esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 3 Results and Discussion
### Effect of Different Sloping Wall Heights on Separation Performance  
Keeping the inlet velocity constant at 5 m/s, the radius of the upper discharge opening was selected in this study to be 90 mm, and its effect on the performance of the cyclone separator in separating cenospheres was analyzed by varying the height of the sloping wall. The purpose of this analysis is to investigate the specific effect of changing the height of the  
Figure 5: Newton’s efficiency for different upper discharge opening radii.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Upper Discharge Radius** & \begin{tabular}{c} **Separation Efficiency of** \\ **Cenospheres** \\ **\(\gamma_{A}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Fly Ash Separation Efficiency** \\ **\(\gamma_{B}\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Newton’s Efficiency** \\ **\(\eta_{N}\)** \\ \end{tabular} \\ \hline
60 & 92.68 & 46.95 & 0.457 \\
70 & 92.68 & 46.95 & 0.457 \\
80 & 89.02 & 45.12 & 0.439 \\
90 & 92.86 & 47.02 & 0.458 \\
100 & 91.67 & 44.64 & 0.470 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Efficiency with different upper discharge opening radii.  
sloping wall on the separation efficiency and other performance indexes. Figure 6 shows the velocity vector plots derived from simulation calculations for sloping wall heights \(l_{2}\) of 400 mm, 450 mm, and 500 mm, respectively, and Figure 7 shows the particle trajectory plots for different sloping wall heights \(l_{2}\).  
When simulating the cyclone separator, by varying the size of the sloping wall height \(l_{2}\), we can observe, as shown in Figure 6, that the velocity vector diagram remains relatively stable under the change in the sloping wall height. Furthermore, as shown in Figure 7, by carefully analyzing the particle movement at the upper discharge opening, it was found that the number of escaping particles also shows a decreasing trend with the gradual decrease in the sloping wall height. In addition, as shown in Table 4, the decrease in sloping wall height was accompanied by a continuous decrease in separation efficiency and Newton's efficiency.  
Figure 6: Velocity vectors for different sloping wall heights.  
Figure 7: Particle trajectory plots for different sloping wall heights.
## 2 Materials and Methods
### Preparation of Conventional Emulsions (CEs) and Nanostructured Lipid Carriers (NLCs)  
Oil-in-water (_o/w_) conventional emulsions (CEs) and nanostructured lipid carriers (NLCs) were prepared. The ingredients used to prepare CEs were added in the ratios summarized in Table 1.  
For the preparation of a CE, the ingredients of the oil phase were melted in a water bath set at 70 \({}^{\circ}\)C. The water (aqua) was heated at the same temperature, and the two phases were mixed by vigorous stirring using a heating magnetic stirrer (ARE 5, Velp Scientific, Usmate Velate (MB), Italy) set at speed 4 until the emulsion reached ambient temperature. Finally, the preservative phenoxyethanol was added under continuous stirring. Three batches of the CE were prepared.  
The NLC was prepared from an aliquot of the prepared CE using a two-step process. First, the CE was subjected to high-energy ultrasonication using a Vibra-Cell(tm) sonicator (Sonics & Materials, Newtown, CT, USA) with the amplitude set at 83%. Sonication was performed for 1 min per milliliter of sample to reduce droplet size and achieve nanostructuring. Following ultrasonication, the resulting NLC was vortexed at 8000 rpm using a Vortex-Genie 2 (Scientific Industries, Bohemia, NY, USA) until it cooled to ambient temperature, ensuring uniform dispersion and preventing phase separation. Finally, the NLC  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ingredient (INCI)** & **CAS Number** & **Function** & **Composition** \\  & & & **(\% w/w)** \\ \hline Caprylic/capric triglyceride & 73398-61-5/65381-09-1 & Oil phase (liquid triglyceride) & 0.5 \\ Hydrogenated coco-glycerides & 91744-42-2 & Oil phase (solid triglyceride) & 1 \\ Lecithin & 8002-43-5/8030-76-0 & Emulsifier (HLB-7) & 1.5 \\ PEG-15 hydroxyapatite & 70142-34-6 & Emulsifier (HLB 15) & 0.05 \\ Phenoxyethanol & 122-99-6 & Preservative & 1 \\ Aqua * & 7732-18-5 & Continuous phase & q.s. ad 100 ** \\ \hline \end{tabular}  
* Aqua: water for injection (WFI) was used. ** q.s. ad 100: _quantum sufficient ad_ 100, as much as is sufficient to make 100.  
