esa-sceva/satcom-chunk-collection huggingface dataset:
# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 2 Materials and Methods
### Model Outcomes and Performance
#### 2.4.4 Variability and Optimal Sample Size  
Markov CH models yield deterministic results with single point estimates for each outcome, while PL models produce stochastic results with a range of estimates due to inherent variability, often referred to as first-order uncertainty. This variability can be reduced by increasing the number of simulated patients. To assess the impact of this variability in the PL model, we analyzed the mean and the standard deviation (SD) of the NMB measure, calculated as:  
\[\text{NMB}=\text{(Incremental Costs)}-\text{(Incremental QALYs }\times\text{Threshold)} \tag{3}\]  
where the willingness-to-pay threshold was set at 34,764 EUR/QALY [16]. We computed the coefficient of variation (CV) for NMB, defined as SD divided by the mean. To find the optimal cohort size, we conducted 100 simulations for various cohort sizes and compared the absolute CV values, their relative changes with cohort size, and the associated computational burden.  
In detail, to determine the optimal sample size, we calculated the NMB and analyzed its mean and SD across different sample sizes, using CV as a key metric. We conducted 100 simulations for various cohort sizes and compared the absolute CV values, their relative changes, and the associated computational burdens. The results demonstrated that increasing the sample size initially resulted in substantial reductions in variability, thereby improving model accuracy. However, beyond a certain point, further increases in sample size yielded diminishing returns, with only marginal improvements in CV despite a significant rise in computational time. This analysis guided our decision to select 10,000 patients as a balance between achieving model precision and maintaining computational efficiency.
## 2 Material and Methods
### Techniques  
#### 2.3.1 Characterization of Bulk Dispersions  
Dynamic light scattering (DLS) experiments were performed in a quasi-backscattering configuration, with a scattering angle (\(\theta\)) of 173\({}^{\circ}\), using a Zetasizer Nano ZS instrument from Malvern Instruments Ltd. (Malvern, UK). The DLS experiments were performed using red-line radiation emitted by a He-Ne laser at a wavelength (\(\lambda\)) of 632 nm [21]. The DLS experiments allowed us to obtain the apparent diffusion coefficient, \(D_{\mathrm{app}}\), at a constant temperature of 22 \({}^{\circ}\)C for scatters dispersed in a liquid, assuming their Brownian motion. From the values of \(D_{\mathrm{app}}\), it is possible to evaluate the size of the scatters in terms of the apparent hydrodynamic diameter of the scatters, \(d_{h}^{app}\), assuming the validity of the Stokes-Einstein equation:  
\[d_{h}^{app}=\frac{k_{B}T}{3\pi\eta D_{app}}, \tag{1}\]  
where \(k_{\mathrm{B}}\) and \(T\) are the Boltzmann constant and the absolute temperature, respectively, and \(\eta\) is the viscosity of the continuous phase. It should be noted that the DLS technique can only be used to analyze transparent dispersions in such a way that multiple scattering phenomena can be avoided.  
The effective charge density of Pluronic-F-127-decorated silica nanoparticles can be determined by measuring the electrophoretic mobility (\(u_{\epsilon}\)) using laser Doppler velocimetry with a Zetasizer Nano ZS instrument from Malvern Instruments Ltd. (Malvern, UK). The electrophoretic mobility is directly proportional to the zeta potential (\(\lambda\)), which provides a quantification of the effective charge carried by the colloids dispersed in the aqueous medium [22; 23].
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 1 Introduction
This study aimed to enhance the structural safety of electric scooters by applying topology optimization to the headtube, a known structural weakness, under multiple-load conditions. By adopting this approach, this study aimed to prevent structural failures and improve the overall safety of electric scooters. A representative electric scooter was modeled as a finite element (FE) model, and topology optimization of the headtube was conducted for 16 single-load scenarios. Subsequently, topology optimization was performed by jointly considering multiple-load conditions. Finally, the optimized design was analyzed, and its collision performance was compared with those of two market-leading models.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Pressure Measurements
#### 4.2.6 Hybrid Pressure Sensor Usage  
[PERSON] et al. [140] utilise a large-scale experimental set-up for measuring spatially and localised loading caused by the detonation of shallow-buried [141; 142; 143] and near-field free-air explosives [144]. They explore a range of instrumentation techniques, which are summarised in a tabulated format. The options include:  
* Hopkinson pressure bars (HPBs);
* Stress gauges;
* Piezo electric/resistive pressure gauges;
* Impulse momentum traps;
* Load cells.  
