esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Activity and Redox Balance in the Elderly: Signal Transduction Mechanisms
###### Abstract
Thus, it seems that the body's antioxidant responses are proportional to exercise intensity, but too-high-intensity exercise produces inflammation and cell damage, both in young and elderly subjects. On the other hand, [PERSON] et al. (2017) [10] showed that, independently of intensity, volume, type of exercise and population, physical exercise always has an antioxidant effect. In conclusion, moderate-intensity exercise should be the right compromise to balance oxidant effects. Interestingly, different scenarios are present in athletes with different levels of ability: while in elite athletes, physical exercise is not enough to maintain the oxidant/antioxidant balance, making an anti-oxidant supplementation necessary, in amateur and master athletes, oxidant/antioxidant activity is kept balanced by exercise, protecting the muscle from damage [11]. Of note, physical exercise also shows positive effects on the redox environment in non-muscular systems such as the cerebral [12,13] and vascular [14] systems. In fact, [PERSON] et al. (2019) [12] studied the effect of exercise volume on antioxidant enzyme levels in various brain regions. They found that 30-60 min of exercise increased Catalase (CAT) activity, while 8 weeks of training increased Superoxide Dismutase (SOD). More than 8 weeks of training increased both ROS and antioxidant activity. Thus, four to eight weeks of moderate exercise promote a healthy balance of antioxidant enzymes in the rodent brain. Recently, [PERSON] et al. (2019) [13] reviewed the effects of resistance training to counteract oxidative stress in the brain. In particular, they showed evidence that Insulin Growth Factor-1 (IGF-1) in muscle activates the Protein Kinase B (Akt) signalling pathway at brain level. However, further investigations are necessary, since some observations are only speculative. In 2016 [PERSON] et al. [14] studied the positive effects of exercise on redox balance in vessels. They found that arteries from trained mice presented higher levels of antioxidant markers such as: (i) Peroxisome Proliferative Activated Receptor-coactivator-1 (PPARgamma), (ii) Cytochrome-C Oxidase Subunit IV isoform 1 (COX4I1), and (iii) Isocitrate Dehydrogenase 2 (Idh-2). Moreover, the trained mice showed higher respiratory capacity and improved oxidant/antioxidant balance compared to untrained mice. These results sustain the hypothesis that physical activity can be vasculo-protective. Unfortunately, further studies are necessary. The aim of this review is to unravel the relationship between redox balance, physical activity and aging, analysing what is known of the involved molecular pathways. It has therefore been organized into five sections: (1) Introduction, (2) Physical activity and redox balance in the young; (3) Physical activity and redox balance in the elderly; (4) Insights into signal transduction; and (5) Conclusions. Figure 1 reports the diagram of the process that we apply to select papers to review.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 1 Introduction
### Contributions  
This study of energy-aware security mechanisms provides an effective solution to the conflict between the security and energy efficiency of IoT devices. Compared with existing research, we comprehensively examine multiple dimensions such as hardware, software, network, and data management, summarize the main contributions of the current research results in terms of the two key themes of energy efficiency and security, and provide an in-depth analysis of the presence of potential problems. It also provides new perspectives and ideas for subsequent research on energy-aware security mechanisms, which will help promote the sustainable development of IoT technologies. This survey will help future researchers apply energy-aware security mechanisms in real IoT systems.
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 2 Materials and Methods
### Multi-Linear Regression Analysis Using Machine Learning  
Regression models, such as trees, neural networks, kernel approximation, and the linear regression model, were employed for the machine learning component of this study. Given their capacity to model complex relationships, we utilized neural networks, specifically structured similarly to convolutional neural networks (CNNs) to estimate saliva secretion from the tongue image. From a total of 496 image features, we extracted 100 features that exhibited a high correlation with saliva secretion. These 100 high-correlation features served as input data, while the measured saliva secretion data acted as target data. The _fitrnet_ function in MATLAB R2023a (Mathworks, Inc., Natick, MA, USA) was utilized for non-linear regression [32]. This function trains a feedforward, fully connected neural network for regression. The neural network model was configured with three fully connected  
Figure 3: Three sample images (red boxes, 100 \(\times\) 100 pixels) in a tongue image.  
hidden layers, containing 200, 100, and 50 perceptron in each layer, respectively (Figure 4). The ReLU (rectified linear unit) function was used as the activation function.  
