esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 4 Vitamin D Deficiency and Cardiovascular Risk
### Vitamin D Deficiency and Coronary Artery Disease  
The relationship between Vitamin D deficiency and coronary heart disease (CAD) has been demonstrated in various studies. The risk of coronary heart disease seems to be associated with Vitamin D deficiency. The main indicator of this possible relationship is the presence of VDR in the myocardium, vascular endothelial cells, and fibroblasts. In detail, epidemiological studies highlight that Vitamin D deficiency, causing the activation of the renin-angiotensin-aldosterone system (RAAS), causes an increase in CAD by increasing anti-inflammatory and decreasing proinflammatory mediators [38]. Preliminary studies show a relationship between Vitamin D deficiency and cases of acute myocardial infarction (AMI) in the short and long term [38]. The study included 18,225 male patients aged between 40 and 75 years. Over 10 years, results showed an association between low Vitamin D levels and an increased risk of AMI [27]. In particular, men deficient (\(\leq\)15 ng/mL) in 25(OH)D were at increased risk of AMI compared to those considered to be sufficient (\(\geq\)30 ng/mL) in 25(OH)D. It has also been shown that hospitalized patients with AMI had a high incidence of Vitamin D deficiency. [PERSON] et al. found low levels of Vitamin D in 96% of 239 patients with acute coronary syndrome (ACS) [39]. [PERSON] et al. found low levels of Vitamin D in patients who had episodes of myocardial infarction during their lifetime [40]. Increased risks of ischemic heart disease, myocardial infarction, and premature death were also associated with low Vitamin D levels during a large prospective study that included 10,170 individuals [41]. Furthermore, a meta-analysis of 18 studies found an increased risk of ischemic heart disease and premature death in subjects with low Vitamin D levels [41].  
Despite these observations, there is still no certain protocol to support the benefit of Vitamin D supplementation in the treatment/prevention of coronary heart disease. This is partly due to the few ongoing or completed clinical studies examining the effects of Vitamin D on patients with AMI or ACS.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
###### Abstract  
In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.  
cluster-leader (CL); mobile wireless sensor node (MWSN); mobile energy efficient and connected (MEEC); reference point group mobility model (RPGM) +
Footnote †: journal: Sensors
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 1 Introduction
Based on the Shannon entropy, [PERSON], [PERSON], and [PERSON] [9] developed a method to automatically select the optimal wavelet scale for identifying the damage location when wavelet transform is applied. In addition, the wavelet transform was used to apply to the identified mode shapes for detecting bridge damage and even the damage location [36; 37; 38]. Furthermore, a parameter study on localizing the bridge damage using the wavelet transform and vehicle responses was numerically and experimentally presented [39]. The effect of the bridge span, damage extent, vehicle velocity and mass, and road surface profile on the accuracy of the results are studied. [PERSON] and [PERSON] [40] and [PERSON] and [PERSON] [41] demonstrated that road roughness has a significant, negative impact on detecting bridge damage using the wavelet transform. If the road surface profile is considered in the vehicle-bridge interaction (VBI) model, the wavelet transform fails to detect the bridge defect when the vehicle responses are applied to the wavelet transform directly. Wavelet-based energy is also used to detect the bridge defect [2; 42]. The bridge frequency can be obtained using the wavelet pseudo frequency, and the change in pseudo frequency, therefore, can reflect the bridge's healthy conditions. However, the pseudo frequency in wavelet analysis cannot provide any resource of information for times/locations. In other words, the wavelet pseudo frequency can only achieve the first level of bridge SHM, namely identifying the presence or absence of damage. It is well known that it is sometimes challenging to extract the bridge frequency using the drive-by technique of bridge SHM [4]. This paper employs the wavelet analysis of vehicle accelerations to compute the wavelet energy. Instead of extracting the bridge frequency using the wavelet pseudo frequency, the change of the peak wavelet energy is used to estimate the bridge conditions. As already mentioned, this only realizes the first level of bridge SHM. To achieve the second level of bridge SHM, namely finding the damage location, a damage location index is proposed based on the component wavelet energy. The bridge span is divided evenly into 10 parts and each corresponding vehicle response is used to calculate the component wavelet energy. The validity of the proposed approach is verified by a numerical simulation and the effects of road roughness, vehicle mass, vehicle speed, and signal noise are investigated. The result shows that the proposed approach has a potential application in bridge health monitoring using indirect measurements.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 2 Assumptions and Considerations
### Solvability of the Problem  
For the given mathematical problem to be solvable, the information provided by the SCM must be sufficient to determine the roll positions. The SCM provides \(n-1\) angles, but the radial and tangential error components of each outer roll represent \(2n\) degrees of freedom. Two assumptions must be made to reduce the solution's degrees of freedom:  
