esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Unobserved Heterogeneity in Student Performance  
The third research question aimed to determine whether significant unobserved heterogeneity in student performance can be attributed to random effects at the student and module levels. Specifically, the question asked the following:  
_\"Is there significant unobserved heterogeneity in student performance that can be attributed to random effects at the student and module levels?\"_  
Analysing the random effects in the Bayesian hierarchical growth curve model provided evidence of unobserved heterogeneity in student performance at both levels. The student-level random intercepts showed variation in baseline academic performance, with values ranging from \(-0.08\) to \(0.04\) (M = \(-0.01\); see Table 2). This indicates that individual students differ in their starting performance, suggesting the presence of latent factors beyond the included covariates, such as cognitive abilities, psychological factors, or prior educational experiences, all of which influence students' academic performance. On average, students did not systematically deviate from the group mean, but the variation suggests that some students started significantly above or below the expected level.  
Furthermore, the student-level random slopes demonstrated considerable variability, ranging from \(-4.02\) to \(-0.28\) (M = \(-2.45\); see Table 2), reflecting differences in academic growth rates over time. This variability indicates that some students experience a steeper decline in performance compared to others, suggesting that unobserved factors such as motivation, study habits, or external support might influence their academic trajectories throughout the programme.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Random Effect** & **Minimum** & **1 st Quartile** & **Median** & **Mean** & **3 rd Quartile** & **Maximum** \\ \hline Student Intercepts & \(-0.08\) & \(-0.04\) & \(-0.00\) & \(-0.01\) & \(0.02\) & \(0.04\) \\ \hline Student Slopes & \(-4.02\) & \(-3.53\) & \(-3.04\) & \(-2.45\) & \(-1.66\) & \(-0.28\) \\ \hline Module Intercepts & \(-1.96\) & \(-0.08\) & \(0.18\) & \(0.00\) & \(0.34\) & \(0.78\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of random effects and residual variability.  
Figure 1: Student academic trajectories grouped by quintiles.  
The random intercepts indicated variability across modules at the module level, ranging from \(-\)1.96 to 0.78 (M = 0.00; see Table 2). This suggests that some modules are systematically more difficult or lenient than others, even after controlling for student characteristics. These differences could be due to various factors, such as the complexity of the content, instructional quality, or variations in assessment methods and grading standards across modules. Understanding and addressing this variability is essential for ensuring equity in assessments and academic standards across the curriculum.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
## 1 Introduction
For instance, particle swarm optimization has been used to develop a multi-stage optimization algorithm that makes use of natural frequencies to detect multiple damages [23]. Damage is specifically detected by studying the relative elastic modulus reduction. A genetic algorithm has also been used to analyze and identify structural damage by comparing the measured vibrational data to data acquired before damage [24]. This approach has been tested on a cantilever beam and some frame structures. While the actual damage was detected accurately in this study, two additional incorrect damage locations were detected [24]. Other methods that have been used for damage detection include the use of an optimum updating method for global damage detection [25] and the use of sensitivity and optimization algorithms [26]. The location and intensity of structural damage have been determined by using the difference between stiffness matrices of an initial undamaged model and a test-adjusted damaged model [25]. The optimization algorithm in the literature uses an objective function that is defined as the difference between calculated and measured frequency responses; this method has been successfully used in a five-degree-of-freedom mechanical system to accurately predict the location and intensity of damage [26]. In general, the optimization-based methods have been observed to detect additional damage in some instances while finding it challenging to detect relatively small amounts of damage. In summary, the literature on damage detection presents multiple techniques to identify damage location and damage severity with varying levels of sensitivity and robustness. Most of the existing literature, however, presents deterministic results for damage diagnostics. This paper attempts to overcome this limitation by presenting a probabilistic approach with an adaptive algorithm that can be used for damage diagnostics by using the frequency response function. The approach presented in this paper allows an evaluation of the probability of damage characteristics. The adaptive algorithm developed in this study is presented in Section 2, while the analytical model for a beam that was used for damage detection is briefly presented in Section 3. Section 4 presents several simulations that are used to demonstrate the application of the proposed approach, while Section 5 summarizes the main findings and draws overall conclusions.
## 4 Ischemic Heart Disease  
### Prevalence of IHD  
The increase in life expectancy among HIV patients has led to a growing prevalence of both traditional cardiovascular risk factors--such as chronic tobacco use, hypertension, diabetes, and dyslipidemia--as well as HIV-specific factors, including chronic inflammation and immune suppression. This dual burden significantly raises cardiovascular disease risk in this population  
Several studies have aimed to evaluate the risk of acute myocardial infarction (AMI) in HIV-positive individuals compared with HIV-negative controls.  