\end{table}
Table 1: Formulation of emulsions.  
was transferred to light-protected, sealed containers and stored at 4 \({}^{\circ}\)C to maintain stability until further use [18].  
The CE EO and NLC EO were prepared by loading the CE and NLC with a mixture of selected essential oils (EOs) with insect-repellent action, which was previously prepared. The EO consisted of (INCI) _Lavandula Angustifolia_ oil, _Pelargonium Gravelens_ oil, _Eucalyptus Globulus_ leaf oil, _Eugenia Caryophyllus_ bud oil, and _Ocimum Basilicum_ oil in a 2:1:1:1:0.5 (\(v/v\)) ratio.  
The loading of the CE and NLC with EO was performed by adding the EO dropwise to each sample under vigorous shaking (Vortex-Genie 2, Scientific Industries, Bohemia, NY, USA) up to the point where it was homogenously incorporated without the appearance of oil on the sample's surface (about 10 \(\mu\)L/ mL), and then each sample was stored at 4 \({}^{\circ}\)C in sealed amber containers.
# Multi-Directional Shape Change Analysis of Biotensegrity Model Mimicking Human Spine Curvature
###### Abstract  
This paper presents a numerical strategy for the shape change analysis of spine biotensegrity models in multi-directional modes. The formulation of incremental equilibrium equations and optimization problem for shape change analysis via the forced elongation of cables to achieve the target coordinates of the monitored nodes of spine biotensegrity models are presented. The distance between the monitored nodes and the target coordinates is chosen as the objective function which is minimized subject to inequality constraints on member axial forces and cable forced elongation. Three spine biotensegrity models were analyzed to validate the effectiveness of the proposed method. The deformation characteristics of the Class-1 four-stage biotensegrity models mimicking the natural curvature of the human spine were investigated. A highly successful rate in achieving the target coordinates was observed in a total of 258 analysis cases, with percentages of 99.9%, 99.9% and 98.9% for shape change analysis involving uni-, bi- and tri-directional modes, respectively. The results show that the spine biotensegrity models have more flexibility in undergoing bending in comparison with axial deformation. With the established shape change strategy, the flexibility and versatility of the movement of spine biotensegrity models can be further studied for potential application in the shape change control of deployable structures together with the use of IoT.  
biotensegrity; shape change; tensegrity; spine; optimization; sequential quadratic programming; IoT +
Footnote †: journal: Applied
## 3 Results and Discussion
### Robustness and Method Control
#### 3.4.3 Method Control  
The method control was performed by generating a large amount of data (100 cases) through Monte Carlo simulation at the CMP range with the highest desirability index. Then, the capability analysis process was applied to the estimated residual errors from all CMA responses. The established limits and results of the capability analysis are listed in Table 2 and presented in Figure 4 for HS-SPME and Figure S5 (Supplementary Material) for GC-MS.  
The process capability index (Cpk) values varied between 1.36 to 1.49. From the results, it can be concluded with a high level of confidence that the proposed analytical method is robust, since all CpK values were above the minimal reference value (1.33), for a method to be considered robust [16].