1. The first roll is in the perfect position.
2. The tangential component of the deflection is zero.  
The first assumption is necessary because the measured angles only provide a relative measurement, with no connection to the absolute coordinate system of the SCM. The known position of one roll provides an \"anchor point\" to the coordinate system. The first outer roll, often referred to as the \"footroll\", is chosen and its position is assumed to always be ideal. Since it is the first roll to contact the strand, its influence on the quality is considerable. Thus, the position of this roll is monitored very strictly, making the assumption reasonable and reducing the degrees of freedom by 2.  
The second assumption must be made because the measured tangents do not fully constrain the roll positions. Every roll could move in the direction of the tangent lines without changing the measured angle. This is a fundamental fact that cannot be overcome by an algorithm. Therefore, the assumption is made that each roll position error only contains a radial component. This reduces the degrees of freedom further by \(n-1\). Of course, in the real world, the roll errors do contain tangential components, which will cause the algorithms to produce systematic calculation errors. This will be investigated in detail in Section 4.  
Under these assumptions, the degrees of freedom of the solution are reduced to  
\[2n-2-(n-1)=n-1, \tag{1}\]  
which is equal to the number of measured angles and thus leads to an unambiguously solvable system.
# Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather
## 6 Conclusions  
1. Based on the study of the tail feather unfolding behavior of birds such as peacocks, a new deployable antenna configuration is designed by bionically proposing a two-stage unfolding antenna driving method.
2. The kinematic modeling of the new spatially deployable antenna mechanism is carried out, and the variation in kinematic parameters is obtained using an MATLAB solution, and the simulation of the rod motion is carried out using ADAMS software. The angle variation results obtained from ADAMS simulation are compared with the MATLAB ones to verify the correctness of the kinematic equations.
3. The antenna unfolding trajectory is analyzed, and the horizontal working space of the deployable antenna is determined to be 4 m \(\times\) 3.5 m.  
Conceptualization H.X. and X.M.; methodology, J.Z.; software, J.Z.; validation, Y.F.; formal analysis, Y.F. and J.Z.; writing--original draft preparation, Y.F.; writing--review and editing, H.X. and X.M.; funding acquisition, H.X. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Laboratory Foundation 2021-JCJQ-LB-006 grant number 6142411342110.  
**Institutional Review Board Statement:** Not applicable.  
Figure 23: Lateral rib unfolding trajectory.
## 3 Results
### Mechanical Tests  
The tensile properties are summarized in Table 4. The highest values of strength were observed in the SLM samples, the lowest in the wrought material. On the other hand, the elongation in the SLM sample was a mere 2-3%, whereas in the wrought and EBM materials 12-14%. It is worth noting that only in the case of SLM samples, differences between samples cut in \(XY\) and \(YZ\) planes were observed. EBM and wrought material were isotropic. To supplement those results, microindentation tests were done. Vickers microindentation values for the EBM samples were 315 \(\pm\) 13 HV\({}_{0.3}\) and 340 \(\pm\) 12 HV\({}_{0.3}\) for the \(XY\) and \(XZ\) planes, respectively. These values are similar to those given in the literature for the \(\alpha+\beta\) structure in samples fabricated by the EBM method [78]. The values of Vickers microindentation hardness measured for the SLM samples were 370 \(\pm\) 19 HV\({}_{0.3}\) and 390 \(\pm\) 24 HV\({}_{0.3}\) for the \(XY\) and \(XZ\) planes, respectively, similar to the hardness of an \(\alpha^{\prime}\) martensitic-type phase obtained for Ti-6 Al-4V alloy by other authors [74]. The values of Vickers microindentation hardness measured for the bimodal Ti-6 Al-4V alloy was 360 \(\pm\) 11 HV\({}_{0.3}\). The results show that only EBM and wrought material meet the requirements of ASTM F136. In the case of SLM samples, the ductility is below the needed minimum value.  
