esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
It draws from the SSO update mechanism to generate offspring [18], the crowding distance to rank nondominated solutions [19], and new hybrid elite selection to select parents [20], and the limited number of nondominated solutions adapted from multi-objective particle swarm optimization (MOPSO) serves to guide the update [13]. The motivation and contribution of this work are highlighted as follows:  
1. An improved algorithm named two-objective simplified swarm optimization (tSSO) is developed in this work to revise and improve errors in the previous MOSSO algorithm [17], which ignores the fact that the number of temporary nondominated solutions is not always only one in the multi-objective problem, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation. The algorithm is based on SSO to deliver the job scheduling in cloud computing. 2. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective time-constrained task scheduling problem in a more efficient manner. 3. In the experiments conducted, the proposed tSSO outperforms existing established algorithms, e.g., NSGA-II, MOPSO, and MOSSO, in the convergence, diversity, number of obtained temporary nondominated solutions, and the number of obtained real nondominated solutions. The remainder of this paper is organized as follows. Related work in the existing literature is discussed in Section 2, which also demonstrates how this work is different in its approach. Section 3 presents notations, assumptions, and the mathematical modeling of the proposed two-objective time-constrained job scheduling problem to address energy consumption and service quality in terms of the makspan. Section 4 introduces the simplified swarm optimization (SSO) [18], the concept of crowd distance [19], and traditional elite selection [20]. The proposed tSSO is presented in Section 5, together with the discussion of its novelties and pseudo code. The section also explains the errors in the previous MOSSO algorithm and how the proposed new algorithm overcomes them. Section 6 compares the proposed tSSO in terms of nine different parameter settings with the MOSSO proposed in [17], the MOPSO proposed in [13], and the famous NSGA-II [19; 20]. Three benchmark problems, one small-size, one medium-size, and one large-size, are utilized and analyzed from the viewpoint of convergence, diversity, and number of obtained nondominated solutions in order to demonstrate the performance of the tSSO. Our conclusions are given in Section 7.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 4 Dosimetric Models
### Human Head--Simplified 3D Models  
To highlight the source of numerical artifacts, before considering the complete 3D model described in Section 4.2, two simplified 3D models were created. The first model is obtained by rotating a cutplane of Colin27 average brain (Figure 4b) along the longitudinal axis (axis 2 of the figure) by 2\(\pi\) rad. The result is the axisymmetric model represented in Figure 4c. In this case, a uniform vertical magnetic field is used as the source. The second model is obtained by mirroring the cutplane of Figure 4b with respect to the sagittal plane and extruding the result by a thickness of 2 mm along the \(z\)-axis. The result is the 3D representation of a planar geometry reported in Figure 4d. An impressed external field parallel to the extrusion direction is considered as source. The added value of these simplified models is that they can be simulated as 2D axisymmetric and 2D planar problems. The 2D solutions obtained with a very fine mesh are used as reference for the 3D simulations, making it possible to investigate and understand the causes of the numerical artifacts. The mesh related to the 2D head models has been manually tuned in order to achieve accurate results without the need for any filtering of the raw solution. For instance, all sharp edges were pre-processed with an automated program, imposing a minimum curvature radius of 1 mm between each of the adjacent boundary edges, as shown in Figure 5.  
Figure 4: Colin27 Average Brain: 3D model (**a**), 2D cut-plane (**b**), axisymmetric model (**c**) and planar model (**d**).
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### DL for Beamforming and Selection  
The performance of 5G technology is also determined by the beamforming process. A quick unsupervised learning-based beamforming design methodology has been put out by authors in [108]. In this approach, DNN is trained offline and provides real-time assistance for simple neural network tasks once it is online. DNN in the downlink records the channel's characteristics, takes the channel coefficients as input, and produces a beamformer. Pruned DNN is used because it decreases the parameters and, as a result, the computational complexity and time required by DNN. The simulations showed that, although degrading  
Figure 6: Performance comparison of MIMO-NOMA-DL and MIMO-NOMA-SIC [103].  