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 7 Validation and Discussion
### Evaluation Criterion  
In this study, we select two widely recognized metrics, namely image error and correlation coefficient, as standards for evaluating reconstruction quality [16].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithms** & **Abbreviation** \\ \hline Polak–Ribière–Polyak conjugate gradient method [71; 72] & PRPCG \\ Combined \(L_{1}\) with second-order total variation method [16] & L1 SOTV \\ Joint \(L_{1}\) and total variation regularization [9] & L1 TV \\ Elastic net regularization method [13] & ELNR \\ Transformed \(L_{1}\) regularization [73; 74] & TL1R \\ \(L_{1/2}\) regularization method [75] & L\({}_{1/2}\)R \\ Joint \(L_{1}\) and low-rank regularization [8] & L1 LR \\ \(L_{1}\) norm regularization method [76] & L1R \\ Low-rank regularization [77] & LRR \\ \(L_{1-2}\) regularization [5] & L\({}_{1-2}\)R \\ Fractional \(L_{1}\) regularization [19] & FL1R \\ Fractional-order elastic net regularization [18] & FOENR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tested algorithms.  
Assessing the consistency of reconstruction results is equally important in determining the reliability of an algorithm. Therefore, we pay special attention to the performance stability of the MOROR algorithm under different operating conditions. To this end, we calculate the variance in the image error and correlation coefficient. A smaller variance indicates greater stability and consistency of the algorithm during the reconstruction process, thus enhancing its reliability in practical applications.  
To comprehensively test the adaptability of the MOROR algorithm under different environmental conditions, we simulate various noise conditions and present visualized images under these conditions. At the same time, we provide the corresponding image error and correlation coefficient values to further analyze the algorithm's performance in the presence of noise interference. This approach allows us to identify the algorithm's potential and shortcomings in terms of noise resistance.  
In designing the experiments, we refer to the definitions of noise levels in [16] to ensure the comparability and scientific validity of the results. Through such meticulous and comprehensive evaluation, we gain a more thorough understanding of the MOROR algorithm's performance across diverse application scenarios while clarifying its advantages and areas for improvement. This provides valuable guidance for the further optimization of the algorithm and lays the foundation for its practical application.
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 3 Virtual Design and Construction (VDC)
### Integrated Concurrent Engineering (ICE)  
The organization model in the VDC framework is defined by the practice of ICE, a concept based on the powerful collaboration method promoted by NASA's Jet Propulsion Laboratory [12].  
The application of ICE constitutes the simultaneous (i.e., concurrent) development of interdependent tasks by the actors under highly concentrated coordination efforts [51]. This concept can be applied in two ways: by constant co-location for a defined period by the actors with interdependent tasks, or by conducting ICE sessions to resolve specific project issues efficiently.  
Regardless of the type of application, ICE is usually carried out in a workspace equipped with modern collaboration tools [45]. The idea of this place is that those involved can share information and solve problems effectively and directly by working face-to-face with the necessary resources [47; 52; 53]. Moreover, there is the fundamental use of BIM because the ICE session is one of the main spaces where all participants can interact and learn directly with the models [35].  
Working face-to-face does not necessarily imply or mean working in person. The impact of the COVID-19 pandemic on the construction industry, mainly during the first half of 2020, has forced many face-to-face ICE sessions to be carried out virtually, opening the space for the use of big virtual rooms employing the latest tools/technologies available in digital collaborative boards/workspaces.  
ICE sessions usually represent the most common application among those who use VDC, as seen in the study by [PERSON] et al. [54]. Furthermore, unlike traditional meetings, which often lack proper planning with pre-established time frames and preparation by the members, ICE sessions follow a detailed agenda previously sent to the participants, with clear objectives and estimated times for each topic to be discussed [45; 51].  
ICE's success lies in the dramatic reduction in coordination (or response) latency, which is the time that goes by from the moment a request for information or action is made to the completion of that request [51; 55]. Similarly, co-location in a small or big room considerably reduces decision latency [47] by having the leading managers, stakeholders and decision-makers present.  
ICE provides an environment where all VDC elements (Figure 1) are integrated with measured metrics for the product, organization and process [18].  
Finally, ICE is centered on the people who drive the project. The ICE sessions are where the contributions of the project disciplines are integrated by maximizing the concurrency to find the best solution for the client.