Figure 9: The XRD (X-ray diffraction) patterns for Ti-6 Al-4V alloy obtained in the SLM (**a**); EBM (**b**) and heat-treated and annealed sheet (**c**).
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 4 Material and Methods
### WiMAX IEEE 802.16e Performance Metrics Used
#### 4.1.2 Delay  
Transmission delay is the end-to-end delay of packets received, and is one of the many networks' most essential performance measurements. Delay usually specifies how long it takes for a packet/data to travel from the transmission link to the receiver node. The moment the packet leaves the transmission site or sources, it goes from the switches and is then directed to other links in the network until it reaches its destination. In simpler terms, transmission delay is the time needed to transmit all bits via a transmission medium [26].  
\[\text{t(time)}=\frac{(\text{Number of bits in a packet})}{(\text{total Number o f transmitted packets})} \tag{2}\]
# Simplified Welch Algorithm for Spectrum Monitoring
## 3 Modified Welch Algorithm
### Theoretical Analysis
The frequency response is generally attenuated and for averaging with larger order, larger attenuation is observed. The transfer function of the modified version can be expressed as the following:  
As it can be seen in Figure 6, the convergence time and gain are different for each different alpha (\(\alpha\)) (feedback factor) value. By analyzing the system response, a normalization factor can be added after the modified Welch algorithm to cancel the gain based on the \(\alpha\) parameter. Final gain \(G\) for the selected parameter can be calculated based on the infinite geometric series:  
\[Gain\ G=\ \sum_{m=0}^{\infty}a^{m}=\frac{1}{1-a} \tag{19}\]  
Then the normalization factor (\(NF\)) is:  
\[NF=1-\alpha \tag{20}\]  
The correction can be added after the algorithm to retrieve a normalized \(PSD\). Figure 7 presents the corrected transfer function of the modified Welch algorithm after the normalization. Comparing Figure 4 with Figure 7 reveals that both systems attenuate frequency components with fast variation, but the modified version has a smoother and more uniform response than the original one and removes the big gaps in frequency response of moving average. Figure 5: Frequency representation of the modified Welch frequency variation response. Figure 6: Convergence and gain of different a parameter. The final point to address is the saturation time based on the \(\alpha\) factor. Since the formula is exponential, the following expression can be used to estimate the number of FFT scans until a certain precision of the saturation for a noiseless signal:  
\[Number\ of\ scans=\frac{log_{10}\epsilon}{log_{10}\alpha} \tag{21}\]  
where \(\epsilon\) is the precision of the saturation. The number of scans is an approximation of the total required FFT packets until partial saturation.
# Deep Learning-Enhanced Spectrogram Analysis for
###### Abstract  
Accurate classification of biomedical signals is essential for advancing non-invasive diagnostic techniques and improving clinical decision-making. This study introduces a deep learning-augmented spectrogram analysis framework for classifying biomedical signals into eight anatomically distinct regions, thereby addressing a significant deficiency in automated signal interpretation. The proposed approach leverages a fine-tuned ResNet50 model, pre-trained on ImageNet, and adapted for a single-channel spectrogram input to ensure robust feature extraction and high classification accuracy. Spectrograms derived from palpation and percussion signals were preprocessed into grayscale images and optimized through data augmentation and hyperparameter tuning to enhance the model's generalization. The experimental results demonstrate a classification accuracy of 93.37%, surpassing that of conventional methods and highlighting the effectiveness of deep learning in biomedical signal processing. This study bridges the gap between machine learning and clinical applications, enabling an interpretable and region-specific classification system that enhances diagnostic precision. Future work will explore cross-domain generalization, multi-modal medical data integration, and real-time deployment for clinical applications. The findings establish a significant advancement in non-invasive diagnostics, demonstrating the potential of deep learning to refine and automate biomedical signal analysis in clinical practice.  