[PERSON] et al. [140] were interested in capturing peak overpressures whilst also aiming to measure _\"long duration stagnation pressures\"_. From their initial instrumentation considerations, it was apparent that a single pressure sensor type could not achieve the desired measurements suitably. Therefore, the decision was made to utilise multiple sensor types. Hopkinson [145] pressure bars (JPBs) were used to measure peak overpressures in conjunction with load cells for the longer-duration pressures.  
The duration for which a HPB can record loading is determined by the time taken for an elastic stress pulse to reach the end of the bar and return to the location of the strain gauge. Consequently, the maximum measurable load duration is limited by the length. As used in experimental testing, HPBs with lengths of 3.25 m could measure for a load duration of 1.2 ms. The HPBs used could achieve an effective sampling rate of up to 250 kHz with a radius of 5 mm for the bars (selected to balance sampling rate and the need for a larger bar diameter to capture representative loading from discrete particle strikes). HPBs have been used successfully as dynamic force transducers [146; 147; 148; 149; 150; 151; 152; 153]; however, the need for a considerable bar length limits the scenarios and positions where HPBs can be utilised within experimental testing. More information on the use of HPBs for blast load characterisation is available in the review of [PERSON] et al. [156].
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 2 Specifications of 5G CS and Literature Review  
In this section, we discuss the 3 GPP specifications for 5G CS PHY and review the works related to the hardware implementation of CS PHY.
# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
###### Abstract  
Most object detection methods use rectangular bounding boxes to represent the object, while the representative points network (RepPoints) employs a point set to describe the object. The RepPoints can provide more fine-grained localization and facilitates classification. However, it ignores the difference between localization and classification tasks. Therefore, a lightweight RepPoints with decoupling of the sampling point set (LRP-DS) is proposed in this paper. Firstly, the lightweight MobileNet-V2 and Feature Pyramid Networks (FPN) is employed as the backbone network to realize the lightweight network, rather than the Resnet. Secondly, considering the difference between classification and localization tasks, the sampling points of classification and localization are decoupled, by introducing classification free sampling method. Finally, due to the introduction of the classification free sampling method, the problem of the mismatch between the localization accuracy and the classification confidence is highlighted, so the localization score is employed to describe the localization accuracy independently. The final network structure of this paper achieves 73.3% mean average precision (mAP) on the VOC07 test dataset, which is 1.9% higher than original RepPoints with the same backbone network MobileNetV2 and FPN. Our LRP-DS has a detection speed of 20 FPS for the input image of (1000, 600), on RTX2060 GPU, which is nearly twice as fast as the backbone network of ResNet50 and FPN. Experimental results show the effectiveness of our method.  
RepPoints; LRP-DS; decouple; sample; mismatch +
Footnote †: journal: Applied
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 2 System Model
### Channel Model  
In this work, both tiers are working in a sub 6-GHz band. An Orthogonal Frequency Division Multiplex (OFDM) communication system is assumed, resulting in a flat-fading channel for each sub-carrier; the channel between the \(i\)-th antenna in the BS of the \(m\)-th cell of \(k\)-th tier, and the \(j\)-th user associated with the \(n\)-th cell of the \(l\)-th tier, has been modeled with a single-tap channel coefficient [15].  