## 3 Analysis of Torque Load Transfer Mechanism
### Influence of Foundation Ring Depth  
Using the same analysis method, the torque load transfer process of specimen ZT-2, with a reduced foundation ring depth, was analyzed. The changes in torsional shear stress at the measurement points on the planes above or below the opening of ZT-2, under various loads, are shown in Figure 10.  
Using the simplified analysis method, the forces in the top radial reinforcement and the foundation ring of ZT-2, during the whole loading process, were analyzed, as shown in Figure 11.  
Figure 11: Torque load sharing ratios of ZT-2.  
Figure 10: Torsional shear stress on the foundation ring of ZT-2.  
The variation trend of ZT-2 is consistent with that of ZT-1. Under the working conditions of the 1 st-level load, the torque load sharing ratio of the foundation ring is smaller; afterward, the overall trend increases and tends to be stable. In addition to the conditions of the 1 st-level load, the foundation ring still shares the main torque load. Within 0.4-0.8 \(T\), the torque load sharing ratio increases rapidly. After 1.0 \(T\), the foundation ring shares about 86%, which is basically the same as that of ZT-1. The torque load is mainly transferred downwards and around the foundation ring, while the top radial reinforcement in the top layer only shares a smaller part.  
Combined with the results of the previous analysis, the stress change in the rest reinforcement around the foundation ring is limited, so it can be inferred that the torque load is mainly sustained by the sidewall of the foundation ring, as well as the bonding force between the bottom flange and the surrounding concrete. Under 1.5 times the limit load, there is no obvious stress damage to the foundation ring or to the surrounding concrete. When the foundation ring depth was reduced by 13%, the load transfer process and the mechanism of the torsional load on the foundation ring were not obviously affected. The torque load was still mainly sustained and transferred by the foundation ring. After 1.0 \(T\), the torque load sharing ratio of the top radial reinforcement is about 14%.
## 3 WBAN Requirements
### Energy Efficiency  
Energy efficiency within a WBAN environment is a key requirement where batteries are likely to be operational for months or years rather than days. This is especially important in the case of embedded sensors where patients may have to undergo a minor operation to change the battery. A pacemaker or glucose monitor could require a battery life of five years or more [11]. Characteristics such as low duty cycle, high levels of node sleep duration, use of efficient protocols and link adaptation/power management techniques can all contribute to extended battery life.  
A node may spend large amounts of time in sleep mode but can wake up frequently to communicate key physiological data. The energy consumed during the node wakeup process can be significant and degrade battery life. Propagation and signal absorption issues can be a significant problem for WBANs requiring specific energy efficient solutions. The optimal transmission power for on-body transmission for a wireless link depends on the physical distance of the link and its instantaneous channel condition [13]. Patient mobility and posture can have a significant effect on efficient packet delivery. The inherent characteristics of RF attenuation in and around the human body can have a direct impact on channel conditions [14].  
There is a potential scope for some forms of energy harvesting especially with a mobile patient, but is only likely to deliver small amounts of energy [15]. However, mechanisms that use body heat or vibration seem well suited to energy harvesting and could form part of an overall energy efficient solution [11].
## 5 Discussion  
This section focuses on the analysis of the proposed methodology and its effectiveness, along with its limitations. Better segmentation and heat maps contribute towards improved classification accuracy, precision, and recall. The dataset is split into various train-to-test ratios in order to ensure that no bias exists, and that samples are actual representatives of the dataset. If we analyze the results in Tables 3-6, it is clear that when the training data are reduced to 60%, the accuracy is reduced drastically, and the model treated 25 disease cases as normal, which means that the model is not generalized well when the training data are reduced. Moreover, better results on 10-fold cross-validation indicate balance between bias and variance of model. It is also evident from the results that there is a significant improvement in precision and recall rate, along with accuracy, which is also an indication of robustness. Upon analysis of the confusion matrix presented in Figure 15, a clear indication of better performance in the case of diseased data is observed, as no disease case is treated as normal. The proposed framework includes numerous significant steps, and major classification results were improved by using the images with contours, which indicates the significance of this step. The performance improvement between original and contour images can be observed by looking at the results presented in Tables 2 and 6. There is drastic improvement in accuracy of up to 17.26% for images with contours. This step highlights the boundary region of disease in an image, which in turn improves the classification outcomes. Moreover, heat maps also reveal that when the segmentation is performed, the model is more focused on a diseased area. Overall, the performance of the model in terms of false-positive rate (no diseased instance is classified as normal) with the 10-fold cross-validation technique demonstrates the robustness of our proposed methodology.  