with increasing SNR and transmit antenna count, deep neural network performance is comparable to that of WMMSE. In [109], DL-aided hybrid beamforming (HB) is suggested, where supervised learning and an autoencoder build the HB. Compared to other traditional beamforming methods, this approach performed better in the context of bit error rate. A recent study [110] introduces a novel MIMO-NOMA system that addresses partial CSI feedback. Channel quality information (CQI), the best beam, and beam correlation are used to cluster users. The user pair chosen for clustering had the greatest CQI differential and the highest beam correlation. HB is created through clustering. As an analog beamforming vector, the best beam from a high data rate user is selected. Thus, inter-cluster interference is decreased. For weak users, digital beamforming is used to reduce intra-cluster interference. Furthermore, the authors developed a system with efficient power allocation by optimizing the power differential between UEs in a cluster while subject to rate constraints. The system that was presented had a greater sum rate. With DL, choosing a beam is simpler. Using two optimal beam indices as inputs and an estimated power delay profile (PDP) as a label, the DL model is trained using supervised learning. Adam is used for optimization, cross-entropy is utilized as the cost function, and softmax activation is employed at the output layer, where the number of beams equals the number of neurons [111]. DNN may be used to perform beam selection and hybrid beamforming with little latency. Additionally, it produces better results from the perspective of summation rate and BER.
Thirdly, with two identified operating regions corresponding to small wind speeds and large wind speeds, feasible control is proposed dedicated to improved tracking performance under disturbance rejection. For this, realistic ramp-like linear wind speed variation disturbances are considered and the possibility of reducing the steady-state errors in the controller variables of interest is investigated. Using a linearized model, we show that in the MPPT operation mode of the first analyzed region corresponding to smaller wind speeds, we cannot use a stabilizing double integrator within the MAS controller, as required by the internal model principle from control theory. The discussion and validation of control approaches in such a wide operating range of wind speed variation has not been tackled properly in the analyzed literature. Fourthly, we propose the original application of modern control algorithms and compare their performances. These algorithms are VRFT control, sliding-mode control and model predictive adaptive control (MPAC). Comparing the performances of such controllers requires careful design and preparation, which are discussed in detail herein. Last but not least, the use of an architecture with two power converters in the proposed combination of WT and doubly fed induction generator is a first proposal in the literature, aimed at maximizing energy capture over the entire wind speed operation range. This paper discusses the mathematical model of the analyzed WT in Section 2, both in the blade power characteristic identification and in the complete characterization of the electric doubly fed induction generator and its associated stator and rotor converters. Section 3 is dedicated to the WT control in the operating region corresponding to low wind speeds and it is aimed at ensuring MPP operation. Section 4 aims for reliable WT control in the second operating region of interest corresponding to high wind speeds, where the MAS and the generator value must be saturated. Section 5 concludes the results and points out future research directions.
## 3 Theoretical Analysis
### The Droplet Formation Process  
Figure 5 shows the modeling of the droplet formation process in one cycle. It can be seen from the figure that when the pressure in the micro-cone hole is positive, the liquid in the cone hole flows outward to form a liquid column; when the pressure in the cone hole is negative, the liquid droplets will continue to flow outward for a period of time due to inertia; with the continuous negative pressure in the micro-cone hole, the liquid column begins to flow back into the hole, then the liquid begins to move along the inner wall of the cone hole to the hole, and the liquid part in the hole. Gradually fined and then fractured to form atomized droplets. It can be found from the figure that the outflow liquid flows outward at the center faster than the liquid near the outlet, so when the liquid at the outlet begins to move into the hole, the outermost liquid still keeps flowing outward, which makes the droplet appear as thinning neck near the outlet, thus creating conditions for liquid fracture.