A meta-analysis by Rao et al., which reviewed data from 16 longitudinal cohort studies involving HIV-infected adults, found that individuals with HIV have twice the risk of AMI compared to HIV-negative controls. This translates to an absolute increase of 2.2 cases per 1000 people per year [20]. Arterial hypertension, tobacco use, and dyslipidemia were the most important contributors to AMI risk.  
Other meta-analyses and cohort studies have confirmed these findings. Moreover, several studies have identified an increased cardiovascular risk specifically associated with exposure to antiretroviral therapies [21; 22; 23; 24].  
However, some studies have reported different conclusions regarding myocardial infarction risk. A cohort study by Klein et al. [25], which analyzed data from 1996 to 2011, found that after adjusting for age, sex, diabetes, hypertension, and lipid-lowering therapy, there was no significant increase in AMI risk for HIV-positive individuals during the 2010-2011 period compared to matched HIV-negative controls.  
This discrepancy may be explained by improvements in cardiovascular risk management, including a 30% increase in lipid-lowering and antihypertensive prescriptions. Additionally, better adherence to antiretroviral therapy and the achievement of very low viral loads (HIV RNA < 500 copies/mL) may have reduced overall inflammation, thereby lowering cardiovascular risk [25].  
The potential differences in AMI outcomes--both in-hospital mortality and 1-year prognosis--have drawn significant attention. Lorgis et al. [26], who examined AMI patients hospitalized between 2005 and 2009, found significantly lower hospital and 1-year mortality rates in the HIV-infected group compared to HIV-negative individuals (3.1% vs. 8.1%, \(p<0.001\) for hospital mortality; 1.4% vs. 5.5%, \(p<0.001\) for 1-year mortality). However, ischemic cardiomyopathy and heart failure hospitalizations at 12 months were more common in the HIV group (3.3% vs. 1.4%).  
There was a male sex predominance (88% of the HIV patients), and the most prevalent risk factors were smoking and dyslipidemia. Interestingly, HIV-positive individuals had a lower prevalence of diabetes and hypertension compared to HIV-negative individuals, a trend also observed in studies by Boccara et al. and Hsue et al. [27; 28]. These findings suggest that while the short-term risk of AMI is similar between HIV-positive and HIV-negative individuals, HIV status may influence long-term cardiovascular outcomes [26].
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### QoS Violation WITH Flow Ratio Change  
To study the impact of the flow ratio on the algorithm performance, we plotted the QoS satisfaction rate of each algorithm according to the flow ratio \(\varphi\) in Equation (5). Note that we set the trading interval to 5 unless otherwise noted. Since inelastic flows usually require strict bandwidth, if the proportion of inelastic flows become large, the number of QoS violations may increase.  
Indeed, the result illustrated in Figure 7 demonstrates that the QoS satisfaction rate of all algorithms decreased as \(\varphi\) increased in general. In particular, even though the _previous-slot-demand_ strategy is a dynamic adjustment algorithm, it depends upon the preceding information before trading interval \(h\). Since this algorithm did not prepare for sudden network traffic increase, QoS satisfaction rate decreased as the proportion of inelastic flows that require instantaneous bandwidth increased. Our algorithm guaranteed the QoS regardless of the inelastic flow ratio except in the case of the overwhelming number of inelastic traffic (of course pretty good performance). The result shows that our algorithm could adaptively determine how much amount of resource a tenant purchases in advance according to the inelastic flow ratio.
# A High Signal-Noise Ratio UWB Radar for Buried Pipe Location Using Golay Complementary Sequences
## 4 Experimental Results  
We experimentally locate underground pipe utilizing Golay complementary sequences. A 2.00 m \(\times\) 1.50 m \(\times\) 1.50 m tank filled with dry sand is used to simulate the experimental scenario. As the tank has steel structures, we attach absorbing materials to the surface of the steel to eliminate its strong interference. In our experiment, the TX and RX are placed on the sand with the distance between TX and RX of 2 cm. They were moved along the x-axis with a step size of 5 cm, as shown in Figure 1. In addition, we utilized average filtering to inhibit the direct wave reflected from the surface and crosstalk between TX and RX. The wave velocity in our experiment is about \(2\times 10^{8}\) m/s, which is deduced from the delay time and the known-depth of the pipe. The range resolution of the Golay-based radar system was about 10 cm. It is estimated by \(v\)/\(2B_{\mathrm{w}}\), where \(v\) is the wave velocity of the electromagnetic wave in the transmission media, \(B_{\mathrm{w}}\) is the bandwidth of the signal. The parameters of the pipes used in the experiments are shown in Table 1. All of these pipes were filled with air.  