Regression fitting training data were randomly extracted from 90% of the total data. The remaining 10% data were used for testing. Cross-validation was performed during the training process, where 20% of training datasets was used for cross-validation. And we calculated the correlation coefficients of the train data, test data, and total data, respectively.
## 4 Results
### Respondents with Asthma or COPD
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Follow-Up**} & \multirow{2}{*}{**Range**} \\ \cline{2-2} \cline{5-6}
**ACT Score** & & & & & **Median** \\ \hline \((n=30;n=31;p=0.154)\) & \(16.6\) & \(17.0\) & \(8.0\)–\(25.0\) & \(18.0\) & \(19.0\) & \(6.0\)–\(25.0\) \\ \hline \(\begin{array}{c}\text{CCQ Total Score}\\ \text{$(n=11;n=13;p=0.068)$}\end{array}\) & \(1.9\) & \(2.1\) & \(0.1\)–\(3.8\) & \(2.5\) & \(2.3\) & \(0.9\)–\(3.9\) \\ \hline \(\begin{array}{c}\text{CCQ Symptom Score}\\ \text{$(n=11;n=13;p=0.035)$}\end{array}\) & \(2.2\) & \(2.0\) & \(0.2\)–\(4.5\) & \(3.0\) & \(2.8\) & \(1.3\)–\(4.8\) \\ \hline \(\begin{array}{c}\text{CCQ Functional Score}\\ \text{$(n=11;n=13;p=0.506)$}\end{array}\) & \(1.6\) & \(1.5\) & \(0.0\)–\(4.0\) & \(2.0\) & \(2.0\) & \(0.5\)–\(3.8\) \\ \hline \(\begin{array}{c}\text{CCQ Mental Score}\\ \text{$(n=11;n=13;p=0.382)$}\end{array}\) & \(1.8\) & \(1.5\) & \(0.0\)–\(5.5\) & \(2.5\) & \(2.5\) & \(0.5\)–\(4.0\) \\ \hline \(\begin{array}{c}\text{CAT score}\\ \text{$(n=11;n=11;p=0.034)$}\end{array}\) & \(10.3\) & \(10.0\) & \(0.0\)–\(26.0\) & \(14.7\) & \(16.0\) & \(7.0\)–\(27.0\) \\ \hline \(\begin{array}{c}\text{St George Score}\\ \text{$(n=43;n=45;p=0.851)$}\end{array}\) & \(36.2\) & \(37.3\) & \(0.9\)–\(69.2\) & \(33.6\) & \(35.2\) & \(2.0\)–\(67.3\) \\ \hline \(\begin{array}{c}\text{St George Symptom Score}\\ \text{$(n=43;n=49;p=0.220)$}\end{array}\) & \(50.8\) & \(53.0\) & \(7.5\)–\(90.0\) & \(43.3\) & \(40.9\) & \(0.0\)–\(97.3\) \\ \hline \(\begin{array}{c}\text{St George Activity Score}\\ \text{$(n=59;n=54;p=0.0015)$}\end{array}\) & \(48.6\) & \(53.5\) & \(0.0\)–\(87.3\) & \(53.2\) & \(57.7\) & \(0.0\)–\(92.5\) \\ \hline \(\begin{array}{c}\text{St George Impacts Score}\\ \text{$(n=59;n=57;p=0.045)$}\end{array}\) & \(20.7\) & \(18.9\) & \(0.0\)–\(58.7\) & \(17.7\) & \(15.5\) & \(0.0\)–\(50.3\) \\ \hline \hline \end{tabular}  
* In the past 12 months, have you had a COPD exacerbation?