biomedical signal processing; spectrogram-based feature extraction; deep learning; ResNet50-based classification; anatomical region classification; non-invasive medical diagnostics
## 3 Numerical Model and Method
### EDEM-CFD FLUENT Coupling Details  
The fluid phase is, at the start, analyzed by ANSYS-FLUENT using the solutions of the Navier-Stokes equation and the standard k-epsilon turbulence model. For a given time step, iteration of the simulation is performed in Fluent until convergence is reached.  
Then, EDEM calculates body forces acting on each particle (gravity, fluid drag, electrostatic) using data extracted from fluid mesh cells, and eventually determines updated particle position, acceleration and velocity. These different coordinates of the particles thus updated are transferred to the CFD solver ANSYS-FLUENT and the forces exerted on the fluid by the particles are introduced into the fluid through a series of momentum source terms.  
In the EDEM-CFD FLUENT coupling, EDEM replaces the solid phase approximation in FLUENT with an explicit computation of particle dynamics in EDEM.  
In this study, the Gidaspow model, which is a combination of the Wen and Yu model and the Ergun equation, was used. Moreover, the soft sphere model was adopted to solve all the problems of particle deformation and collision of each particle. In every time step the result from EDEM was transferred to FLUENT and there, the equations of continuity and momentum conservation were solved for each time step. To avoid a large solids kinetic energy dissipation at the wall region and to avoid allowing some particles to stay in the wall region, a fixed restitution and friction coefficients were chosen (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Units** \\ \hline Fluid density & 1000 & kg/m\({}^{3}\) \\ \hline Solid density & 2500 & kg/m\({}^{3}\) \\ \hline Kinematic viscosity of the fluid & 2.0 \(\times\) 10\({}^{-6}\) & m\({}^{2}\)/s \\ \hline Coefficient of friction of particle & 0.5 & - \\ \hline Coefficient of restitution of particle & 0.5 & - \\ \hline Grain average diameter & 1.610\({}^{-3}\) & m \\ \hline Initial volume fraction of sand & 0.52–0.62 & - \\ \hline Gravity & 9.81 & (m/s\({}^{2}\)) \\ \hline Duration of simulation & 10 & s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fluent-EDEM simulation parameters used for the study.
# Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging
## 3 Numerical Results  
### Numerical Simulations  
#### 3.1.1 Simulated US Images  
Two ultrasound data sets, named _group1_ and _group2_, were obtained by 2D convolution between spatially invariant PSFs and the TRFs [15]. The results were quantitatively evaluated in terms of peak signal-to-noise ratio (PSNR), image structural similarity SSIM [18], improvement in SNR (ISNR), and the normalized root mean square error (NRMSE). The metrics are expressed as follows:  
PSNR \[=10\log_{10}\frac{NL^{2}}{\|x-\hat{x}\|^{2}},\] (32) SSIM \[=\frac{(2\mu_{x}\mu_{x}+c_{1})(2\sigma_{xx}+c_{2})}{(\mu_{x}^{2}+ \mu_{x}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{x}^{2}+c_{2})},\] (33) ISNR \[=10\log_{10}\frac{\|x-y\|^{2}}{\|x-\hat{x}\|},\] (34) NRMSE \[=\sqrt{\frac{\|x-\hat{x}\|^{2}}{\|x\|^{2}}},\] (35)  
where \(x,y,\hat{x}\) are respectively the original image, the RF image, and the reconstructed image. \(L\) denotes the maximum intensity value in \(x\). \(\sigma_{x}\) and \(\sigma_{\hat{x}}\) are the mean and variance values of \(x\) and \(\hat{x}\); \(c_{1}=(k_{1}C)^{2}\) and \(c_{2}=(k_{2}C)^{2}\) are two variables stabilizing the division with a weak denominator. \(C\) represents the dynamic range of the pixel-values and \(k_{1},\ k_{2}\) denote constants. Herein, \(k_{1}=0.01\), \(k_{2}=0.03\), and \(C=1\).
# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 4 Proposed System
### Decentralized Applications
#### 4.2.2 DApp for Data Checking  
This DApp is designed to verify the consistency of training data throughout the entire pipeline. Before initiating model training, the system retrieves the previously stored cryptographic hash and compares it with a newly generated hash derived from the current version of the dataset. This comparison confirms whether the training data have remained unaltered, ensuring end-to-end data integrity.  
* Integrity Verification: Prior to granting an AI model access to the dataset, the system performs an integrity check by fetching the original cryptographic hash from the blockchain and comparing it with a freshly computed hash of the present dataset. A match between the two values validates that the data have not been modified or corrupted, thereby allowing the training process to proceed with high assurance. This verification step is crucial for maintaining the accuracy and reliability of training data, especially in safety-critical AI systems.  
Such verification is particularly vital in autonomous vehicle applications, where model performance, operational safety, and decision reliability are tightly linked to the quality and trustworthiness of the training data.
###### Abstract  
Rotator cuff-related shoulder pain (RCRSP) is a common musculoskeletal condition characterized by pain, functional disability, reduced mobility, and weakness. There is a need for valid functional tests that can measure shoulder strength and power without exacerbating pain. The Seated Medicine Ball Throw (SMBT) and Unilateral Shot-Put Test (ULSPT) are throwing tests that use a weighted ball in a seated position, measuring throwing distance (m). This study aimed to evaluate the feasibility, discriminative validity, and convergent validity of these tests in individuals with RCRSP. **Methods:** This cross-sectional study included 64 participants: 30 with RCRSP and 34 asymptomatic controls. Participants completed the QuickDASH and Fear-Avoidance Beliefs Questionnaire (FABQ). Pain was assessed using a 10 cm visual analog scale (VAS) at multiple time points. The SMBT and ULSPT were performed using a 2 kg ball, with throwing distance calculated as the average of three trials. Active shoulder range of motion (AROM) and grip strength were also measured. A two-way mixed-model repeated-measures ANOVA was conducted to examine group effects, with post hoc analyses performed where relevant. Pearson correlations explored associations among outcome measures. **Results:** The RCRSP group presented with persistent moderate shoulder pain (mean duration = 6.33 \(\pm\) 5.7 months, VAS = 5.03 \(\pm\) 1.99 cm, QuickDASH = 26.2 \(\pm\) 10.54). Pain did not significantly increase after throwing (VAS change = 0.5 \(\pm\) 1.6 cm, \(P\) = 0.4), supporting the tests' feasibility. ULSPT demonstrated significant differences between the RCRSP and control groups for both symptomatic (2.03 \(\pm\) 0.81 m) and asymptomatic shoulders (2.04 \(\pm\) 0.8 m) compared with controls (2.51 \(\pm\) 0.93 m, \(P\) < 0.01). SMBT showed a trend toward group differences (\(P\) = 0.05). RCRSP participants showed reduced AROM (166.2 \(\pm\) 10\({}^{\circ}\) vs. 175.1 \(\pm\) 8.2\({}^{\circ}\)) but similar grip strength compared to controls. ULSPT strongly correlated with SMBT (r = 0.92-0.94, \(P\) < 0.0001). Both throwing tests correlated moderately with grip strength (r = 0.61-0.81, \(P\) < 0.05) and showed mild to moderate correlations with disability, pain, and fear-avoidance measures (r = 0.26-0.48, \(P\) < 0.05). **Conclusions:** The ULSPT demonstrated good discriminative validity in differentiating individuals with RCRSP from controls, while the SMBT showed a trend toward discrimination. Both tests were feasible to administer without significantly exacerbating pain. The strong correlation between ULSPT and SMBT, along with their associations with established measures, supports their potential as functional assessments of upper extremity performance in RCRSP.  
seated medicine ball throw; unilateral shot-put test; rotator cuff-related shoulder pain; functional shoulder test; upper extremity function 4.0/. +
Footnote †: journal: Applied sciences