Figure 4: Autocorrelation traces of code A, complementary code B, and code A+B.  
Figure 3: Time series of (**a**) code A and (**b**) complementary code B. Power spectrum of (**c**) code A and (**d**) complementary code B.  
Figure 5 shows the experimental results. The circles in the figure represent the actual profile of pipes. The coordinates correspond to the top position of the buried pipe. We first located two plastic pipes (P1 and P2) buried 85 cm and 60 cm below the sand surface. The result is shown in Figure 5a, which demonstrates plastic pipes buried at different depth can be located. In Figure 5a, the reflection appearing at a depth of approximately 135 cm is from the steel of the tank bottom. Similarly, when two metallic pipes (M1 and M2) buried at different depths, from the corresponding 2D image, we can also clearly identify their positions. Furthermore, to give a better description of the real condition, we locate two different material pipes (P1 and M2) simultaneously. Figure 5c shows that both the positions of the plastic pipe and metallic pipe can be visible and each position accords with the real value. The above experimental results illustrate that the Golay complementary sequences can successfully be applied to locate underground pipes. In addition, from Figure 5b,c we can observe that due to the existence of the metallic pipe, the reflection of the other pipe around metallic one will be weakened.
## 2 System Description
### Synchronization Module  
The WIOBSS-AA loads a time-frequency synchronization module with a GPS core. The GPS is used to calibrate the system clock. The synchronization unit delivers a long-term stability of 1 pulse per second (1 PPS) and a 10 MHz clock generated by a local oven-controlled crystal oscillator (OCXO)  
Figure 2: General architecture of WIOBSS-AA. This radio system is composed by the transmitting part, receiving part, and synchronization module.  
to the FPGA. The precision-unknown 10 MHz clock is calibrated by the 1 PPS scale in the FPGA. The calibrated clock is used for the reference clock in order to maintain frequency synchronization in the entire system. The transmitting and the receiving parts both use the synchronization module for clock and reference frequency synchronization.
## 5 Analysis and Discussion  
The proposed model performed relatively well compared with the state-of-the-art method. The XGBoost classifier achieves outstanding classification accuracy of 98.6%. The classification result is presented by a CM, which shows that crazing, rolled-in scale and  
Figure 9: Incorrect detection of (**a**) crazing (**b**) inclusion (**c**) patches (**d**) pitted surface (**e**) rolled-in scale (**f**) scratches defects, including test image, GT image and predicted image.  
scratches are classified at 100%, patches and pitted surface classification is around 98%, and inclusion classification is 95%. The model is compared with the traditional methods and state-of-the-art deep learning models. In Table 3, it can be observed that the proposed model performed extraordinarily well. The feature extraction of traditional methods is limited and is not sufficient enough to detect the defects properly. Figure 6 shows that the mAP of the proposed method is around 30% higher than the traditional methods. Regarding the state-of-the-art method, the proposed model performed well on the pitted surfaces, crazing, patches, and rolled-in scale. Although the SSD model performed well in inclusion, our result is still acceptable. In scratches, although our model shows less compared to other models, it is still better because the proposed model can detect strong, as well as weak, scratches. Furthermore, the information about weak scratch is not provided in the GT image; thus, this shows the model's efficiency. The YOLO-V2, YOLO-V3, and Faster-RCNN model performed poorly in the detection of crazing and pitted surface defects because crazing and pitted surfaces are small-scale defects, and this model focuses on high-level features with a fixed detection scale, with leads to low detection rate. The overall detection accuracy of the model is better than the others, with 77.12% mAP. The detection visualization of the proposed model is shown in Figure 8, where the GT image and the predicted results are completely matching, with an impressive score. Although the model achieves promising outcomes, some defects were still left undetected, as shown in Figure 9. Various conditions can result in undetected images. Firstly, with defects such as crazing, and rolled-in scale, the difference between the foreground defect and background is fuzzy. When defects are narrow and blend with the background, it is not easy to detect them. With the limited set of anchor boxes, some defects, such as inclusion, are difficult to detect as the anchor box requires that some images in inclusion are narrow and elongated. The dataset also lacks proper annotations for some defects and suffers from the illumination factor.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 2 Fluctuation Enhanced Sensing: A Powerful Sensing Technique