* In the past 12 months, have you been admitted to hospital due to COPD exacerbations?
* In the past 12 months, have you had any days off work, study or your usual activities because of COPD exacerbations?  
\end{table} 
Table 3: Comparison of validated test scores at baseline and follow-up. \(p\)-values were obtained from the paired \(t\)-test to compare the change in means from baseline to follow-up. Figure 3: Respondents’ agreement with statements related to COPD.
## 4 Numerical Simulation Results on Heat Transfer Characteristics
### Cooling Effect of the Cold Plate at Different Powers  
The cold plate is designed for radar cooling requirements. When the radar system needs to operate under extreme environmental conditions, 60% concentration of ethylene glycol aqueous solution is used as the cooling medium [19] (with excellent thermal conductivity and low-temperature stability). By simulating different heat source power conditions, the system evaluates the heat dissipation performance of the cold plate under different heat load conditions, which can also provide a theoretical basis for other heat source cooling.  
According to the simulation results, it can be found that when the heat flow density of the T/R component is 500 W/cm\({}^{2}\), the maximum temperature of the T/R component is 369.2 K, which has exceeded the normal temperature of the radar operation, and the radar component can still work under this temperature, but the radar's working reliability and life are seriously affected. When the heat flow density of the T/R component is 1000 W/cm\({}^{2}\), the maximum temperature of the T/R component is 445.95 K, which seriously exceeds the safe working temperature range of the radar, indicating that the 6 \(\times\) 5 parallel multi-branch cold plate can meet the 500 W/cm\({}^{2}\) and below heat flow density components under the design flow rate but cannot meet the 1000 W/cm\({}^{2}\) heat flow density of the T/R component. The are two reasons for this: one is the runner in the branched cold plate is DC channel, and the second is the coolant flow rate in the runner is too fast, leading to the cold plate not carrying out sufficient heat exchange with the coolant. For heat flux densities exceeding 1000 W/cm\({}^{2}\), as discussed in this paper, modifications to the placement of the heating element or the width of the cold plate channel can enhance heat transfer. These adjustments increase the heat transfer area, thereby improving the efficiency of heat removal.
## 6 Results and Discussion
### Comparison of Classification Performance to the Existing Works  
To compare the performance of our proposed PQD classification to the other existing works, Table 10 shows a list of the accuracy of PQD classification appeared in the literature. Noted that accuracy of each mentioned system is adopted from those references. It is seen that the 98.77% classification accuracy of our proposed system is classified as very high value compared to the existing works. Additionally, our classification system can classify power quality signals of up to 13 different types, which is more than most research in this field. Furthermore, the classification system using the SSA as an optimal feature selection algorithm can quickly provide a solution. Hence, the classification system proposed in this work is another capable method for PQD classification and is appropriate for use in practical distribution systems.
## 3 Supervised ML Models
### Stand-Alone Algorithms
#### 3.2.3 Bayesian Linear Regressor (BLR)  
Just like the LR, Bayesian linear regressor represents a special case of REG that allows modelling to be performed within the \"Bayes\" theorem statistical inference.  
Hence, for a given dataset say \(D=(x_{1},y_{1})\),..., \((x_{n}y_{n})\) where \(x\in\Re_{\rm d}\), and \(y\in\Re\), then a BLR model can be expressed as:  
Prior:  
\[\omega\ \sim\ \mathcal{N}\Big{(}0,\sigma_{\omega}^{2}I_{d}\Big{)}\]  
\(\Omega\) is the vector \((\omega_{1},\ldots,\omega_{d})\)T, making the previous distribution a multivariate Gaussian; and \(I_{d}\) is a \(d\)\(x\)\(d\) identity matrix.  
Likelihood  
\[Y_{i}\ \sim\ \mathcal{N}\Big{(}\omega^{T}x_{i},\ \sigma^{2}\Big{)}\]  
With the assumption that \(Y_{i}\perp Y_{j}\upharpoonright\omega\), \(i\
eq j\)  
If we use the variance, \(a=1/\sigma^{2}\), and \(b=1/\sigma^{2}\omega\) then we assume that \(a\) and \(b\) are unknown.  