The proposed methodology outperformed the cutting-edge methods, thus having major contributions; however, there are certain limitations that need to be addressed for future study. For instance, this study does not take into account the contrast and brightness issues of the endoscopic images. Moreover, neither the influence of training several models nor the optimization of features was taken into account in this study, which might lead to better results.  
Finally, we also present a comparison with cutting-edge methods. Table 7 shows that the suggested model outperforms the state-of-the-art methods in terms of accuracy.
## 4 Experiments
### Evaluation on Balanced Dataset  
In this experiment, a balanced dataset was created by merging 3637 online phishing webpages with an equal number of randomly sampled webpages from a pool of 40,066 benign webpages. The average results of several methods, obtained through five-fold cross-validation, are presented in Table 1.  
Compared to other methods, our approach demonstrates significant advantages across multiple evaluation metrics, achieving an accuracy of 0.9861 and a high TPR with a low FPR.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **URLNet** & **DURLD** & **DTD** & **FS-NB** & **TCURL** & **PhishBERT** & **Our Method** \\ \hline Accuracy & 0.9793 & 0.9704 & 0.9767 & 0.9718 & 0.9748 & 0.9856 & 0.9861 \\ \hline TPR & 0.9839 & 0.9795 & 0.9698 & 0.9681 & 0.9714 & 0.9852 & 0.9896 \\ \hline FPR & 0.0253 & 0.0387 & 0.0164 & 0.0245 & 0.0218 & 0.0140 & 0.0174 \\ \hline AUC & 0.9843 & 0.9813 & 0.9751 & 0.9736 & 0.9787 & 0.9905 & 0.9932 \\ \hline F1-Score & 0.9794 & 0.9707 & 0.9765 & 0.9717 & 0.9747 & 0.9856 & 0.9861 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results: our method vs. baseline method on balanced dataset using five-fold cross-validation.  
Additionally, our method demonstrates remarkable performance in two comprehensive metrics, F1-score and AUC, with values of 0.9222 and 0.9932, respectively. These results indicate the advantages of our approach in simultaneously maintaining high precision and recall rates, as well as its strong discriminative power and robustness in phishing/benign sample differentiation.
## 5 Tuning of Parameters and Comparison between Classifiers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Classifers**} & \multirow{2}{*}{**Recall (\%)**} & \multicolumn{2}{c}{**Rate of False**} & \multirow{2}{*}{**Accuracy (\%)**} \\  & & & & **Detection (\%)** \\ \hline SIMTU & Softmax & 70.1 & 13.5 & 79.6 \\ SIMTU & Random forest & 81.2 & 10.1 & 86.0 \\ STU & Softmax & 92.3 & 28.3 & 77.9 \\ STU & Random forest & 87.4 & 28.5 & 76.2 \\ KTU & Softmax & 59.8 & 34.9 & 63.9 \\ KTU & Random forest & 74.4 & 37.1 & 65.2 \\ SIMTE & Softmax & 78.5 & 30.1 & 72.4 \\ SIMTE & Random forest & 80.4 & 16.4 & 82.3 \\ STE & Softmax & 81.5 & 28.4 & 74.6 \\ STE & Random forest & 78.1 & 29.0 & 73.1 \\ KTE & Softmax & 69.7 & 37.1 & 64.3 \\ KTE & Random forest & 75.5 & 36.8 & 65.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of softmax and random forest classifiers on simulated and real data. Figure 9: Detection results when changing the subsampling factor of the pooling layer with Dataset STU (blue squares) and KTU (red triangles).