Differently from the resistor thermometer, the \(R(T)\) function is not needed but the resistance value \(R\) must be known; (**c**) fluctuation-enhanced absolute thermometry based on Johnson noise and first principles: the Johnson voltage and current noise are measured (circuits **b** and **c**). The combination of the two measurements provides both the absolute temperature \(T\) and the resistance \(R\). No heating occurs and no preliminary knowledge is needed if accurate current and voltage noise measurement systems are available. the statistical properties of these stochastic spikes change. The brain decodes the changes in statistics and matches the result with an odor database in memory. The first step is common to the classical sensing method: the value of a physical quantity in the sensing medium is measured (for example the output voltage of a chemical sensor). In FES, however, the microscopic spontaneous fluctuations, due to the dynamically changing molecular-level interactions between the odor molecules and the sensing media, superimposed to the average value taken into consideration in classical sensing, are strongly amplified (typically 1-100,000 times) and the statistical properties of these fluctuations are analyzed, with an approach that is somewhat similar to the operation of a biological nose. Note that the chemical signature of the odor is contained right in the fluctuations. The results of the analysis are compared with a statistical pattern database to identify the odor. Since, unlike the classic sensing methods in which a measurement of the average values of the electrical signals (voltage or current) is carried out, the biological sensing exploits the fluctuations of the signals to reach much higher sensitivity, the sensing technique we discuss, which operates on the same principle, has been named \"fluctuation-enhanced\" sensing [34; 35; 36; 37; 38; 39; 40] or FES. Even if the FES method does not reach now the sensitivity level of a dog nose in commercial or proto-type gas sensors, its principle mimics the sense of smell. Moreover, the sensitivity of the FES method can be sharply improved by using a smaller volume of the sensing layer as discussed in Section 4. Indeed, tiny gas sensing layers, made of nano-particle materials, tend to the size of smell cells in biological objects, characterized by lower detection threshold levels.
## 2 Materials and Methods
### Nonlinear Parameters Calculation
#### 2.2.1 Sample Entropy (SampEn)  
The sample entropy was mathematically computed as follows:  
1. From a vector \(\mathbf{X}=\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{N}}\), two sequences of \(\mathbf{m}\) consecutive points--\(\mathbf{x_{m}}(\mathbf{i})=\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{i+m-1}}\) and \(\mathbf{x_{m}}(\mathbf{j})=\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{j+m-1}}\), \(\mathbf{i},\mathbf{j}\in[\mathbf{1},\mathbf{N}-\mathbf{m}],\mathbf{i}\
eq\mathbf{j}\)--were selected to compute the maximum distance and compared to tolerance, \(\mathbf{r}\), for repeated sequence counting, according to: \[\mathbf{d}[\mathbf{X_{m}}(\mathbf{i}),\mathbf{X_{m}}(\mathbf{j})]=\textit{max}\big{[}|\mathbf{x_{i+k}}, \mathbf{x_{j+k}}|\big{]}\leq r,\ (\mathbf{k}\in[0,\mathbf{m-1}],\ \mathbf{r}\geq\mathbf{0}),\] where the tolerance \(\mathbf{r}\) is equal to \(\mathbf{0.1\sim}\mathbf{0.2\ \times}\ \mathbf{SD}\) and \(\mathbf{SD}\) is the standard deviation of \(\mathbf{X_{N}}\)[36].
2. \(\mathbf{B^{m}}(\mathbf{r})\) is the average amount of \(\mathbf{B^{m}}(\mathbf{r})\) for \(\mathbf{i}\in[\mathbf{1},\mathbf{N}-\mathbf{m}]\) and \(\mathbf{B^{m+1}}(\mathbf{r})\) is the average of \(\mathbf{m+1}\) consecutive points; thus, sample entropy can be computed as follows: \[\textit{SampEn}(\mathbf{N},\mathbf{m},\mathbf{r})=-\textit{In}\Big{[}\frac{\mathbf{B^{m+1}}( \mathbf{r})}{\mathbf{B^{m}}(\mathbf{r})}\Big{]}.\]  
In the case of this paper, the SampEn was calculated using MATLAB codes obtained from the Physionet tool [37]. For calculating this measure, the \"default\" parameter values \(\mathbf{m=2}\) and \(\mathbf{r=0.2\ \times}\) (standard deviation of the data) were applied.