We state the prior as:  
\[p(\omega)\propto exp\ \bigg{\{}-\frac{b}{2}\omega^{t}\omega\bigg{\}} \tag{6}\]  
Furthermore, the likelihood stated as:  
\[p(D|\omega)\propto exp\Big{\{}-\frac{a}{2}(y-A\omega)^{T}(y-A\omega)\Big{\}} \tag{7}\]  
where \(y=(y_{1},\ldots,y_{\rm N})^{\rm T}\) and \(A\) is a \(n\)\(x\)\(d\) matrix  
Then, the posterior is:  
\[p(\omega|D)\propto p(D|\omega)p(\omega)\]  
Which ultimately produces the expression:  
\[p(\omega|D)\ \sim\mathcal{N}(\omega\Big{|}u,\Lambda^{-1})\]  
where the precision matrix \(\Lambda\) is:  
\[\Lambda=aA^{T}A+bI_{d}\]  
\[\mu=a\Lambda^{-1}A^{T}y\]  
For the predictive posterior:  
\[p(y|x,D)=\int\ p(y|x,D,\omega)p(\omega|x,D)d\omega=\int\ p(y|x,\omega)p(\omega |D)d\omega\]  
It is then possible to then get the following:  
\[y|x,D\ \sim\ \mathcal{N}\bigg{(}\mu^{T}x,\ \frac{1}{a}+x^{T}\Lambda^{-1}x \bigg{)} \tag{8}\]
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Perceptually Uniform Color Spaces  
For many image processing applications such as compression and quality assessment, pixels are encoded with a three-dimensional representation: one dimension corresponds to an achromatic component the luminance and the two others correspond to the chromatic information. An example of this kind of representations is the linear color space CIE-\(XYZ\) where \(Y\) represents the luminance and \(X\) and \(Z\) the chromatic information. This color space is often used as a reference from which many other color spaces are derived (including most of \(RGB\) color spaces). However this space is not a uniform (or perceptually uniform) color space. A uniform color space is defined so that the difference between two values always corresponds to the same amount of visually perceived change.  
Three uniform color spaces are considered in this article: \(HDR\)-\(Lab\)[20], the HDR extension of the CIE 1976 \(L^{*}a^{*}b^{*}\)[21] and two other HDR/WCG color spaces designed to be perceptually linear, and simple to use: \(ICTCp\)[6] and \(J_{2}a_{2}b_{2}\)[22]. Unlike the \(XYZ\) color space in which all components are always non-negative, these three uniform color spaces represent the chromatic information using a color-opponent model which is coherent with the Human Visual System (HVS) and the opponent color theory.  
In this article, the luminance component of the uniform color spaces is called uniform luminance instead of, according to the case, lightness, brightness or luma to avoid unnecessary complexity. For example, the uniform luminance of \(HDR\)-\(Lab\) should be called lightness while the uniform luminance of \(J_{2}a_{2}b_{2}\) should be called brightness.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
###### Abstract  
Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage.  
fractal dimension; structural health monitoring; offshore wind turbine; kNN; support vector machines +
Footnote †: journal: Applied Sciences
## 6 Case Study  
An actual case study is illustrated to show the applicability of the proposed Construction Data Hub (CDH) in real-life situations and to prove that collecting construction project data from the proposed twelve dimensions is significant for tackling construction project difficulties. A machine learning model that utilizes the data collected through the CDH is developed to analyze the factors influencing construction projects' profit. The case study data set was obtained from a private construction organization that carries out construction works in Alberta, Canada. Their construction projects include government buildings, residential buildings, universities, schools, hospitals, parks, playgrounds, infrastructure works, etc.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 5 Proposed Continuous Authentication Method
### Classification and Authentication Design  
SVM (Support Vector Machine) is defined as the classifier formally defined by a separating hyperplane. In the supervised learning, with a set of training examples marked as one of the two given categories, SVM algorithm is able to construct the model that assigns new examples to one of the two categories. Based on this, the LibSVM [29] is adopted in our design for multi-class classification, where the radial basis function (RBF) kernel is utilized for core experiments of our study. Intuitively, the entire procedure is conducted independently from others. The personalized classification model is built for each participant. The subsequent evaluation is conducted using the personal testing samples.  
