voltage drops and the parasitic resistance were ignored for calculating the ideal voltage gain, whereas, in the later section, the non-ideal voltage gain was calculated considering the diode forward voltage drop. Details of the characteristics of the two-mode operation are discussed as follows.
## 4 Data Processing and Features
### Machine Learning  
In the discipline of computer science known as \"machine learning,\" programmatic learning is made possible. Algorithms for machine learning may be broken down into  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Article** & **Prediction Model** \\ \hline
[10] & ARMA, LSTM \\ \hline
[30] & LSTM and Random Forest \\ \hline
[19] & NLP and ML \\ \hline
[21] & GRU and RL \\ \hline
[22] & CNN and Deep RL \\ \hline
[34] & LSTM, DRL \\ \hline
[57] & Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBost, ANN, RNN, and LSTM \\ \hline
[37] & LSTM, RNN, KNN, LR, RF, DT, GBT, ABT NB, LDA, QDA, SVC \\ \hline
[1] & ML, DL, RL, Deep RL \\ \hline
[33] & LM, RF, SVM, ANN \\ \hline
[7] & ML, DL, RL, Deep RL \\ \hline
[36] & CNN, LSTM, Deep RL \\ \hline
[61] & SVM, RF, ANN \\ \hline
[65,74,76] & SVM, ANN \\ \hline
[68] & MLP, DBN, SAE, RNN, LSTM, GRU, CART, NB, RF, LR, SVM \\ \hline \hline \end{tabular}
\end{table}
Table 10: List of combined or multiple or hybrid models used for stock market prediction.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Article** & **Prediction Model Used** \\ \hline
[25,27] & ANN \\ \hline
[24,29] & LSTM, GRU \\ \hline
[3,18,26,54] & LSTM \\ \hline
[20] & GRU \\ \hline
[47,73] & Deep Neural Network \\ \hline
[64] & CNN \\ \hline
[69] & RNN \\ \hline
[75] & MLP, dynamic artificial neural network \\ \hline \hline \end{tabular}
\end{table}
Table 8: List of standard deep learning models used for stock market prediction.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Article** & **Prediction Model Used** \\ \hline
[2,31,44,63,72] & RL \\ \hline
[4,9,23,32,35,39,41,42,43,46,48,55,56,60,67,70,71] & Deep RL \\ \hline
[40] & RL-Q-Learning \\ \hline
[58] & RL-Deep Q-Learning \\ \hline
[59] & RL-Deep Q-Learning \\ \hline
[46] & Gated Deep Q-Learning \\ \hline
[8] & Deep Direct RL \\ \hline \hline \end{tabular}
\end{table}
Table 9: List of standard Reinforcement Learning models used for stock market prediction.  
two broad classes. Supervised learning is one kind, whereas unsupervised learning is another. To train a machine learning model, one must first provide both the method and the training data for the model to use in determining its parameters. Various types of machine learning models in the prediction of the stock market. They are Autoregressive Integrated moving average (ARIMA) [10], Linear Regression [28], Logistic Regression (LR) [51], Decision Tree (DT) [37; 49; 57], Random Forest (RF) [30; 37; 49; 57; 66; 68] Support Vector Machine (SVM) [33; 50; 51; 61; 65; 68; 74; 76], and k-Nearest Neighbor (kNN) [37].
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Monitoring Data Processing
#### 4.1.3 Relational Analysis of Displacement Components and Influencing Factor Components  
In order to verify the rationality of the influencing factor selection after decomposing the displacement data and the influencing factor data, it was necessary to analyze the correlation between the displacement data components and the influencing factor components. Gray relational degree theory is an evaluation method in gray system theory [48]. It can compare the relational degrees between different sequences and is suitable for studying the problems of insufficient data, poor information, and uncertainty [49]. The gray relational degree model holds that, when the resolution coefficient \(\rho\) is 0.5, the closer the value of the correlation degree \(r\) is to 1, and the better the correlation is between the two variables. In order to ensure the effectiveness and reliability of the data selection, gray relational analysis was used for the displacement component data and the influencing factor component data when the number of training sets was 20 weeks, 40 weeks, 60 weeks, and 80 weeks. The results are shown in Table 2. When the data volume of the training set was 40 weeks, the data in the periodic component dataset and the random component dataset both showed a strong correlation. It can also be seen from the table that the degree of correlation between the displacement component data and the influencing factor data had little to do with the type of influencing factor, but was mainly related to the number of weeks of the training set contained in the dataset; this proves that the selected influencing factors had a strong correlation with the displacement data.  
Figure 7: Decomposition results of the influencing factors: (**a**) decomposition result of PI; (**b**) decomposition result of P2; (**c**) decomposition result of P3; (**d**) decomposition result of P4.