## 2 Network Model
User 1 and the eavesdropper use maximum-ratio combining, and the rate of Message 2 at U1 is given as follows:  
\[R_{x_{2},\mu_{1}}=\frac{1}{2}{\rm log}_{2}(1+\frac{p_{2}h_{T\mu_{1}}}{p_{1}h_{ T\mu_{1}}+\sigma_{u_{1}}^{2}}+\frac{p_{r}g_{Ru_{1}}}{\sigma_{u_{1}}^{2}}), \tag{4}\]  
where \(h_{Tu_{1}}=\left|\tilde{h}_{Tu_{1}}\right|^{2}\), \(g_{Ru_{1}}=\left|\tilde{g}_{Ru_{1}}\right|^{2}\), and \(p_{r}\) denotes the transmit power of Message 2 at the relay. The rate of Message 2 at the eavesdropper is given as follows:  
\[R_{x_{2},E}=\frac{1}{2}{\rm log}_{2}(1+\frac{p_{2}h_{TE}}{p_{1}h_{TE}+p_{3}h_ {TE}+\sigma_{E}^{2}}+\frac{p_{r}g_{RE}}{\sigma_{E}^{2}}), \tag{5}\]  
Figure 1: Underlying CR-NOMA system with a cooperative relay and an EH user. where \(g_{RE}=\left|\tilde{g}_{RE}\right|^{2}\). Under NOMA, User 1 performs successive interference cancellation to remove the interference of Message 2, and decodes Message 1. Then, the rate of Message 1 at U1 is defined as follows:  
\[R_{x_{1},Tu_{1}}=\frac{1}{2}\text{log}_{2}(1+\frac{p_{1}h_{Tu_{1}}}{\sigma_{u_{ 1}}^{2}}). \tag{6}\]  
Therefore, the secrecy rate of Message 1 is given by  
\[R_{u_{1}}=\left[R_{x_{1},Tu_{1}}-R_{x_{1},TE}\right]^{+}, \tag{7}\]  
where \(\left[c\right]^{+}=\max\left(0,c\right)\). The rate of Message 2 at U2 is as follows:  
\[R_{x_{2},Ru_{2}}=\frac{1}{2}\text{log}_{2}(1+\frac{p_{r}g_{Ru_{2}}}{\sigma_{u_ {2}}^{2}}), \tag{8}\]  
where \(g_{Ru_{2}}=\left|\tilde{g}_{Ru_{2}}\right|^{2}\). The achievable rate of Message 2 is defined by the rate observed at U1, the relay, and User 2 as follows:  
\[R_{x_{2},TR}=\frac{1}{2}{\rm log}_{2}(1+\frac{p_{2}h_{TR}}{p_{1}h_{TR}+\sigma_{ R}^{2}}), \tag{1}\]0  
Consequently, the secrecy rate of Message 2 is defined as:  
\[R_{x_{2},TR}=\frac{1}{2}{\rm log}_{2}(1+\frac{p_{2}h_{TR}}{p_{1}h_{TR}+\sigma_{ R}^{2}}), \tag{1}\]1  
The power at the transmitter and the relay should remain at a controllable level to avoid interference with the primary user, and these power constraints are specified in Section 3.
# Efficient MIMO Signal Predistortion for Secrecy-Enhancing
## 1 Introduction
In addition, the complexity of the detection process can be reduced due to the use of the real-numbered channel matrix. This paper is organized as follows. Following this introduction, Section 2 reviews the related works. After presenting the concept of PLS schemes with AN and AI, the concept of O-STBC for real-numbered signals is introduced. Section 3 describes the proposed scheme. Section 4 presents the simulation results and discussions, and finally, Section 5 draws the conclusion. Notation: Bold lower and upper case letters represent vectors and matrices, respectively. \(\mathbf{A}^{T}\) and \(\mathbf{A}^{H}\) represent the transpose and Hermitian transpose of a matrix \(\mathbf{A}\), respectively. For a column vector, \(\mathbf{a}\), diag(\(\mathbf{a}\)) returns a square diagonal matrix with the elements of vector \(\mathbf{a}\) on the main diagonal. For a modulated signal, \(x\) denotes a real-numbered signal while \(z\) denotes a complex-numbered signal. \(||z||\) and \(z^{*}\) denote the absolute value and conjugate of a complex number \(z\), respectively.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Formation Phase
#### 5.2.1 Optimal Clustering  
In optimal clustering, cluster formation aims to reduce the rate of energy usage by the cluster members [57]. This process is achieved by assigning SNs to their nearest CHs after calculating their distances from the CHs based on the strength of the signals received as advertisement messages. In this clustering type, the size of the established clusters and the rate of energy usage within the clusters are not considered.  
Figure 7: Cluster formation phase.