At this point, a personalized model for individual participant is constructed locally, where the training data characterize unique behavioral patterns within a relatively long interval. Intuitively, for participants in certain area, similar activities are performed accordingly, indicating unique environmental characteristics. Hence the major performed activities types are denoted as \(\{\Delta_{1},\ldots,\Delta_{N}\}\), where \(N\in\mathbb{Z}_{\mathcal{P}}\). Consequently, the ratio of specific activity among all the detected actions within certain time interval is defined as \(\{\Phi_{\Delta_{1}},\ldots,\Phi_{\Delta_{N}}\}\), which is presented in the form of biometric parameter \(\Psi_{i}\). Note that for various participants, \(\Psi_{i}\) varies. \(\Psi_{i}=\{\Phi_{\Delta_{1}},\ldots,\Phi_{\Delta_{N}}\}\) is then securely transmitted to SN right after the initial successful certifiateless authentication process. Subsequently, UE periodically updates its biometric parameter \(\Psi_{i}\) and transmits it to SN. Hence the statistical significance involving the previous data and the fresh data are conducted so as to measure the variation, where the significance level is set to be \(\alpha=0.05\). If anomalies detected, SN resets the stored keying information of certain UE and requests re-authentication from specific UE. Finally, after each validation, the stored parameter set \(\Psi_{i}\) is updated using newly acquired value so as to timely reveal the recent biometric status of user It is worth noting that the parameter checking process is performed at set intervals, thus continuous authentication is achieved.
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
## 2 Smart Deployable Scissor Lift Brace
### Numerical Example  
To illustrate the potential benefits of the proposed deployable scissor lift brace system, a numerical example is presented in this section. A structural plan is presented in Figure 5. The building is a single-story, large open ground floor structure with a steel truss roof. The 1.45 m deep truss roof is made of Australian sections 150 UB14 in its top and bottom chords, with web members made of square hollow sections. The roof is supported by 24 columns (200 UC46.2). The roof truss has an estimated mass of 85.02 ton. Lateral load resistance is provided by three braced bays in its N-S elevations, and, in its E-W direction, lateral load resistance is provided by moment frame actions. To simplify the analysis, a rigid diaphragm assumption was applied to the roof structure and the structure was analyzed using the structural software Spacegass version 14.2. The following dynamic properties were obtained:  
* Lateral stiffness (N-S): 99,010.0 kN/m
* Lateral stiffness (E-W): 8944.5 kN/m
* Fundamental frequency (N-S): 5.29 Hz
* Fundamental frequency (E-W): 1.63 Hz  
It was observed that the lateral stiffness in the E-W direction was weak due to the absence of braced bays. In a retrofit scenario, a pair of smart deployable scissor lift braces were added to the building's north and south elevations, as indicted in Figure 5. The scissor lift braces were made up of 150 \(\times\) 150 \(\times\) 5 square hollow sections. When deployed, the fundamental frequency in this direction was increased to 3.46 Hz. To compare the structure's responses under earthquake excitation, a dynamic time-history analysis was  
Figure 5: Structure in the numerical example.  
conducted with an input ground excitation derived from the 1940 El Centro earthquake in the N-S direction of the structure. Figure 6 presents the roof displacement response. Prior to the retrofit, roof displacement reached 87 mm (story drift equals 1/42). This magnitude of story drift is very likely to cause the soft-story mechanism and a total collapse. If a pair of scissor lift braces were to be installed and triggered by the EEW prior to the arrival of the earthquake excitation, the roof displacement response would decrease to 16.9 mm (story drift equals 1/217).