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
## 3. **Glandular Ocular Involvement: Dry Eye Disease (DED) or Keratoconjunctivitis Sicca (KCS)**
### Histology, Biochemistry, Anatomy, and Physiology
#### 3.1.2. Tear Film  
Chemical and microbiological analyses of tear film composition in SS patients have shown abnormal levels of several molecules involved in host defense, immune response, inflammation, cellular apoptosis, cell signaling, and adhesion [27]. Features that pSS patients share with other non-SS DED subjects include tear hyperosmolarity, increased levels of several proinflammatory cytokines, decreased levels of mucin [27], and tear film microbiome dysbiosis, with predominance of proinflammatory bacteria [28]. Conversely, possible tear film SS diagnostic biomarkers include the presence of anti-Ro/SSA and anti-La/SSB antibodies; upregulation of proinflammatory molecules such as interleukin (IL)-17 and IL-8; upregulation of the human leukocyte antigen (HLA)-DR antigen, which seems to be involved in the self-antigens presentation to autoreactive T cells; downregulation of molecules with antimicrobial properties, including lysozyme and lactoferrin, secreted by the lacrimal glands, and IgA, produced by the lymphocytes; decreased levels of epidermal growth factor secreted by the lachrymal gland, of nerve growth factor produced by the ocular surface nerves, and of transforming growth factor (TGF)-\(\beta\) produced by the conjunctival goblet cells [27].
### Main Contributions  
The objective of this paper is to develop a distributed path planner for multi-UAVs with full coverage for certain operational areas with a priority-based mechanism. For this, we propose a distributed trajectory planner based on Particle Swarm Optimization (PSO) and Bresenham Algorithm. PSO is used to generate the optimal trajectory, while the Bresenham algorithm is used for ensuring the full coverage of the operational area. To generate the optimal trajectory, we propose a multi-objective fitness function, where energy consumption and flight risk are taken care of, as well as the maneuverability and feasibility of the paths are taken into consideration.  
This paper is organized as follows, we provide a System Model for UAVs Path Planning and representation of the operational area in Section 2. In Section 3, we discuss the mathematical model for optimal trajectory planning. In this section, we design the Dynamic Fitness Function of the Objective and Constraint function of UAVs. In Section 4, we propose a distributed trajectory planner based on PSO and Bresenham algorithm. Section 5 contains Simulation results and discussions about our implementation of the proposed trajectory planner. In Section 6, we conclude our paper.
## 1 Introduction
### Classification  
#### 1.2.1. Encapsulation Protocol  
Using the five-layer model of the TCP/IP protocol stack in Table 1, we propose a method of dividing tunnel traffic from protocol classification at each layer.
# Quantifying Auditory Presence Using Electroencephalography
## 1 Introduction
### Objective Measurement  
Physiological signals generated from the body can be directly collected to measure presence (e.g., peripheral physiological signals, such as heart rate and skin conductance [21; 22] and neurological signals [23]) without the influence of subjective opinions of the participants. This kind of objective measurement is straightforward and can be real-time and reliable if conducted with strictly controlled procedures or algorithms. Electroencephalography (EEG) has gained its advantage by the freedom it gives the participants when wearing the EEG headsets, especially compared to other devices such as fMRI [6]. EEG is the recorded electrical activity generated by the brain via electrodes placed on the scalp. It is the superposition of many simpler electric signals generated by millions of neurons, reflecting brain activity. EEG has already been used in many types of research regarding presence [6; 7; 22; 24; 25; 26; 27], and other fields [28; 29; 30]. This paper explored the possibility of measuring presence using EEG data.  
The power spectrum of the EEG signal is an informative indicator to reveal neurological activities. Several frequency bands are commonly used, including delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30 Hz and above). Different frequency bands reflect different mental states. Considering that the deep sleep state indicated by delta waves and clinical diseases indicated by gamma waves [31] are not related to the purpose of this study, the three frequency bands of interest in this study are theta, alpha and beta.  
The power ratio between two specified frequency bands is also commonly used in clinical and cognitive neuroscience [32; 33; 34; 35; 36]. Using the three frequency bands of interest in this study (theta, alpha and beta) and following the pattern of calculating the ratio as \"slow-wave/fast-wave\", the power ratios available in the present study could be T/A (theta/alpha), T/AB (theta/(alpha + beta)), T/B (theta/beta) and A/B (alpha/beta).
# The Potential of Blockchain Technology and Smart Contracts in the Energy Sector: A Review
## 5 Discussion  
The automation and reduction of manual processing through smart contracts can increase efficiency and address the weaknesses of limited adoption and technical complexity. By streamlining processes and reducing the need for manual intervention, smart contracts can make it easier for companies, including smaller ones with limited technical resources, to adopt the technology and benefit from its efficiency gains [86].  
The enhanced security provided by smart contracts stored on a distributed ledger can help alleviate concerns about cybersecurity risks. The transparency and immutability of blockchain technology make it more challenging for hackers to compromise the system, thereby mitigating potential threats and protecting funds and sensitive information [87].  
The automatic recording of transactions in the blockchain ensures accurate and permanent record-keeping. This can help address legal uncertainty by providing a transparent and tamper-proof audit trail of all activities, reducing the risk of disputes and ensuring compliance with regulations [88].  
The reduction in transaction costs and increased efficiency offered by smart contracts can address concerns about the high costs associated with implementation. By automating processes and eliminating intermediaries, companies in the energy sector can achieve significant cost savings, making the adoption of smart contracts more financially viable [89].  
The transparency of smart contracts enables all parties involved to see the terms and execution of the contract. This increased transparency can enhance trust among stakeholders and help prevent disputes, ultimately addressing concerns related to legal uncertainty and regulatory challenges [90].  
By leveraging these strengths, the energy sector can overcome the identified weaknesses and barriers associated with smart contract adoption, paving the way for broader implementation and reaping the benefits of increased efficiency, security, cost savings, transparency, and reliable record-keeping.