## 1 Introduction
The method seeks to classify the strata with a complete architectural classification system (such as the four classes (lamina, lamina set, bed, and bedset) of [PERSON] (1975) [31] or the three-level hierarchical framework (story, element, and archetype) of [PERSON] and [PERSON] (2014)) [7] to describe each layer and its interrelation while considering the sedimentary processes, scale, and temporal context of reservoir and nonreservoir layers and, finally, to arrange the sedimentary system into an ordered whole with genetic relations among multiple levels and multiple structures. At present, this theory has been widely used in the study of slope turbidite systems in deep water [35], marginal-marine systems [33], submarine fans [36], fluvial systems [7; 34; 7], and carbonate reservoirs. In particular, the application to fluvial systems (mainly meandering rivers) solves the problems of filling types, scale, temporal context, architectural elements, and the hierarchical order of boundary surfaces in the previous process of describing river sedimentary systems [7] and promotes the understanding and prediction of the distribution and heterogeneity of alluvial architecture [34]. However, limited by the lack of outcrops, except for the description of the meso- and macroscale of braided rivers in the process of dividing the amalgamated, multistory sedimentary strata composed of channel-fill (braided to meandering) and splay deposits with floodplain fines, a hierarchical analysis approach has not been applied to the detailed anatomy of braided river architectural structure and the analysis of its sequential evolution. The 3D outcrops of the braided river system in the Songyuan area of the Songliao Basin are the Quaternary sedimentary strata of the Second Songhua River, which provides an ideal measured profile for the introduction of a hierarchical analysis approach to complete the description of a micro-macro braided river system. Therefore, based on the outcrop data from the Songliao Basin, this study contributes to resolving this gap by (1) validating and refining existing hierarchical schemes through well-exposed 3D outcrops of Quaternary braided rivers in the Songliao Basin; (2) establishing a quantitative architectural model specific to coarse sandy-gravelly braided systems under seasonal hydrological regimes; and (3) providing empirical relationships between channel dimensions that bridge modern and ancient fluvial systems in similar climatic settings.
## 2 Materials and Methods
### Main Channel and Surrounding Topography Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point Number** & **x [m]** & **y [m]** & **h [m]** & **Code** \\ \hline
171 & 1,208,607.636 & 263,347.438 & 245.110 & zones beyond the bank line \\
172 & 1,208,605.987 & 263,340.982 & 241.945 & zones beyond the bank line \\
173 & 1,208,604.824 & 263,337.560 & 241.349 & zones beyond the bank line \\
174 & 1,208,604.792 & 263,335.523 & 239.509 & berm \\ W1 & 1,208,603.994 & 263,331.242 & 238.518 & bottom of watercourse \\ W2 & 1,208,601.794 & 263,319.450 & 238.542 & bottom of watercourse \\
104 & 1,208,601.024 & 263,315.321 & 239.550 & berm \\
103 & 1,208,600.601 & 263,313.695 & 241.329 & zones beyond the bank line \\
102 & 1,208,599.858 & 263,310.369 & 242.101 & zones beyond the bank line \\
101 & 1,208,597.897 & 263,304.328 & 245.040 & zones beyond the bank line \\ \hline \hline \end{tabular} Note: x, y—horizontal information for the localization of measured detail survey points given in the D-UTCN—coordinate system (Datum of Uniform Trigonometric Cadastral Network), h—the altitude of points expressed in the Baltic Vertical Datum—After Adjustment, Code—identification of the measured detail survey point.  
\end{table} 
Table 2: A sample of processing of measured points. Figure 3: Presentation of RTK points on the case study. \[\text{m}_{3D}=\sqrt{(\text{m}_{\text{x}})^{2}+(\text{m}_{\text{y}})^{2}+(\text{m}_{ \text{h}})^{2}} \tag{1}\]  
where:  
\(\text{m}_{3D}\)--spatial accuracy of determination of the point's position,  
\(\text{m}_{\text{x}}\), \(\text{m}_{\text{y}}\)--accuracy of determination of the point's position in the direction of x and y axes,  
\(\text{m}_{\text{h}}\)--accuracy of determination of the point's height. The examined statistical set consists of 516 detailed points (Figure 4), in which 3D coordinates were measured by the RTK kinematic method and their standard deviation \(\text{m}_{3D}\) was determined by the relation (1). The \(\text{m}_{3D}\) at any observed point did not exceed value 4 cm, which is guaranteed in Slovakia by the SRTPS. Nevertheless, the file was examined for outliers. The null hypothesis \"All data values come from the same normal population\", against the alternative hypothesis that \"The largest data value is an outlier\" was examined by the Grubb's test. The test result showed that there is no outlier at the 5% level of significance. As is obvious from Figure 5 and also from the cumulative distribution (Figure 6), there is a certain degree of asymmetry of the \(\text{m}_{3D}\) file. Figure 4: Presentation of the spatial accuracy of the point’s position. Figure 5: [PERSON]’s Test of Outliers. From the Anderson-Darling Normality Test (Figure 7) at the significance level \(\alpha\) = 5% arise that the data do not follow a normal distribution. The m\({}_{\text{3D}}\) values deviate considerably from the red line representing the normal distribution. This conclusion is confirmed by the fact that the \(p\)-Value (0.005) is less than \(\alpha\).