\[g_{(mi)k}^{(nj)l}=\sqrt{\gamma_{(m)k}^{(nj)l}}h_{(mi)k}^{(nj)l}. \tag{1}\]  
The subscript \((mi)k\) and superscript \((nj)l\) represent the index of the transmitting antenna at the BS and user, respectively. \(\gamma_{(m)k}^{(nj)l}\) and \(h_{(mi)k}^{(nj)l}\) are the coefficients capturing the large-scale and small-scale fading effect of the channel, respectively. \(\gamma_{(m)k}^{(nj)l}\in\mathbb{R}^{+}\) is the combination of the path attenuation, depending on the distance between user and the BS, and the shadowing effect due to the propagation environment. The value is modeled with \(10 log_{10}(\gamma_{m}^{n})=(-127.8-35\log d_{m}^{n}+X_{\varphi^{2}})\)dB, where \(d_{m}^{n}\) is the Euclidean distance between the corresponding \(n\)-th user and the \(m\)-th BS, and \(X_{\varphi^{2}}\) is a log-normally distributed random variable with zero mean and \(\sigma^{2}\) dB variance [26]. \(h_{(mi)k}^{(nj)l}\in\mathbb{C}\) is a random variable drawn from an independent Rayleigh distribution, \(h_{(mi)k}^{(nj)l}\sim\mathbb{C}(0,1)\). Considering the block-fading channel, the expected value of the channel coefficient within the coherence time is given by \(E[g_{(mi)k}^{(nj)l}]=\sqrt{\gamma_{(m)k}^{(nj)l}}\). The channel coefficient vector between the BS of the \(m\)-th cell in the \(k\)-th tier, and the \(j\)-th user of the \(n\)-th cell in \(l\)-th tier is as follows  
\[g_{(m)k}^{(nj)l}=\sqrt{\gamma_{(m)k}^{(nj)l}}h_{(m)k}^{(nj)l}\in\mathbb{C}^{N_ {k}X1}=\sqrt{\gamma_{(m)k}^{(nj)l}}\Big{(}h_{(m)k}^{(nj)l}h_{(m2)k}^{(nj)l}, \ldots,h_{(mN_{k})k}^{(nj)l}\Big{)}^{T}. \tag{2}\]
## 3 Results and Discussion
### Characterization and Properties of CRMFoams
#### 3.1.2 X-ray Diffraction Patterns and Raman Spectra
Specifically, the spectra of CRMFoams exhibit all the main Raman peaks corresponding to the graphitized carbon materials: (a) The graphite band (G-band), centered at \(\sim\)1580 cm\({}^{-1}\), which is due to the in-plane motion of sp2 carbon atoms in carbonaceous materials with high degree of symmetry and order (i.e., graphene, graphite, SWNTs) [52,53]. It is extremely sensitive to strain effects and is also a good indicator of the number of graphene layers. [54] (b) The defect activated band (D-band), centered at \(\sim\)1345 cm\({}^{-1}\), which is related to lattice motion away from the center of the Brillouin zone and is usually attributed to disordered carbon (sp3 hybridized carbon), exhibits high sensitivity to the disordered structures. (c) The second order D-band (2D), 2680 cm\({}^{-1}\) (exhibits a strong frequency dependence on the excitation laser frequency), which is a second-order two-phonon process Raman signature of sp2 carbons. It is used for the determination of the number of graphene layers, although its behavior is more complex than the frequency shift observed for the G band. (d) The Raman band D + G, at about 2940 cm\({}^{-1}\), which is associated with a D + G combination mode that it is also induced by disorder of carbon structure and the two weak bands at 2458 and 3240 cm\({}^{-1}\), which are assigned to the combination D + D\" and to the harmonic 2D', respectively (D\" corresponds to a phonon belonging to the in-plane longitudinal acoustic branch close to the K point) [52,53]. As it is also observed in Figure 2, the peak positions of the 2D and G bands, together with their intensity ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)\(\approx\) 0.84), for both spectra are well correlated with partially graphitized carbon. In addition, the 2D band profile could provide an indication of the graphitic layers' number [55]. The symmetric shape of the 2D band suggests the presence of graphitized nanosheets on the surface of CRMFoams. On the other hand, in the low frequency range of the spectra (200-800 cm\({}^{-1}\)), a number of weak bands are observed, which can be correlated with the vibrations of crystalline oxide and/or iron phases obtained upon pyrolysis of foam preforms, at high temperatures.