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 4 Simulation Results and Experimental Verification
### Analysis and Comparison of Algorithm Complexity  
The MATE algorithm employs a stage-wise processing strategy. The initialization phase (i.e., the first signal cycle) achieves parameter acquisition through FFT and IFFT operations, while subsequent cycles enter the tracking phase requiring only addition and multiplication operations. In contrast, the GCC algorithm necessitates FFT and IFFT computations every cycle.  
Table 2 presents a comparative analysis of the time complexity and execution time for both algorithms, measured on the MATLAB R2022a platform using an AMD Ryzen 7 7735H processor with Radeon Graphics@ 3.20 GHz. The processor is manufactured by Advanced Micro Devices, Inc. (AMD), whose headquarters are located in Santa Clara, CA, USA.  
In Table 2, \(K\) represents the number of signal periods, and \(N\) represents the number of sampling points per signal period. Clearly, both the time complexity and running time of the MATE algorithm are significantly lower than those of the GCC algorithm. The measured execution time for processing the same dataset confirms this substantial efficiency improvement, with MATE requiring approximately 57.67% less time than the GCC approach.
# A Systematic Experimental and Computational Analysis of Commercially Available Aliphatic Polyesters
###### Abstract  
Aliphatic polyesters, such as polylactic acid (PLA), polyglycolic acid (PGA), and their copolymer polylactic-co-glycolic acid (PLGA) have become an established choice in the biomedical field in a wide range of applications, from nanoparticles for local drug delivery to bone fixation screws, and, hence, in a huge spectrum of uses in different medical devices currently available on the market worldwide. The reason for their popularity lies in their combination of interesting peculiarities: in situ degradation, intrinsic biocompatibility (degradation products are recognized and metabolized), processability with standard industrial technologies, and tailorable properties. The knowledge of the degradation rate is an essential requirement for optimal device design when, e.g., fast adsorption time is required, or mechanical properties must be assured over a given time span. In this regard, experimental studies can be time- and money-consuming, due to the time scales (weeks-months) involved in the hydrolysis process. This work aims at providing to both industry and academia robust guidelines for optimal material choice through a systematic experimental and computational analysis of most commonly used PLGA formulations (selected from commercially available products), evaluating the degradation kinetics and its impact on polymer properties.  
Keywords:biopolymers; aliphatic polyesters; degradation: mathematical modeling +
Footnote †: journal: Applied Soft. 2019, 9, 3397; doi:10.3390/app916397
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 3 Results
### ROC Curve Based on a Logistic Regression Model and AUC Analysis  
Table 4 compares the receiver operating characteristic (ROC) curve analyses of two logistic regression models--one incorporating HOMA-IR, gender, age, and Diabetes Mellitus status (combined model), and another using HOMA-IR alone (HOMA-only model). Overall, the combined model yielded higher predictive performance than the HOMA-only model. When stratified by age, the predictive accuracy was strongest among younger adults and gradually declined in older age groups. However, the combined model outperformed the HOMA-only approach in every age category, suggesting that gender, age, and Diabetes Mellitus status add significant value to HOMA-IR for predicting Met5. These findings are visually presented if Figure 2. Which illustrates the ROC curves for both models across different age groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Age** & **Model \({}^{*}\)** &
\begin{tabular}{c} **Cut-Off** \\ **Value \({}^{*}\)** \\ \end{tabular} & **Sensitivity** & **Specificity** & **AUC (95\%CI)** & \(p\)** \\ \hline Overall & \multirow{2}{*}{Combined} & \multirow{2}{*}{0.241} & \multirow{2}{*}{0.800} & \multirow{2}{*}{0.689} & \multirow{2}{*}{0.819 (0.807–0.829)} & \multirow{2}{*}{\(<\)0.001} \\ (\(n\) = 6134) & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \cline{3-6}  & & & & & \\ \hline \hline \end{tabular} \({}^{*}\) Combined model incorporated HOMA-IR, sex, age, and Diabetes Mellitus (DM) status as covariates, while the “HOMA-IR only” model used HOMA-IR alone. ROC refers to the receiver operating characteristic, HOMA-IR denotes the homeostasis model assessment of insulin resistance, DM stands for Diabetes Mellitus, and AUC represents the area under the curve. The presented cut-off values represent the optimal predictive probability thresholds for MetS classification.  