# A Graph Convolutional Network-Based Method for Congested Link Identification
###### Abstract
We introduce a congestion link identification method based on the GCN, innovatively converting the intricate Bayesian network-solving problem into a graph node classification task. 3. We conduct extensive simulations to compare the performance of the proposed GCN-based identification of congested link (GIC) method with existing techniques based on various metrics. The results validate the feasibility of the GIC algorithm in identifying congested links and underscore its advantages in scenarios involving node congestion and multiple congested links.
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
## 2 Frequency-Domain LLT-like Modelling
### Aerodynamic Loads
Collecting in the vector \(\mathbf{L}_{c}\) the values of the circulatory lift at the \(N_{B}\) wing sections, Equation (5) is recast in the following matrix form  
\[\tilde{\mathbf{L}}_{c}=\mathbf{C}(k)\left[\tilde{\mathbf{w}}^{i}+\tilde{ \sigma}_{n}^{3/4}\right]=\mathbf{C}(k)\left[\tilde{\mathbf{w}}^{i}+\mathbf{W} _{n}^{1}(k)\,\tilde{\mathbf{g}}\right] \tag{6}\]  
where \(\mathbf{W}_{n}^{1}\) is the \([N_{B}\times 2N_{B}]\) matrix relating the normalwash at every wing section to the corresponding values of sectional plunge and pitch that are collected in the following \([2N_{B}\times 1]\) vector (see Appendix A for details)  
\[\tilde{\mathbf{g}}=\left\{\tilde{w}_{1}\quad\tilde{\sigma}_{1}\quad\cdots \quad\tilde{w}_{N_{B}}\quad\tilde{\sigma}_{N_{B}}\right\}^{T} \tag{7}\]  
In addition, \(\mathbf{C}\) is a diagonal transcendental \([N_{B}\times N_{B}]\) matrix that, for a rectangular wing, can be simply expressed as  
\[\mathbf{C}=2\pi\rho bV\,C(k)\mathbf{I} \tag{8}\]  
with \(\mathbf{I}\) denoting the unit matrix (different values of the diagonal terms appear in the case of a non-rectangular wing). Note that two different vorticity models can be used to define the sectional lift:  
* Model 1 uses the complete [PERSON]'s lift-deficiency function (that implicitly takes into account shed vortices effect) and, therefore, the wake-induced velocity is evaluated including trailed vortices only in Equation (1);
* Model 2 uses the quasi-steady approximation \(C(k)=1\) (so that the [PERSON] theory reduces to the [PERSON] theory for airfoils in steady flows [1]) and, therefore, the wake-induced velocity is evaluated considering both shed and trailed vortices. The choice of the model affects both matrix \(\mathbf{C}\), through the function \(C(k)\), and the matrix \(\mathbf{D}\) (in Equation (1)), through the contribution of the influence coefficients, \(r_{ln}\). Similarly, noncirculatory lift and pitching moment can be expressed in the following matrix forms  
\[\tilde{\mathbf{L}}_{nc}=\mathbf{W}_{n}^{2}(k)\,\tilde{\mathbf{g}} \tag{9}\]  
\[\tilde{\mathbf{M}}=\mathbf{W}_{n}^{3}(k)\,\tilde{\mathbf{g}}+\mathbf{B}\, \tilde{\mathbf{L}}_{c} \tag{10}\]  
where \(\mathbf{W}_{n}^{2}\) and \(\mathbf{W}_{n}^{3}\) are \([N_{B}\times 2N_{B}]\) matrices that relate, respectively, the noncirculatory lift and the contribution of the pitching moment independent from the circulatory lift to the sectional bending and torsion degrees of freedom (see Appendix A for details), while \(\mathbf{B}\) is a diagonal \([N_{B}\times N_{B}]\) constant-coefficient matrix providing the effect of the arm of the circulatory lift with respect to the elastic axis position, namely for rectangular wings  
\[D_{lm}(k)=\sum_{n=1}^{N_{W}}e^{-ik\tau_{n}}\tau_{ln}S_{nm} \tag{2}\]0
# Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
###### Abstract  
Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.  