\end{table}
Table 4: ROC regression model coefficients.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **B** & **Odds Ratio (95\%)** \\ \hline HOMA-IR & 0.448 & 1.566 (1.504–1.630) \\ Sex & 0.391 & 1.479 (1.303–1.678) \\ Age & 0.033 & 1.033 (1.029–1.037) \\ Diabetes Mellitus & 0.649 & 1.914 (1.572–2.330) \\ Intercept & \(-\)4.067 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multivariate logistic regression analysis of the factors associated with metabolic syndrome.
## 3 Model Violations
### Materials
#### 3.3.2 Spatial Dispersion  
The introduction of the dielectric function according to Equations (1) and (2) does not consider effects caused by non-locality of the dielectric response [12]. Indeed, it may happen that the polarization in a given point in space depends on the electric field in that point, and additionally on the field in surrounding points (non-local response). Then, instead of Equation (1), we should write the following:  
\[\mathbf{P}(t,\mathbf{r})=\epsilon_{0}\int_{0}^{\infty}\int_{V}\kappa(\xi,\mathbf{R})\mathbf{E} (t-\xi,\mathbf{r}-\mathbf{R})d\xi d\mathbf{R} \tag{21}\]  
The response function \(\kappa\) is now time- and coordinate-dependent. \(V\) denotes the volume where \(\kappa\) is essentially different from zero.  
Non-locality has a striking effect on the dielectric function. It is now defined as follows:  
\[\begin{gathered}\varepsilon=\varepsilon(\omega,\ \mathbf{k})=1+ \chi\\ \chi=\int_{0}^{\infty}\int_{V}\kappa(\xi,R)e^{i(\omega\xi-\mathbf{R} )}d\xi d\mathbf{R}\equiv\chi(\omega,\mathbf{k})\end{gathered} \tag{22}\]  
The \(\mathbf{k}\)-dependence of \(\varepsilon\) is called spatial dispersion. As a rule of thumb, one may note that the usual dispersion becomes significant when the oscillation period of the light becomes comparable with _characteristic times_ describing the dynamics of the medium (resonance phenomena). Spatial dispersion, on the contrary, becomes significant when the wavelength of the light becomes comparable with _characteristic spatial dimensions_ in the medium. Usually, when performing spectroscopy in the visible and adjacent spectral regions, spatial dispersion may therefore be neglected.  
However, there are exclusions from this rule. In metal island films, spatial dispersion may matter [108; 109]. Also, as pointed out already in [12], non-local response effects may impair the practical relevance of the Kramers-Kronig relations Equations (10) and (11), particularly in metals. Generally, an increase in the refractive index near a resonance will decrease the wavelength of the propagating light, which may also give rise to effects of spatial dispersion. In particular, it may give rise to optical anisotropy in an otherwise isotropic material (compare Section 3.3.1). Note, in this context, that non-locality is naturally included in the quantum mechanical expression for the dielectric function in semiconductor theory [110; 111].
## 3 Methodology
### Overview of the DeepSORT Architecture  
Figure 2 shows the architecture of the DeepSORT algorithm [26]. This architecture fuses tracking approaches that rely on deep learning with object detection techniques. The algorithm detects and identifies objects in images using detection models such as YOLOv5 and then uses DeepSORT to track these objects accurately [27]. This visual representation shows the critical steps of the architecture, including initial object detection, fusion of successive images, and creation of tracking trajectories. The DeepSORT architecture tracks specific objects in various applications, such as customer tracking in e-commerce.