real estate prices; machine learning; predict +
Footnote †: journal: Applied sciences
# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 7 Conclusions and Future Work  
This paper investigated the trends in diabetes prevalence in the Saudi adult population using historical diabetes data, along with smoking, obesity, and inactivity data as predictor variables, employing five different regression modelling techniques. Various evaluation criteria, including MSE, RMSE, MAPE, and R\({}^{2}\), were used to assess the performance of each model. The results showed that there was little difference in the performance of the models when using datasets for men and women. However, the ANFIS model consistently performed well in predicting the overall prevalence of diabetes for both men and women, as well as for each age group. For practical applications, we recommend the ANFIS model as a reliable and effective tool for diabetes prediction. However, this recommendation is based on data from the Saudi population, and further studies are needed to validate its performance in other populations. The findings also indicated that demographic factors (such as age and gender), as well as behavioural risk factors, significantly contribute to the increased prevalence of diabetes. Among these, smoking, obesity, and physical inactivity were identified as the most significant contributors.  
For future research, it would be beneficial to explore the impact of integrating additional risk factors for diabetes in Saudi Arabia, such as diet and blood pressure. Additionally, considering non-modifiable risk factors, including family history and gestational diabetes, could further improve predictions. Expanding the range of risk factors could enhance the accuracy of diabetes prevalence predictions. Furthermore, investigating the application of machine learning techniques to predict the risk of diabetes-related complications, such as nephropathy, retinopathy, and cardiovascular diseases, could provide valuable insights. These efforts could not only help individuals with diabetes live healthier lives but also reduce the rising costs of healthcare.  
Conceptualisation, E.A. and M.A.; methodology, E.A. and M.A.; investigation, E.A.; writing--original draft preparation, E.A.; writing--review and editing, E.A. and Z.H.; supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be shared upon request.  
The authors declare no conflicts of interest.
## 3 Results  
### Demographic and Clinical Findings  
A total of 60 patients were recruited, who had nodules \(\leq\)1 cm (n = 69 nodules); of these, three patients were excluded, with four nodules categorized as BI-RADS 3, who opted for the US follow-up after 6 months. Of the 57 patients left, the average age was 45.6 \(\pm\) 11.9 with an age range from 19 to 70 years old; 65 nodules were included, with an average size of 7.5 \(\pm\) 1.7 mm (range: 4-10 mm). Of the 65 nodules, 53 were categorized  
Figure 1: Shear wave elastography patterns. Pattern 1 (**a**)—a pattern of nodular elasticity similar to adjacent tissue. Pattern 2 (**b**)—a pattern of elasticity that is soft in most of its area. Pattern 3 (**c**)—a pattern of elasticity that is stiff in most of its area. Pattern 4 (**d**)—a pattern of elasticity that is stiff over the entire nodule without exceeding the limits of the lesion.  
as BI-RADS 3, and 12 as BI-RADS 4a through a B-mode ultrasound. Through the use of elastography, the distribution of the patterns were: cyst artifact (n = 13), pattern 1 (n = 4), pattern 2 (n = 31), pattern 3 (n = 13), and pattern 4 (n = 4). The BI-RADS 3 lesions (n = 53) were subjected to FNA, whereas a CNB was performed with the BI-RADS 4a lesions that showed elastography patterns 1, 2, or 3 (n = 8). The pattern 4 lesions were subjected to VAB (n = 4). The findings and the management algorithm are shown in Figure 2. All the patients with a benign diagnosis were subjected to a follow-up US 3 months later, and the stability of the findings was observed and the patients with a malignant diagnosis were subjected to magnetic resonance imaging for cancer staging, after which the appropriate treatment was provided.  
Of the 65 lesions, (Table 1), 15 were cysts, 46 were benign lesions, and three were malignant lesions (two ductal carcinomas in situ, and an infiltrating ductal carcinoma).  
Figure 2: Management algorithm. * Ultrasound. ** Shear wave elastography. BI-RADS—Breast Imaging Reporting and Data System (BI-RADS).