esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper provides power loss optimizations for PSiPs in relation to integrated Si LDMOSFETs and air-core inductors. For integrated Si LDMOSFETs, a size and parallel quantity optimization is provided based on power loss analyzation. For air-core inductors, quality factors are improved by winding structure optimization to reduce coil loss. The power losses of the integrated Si LDMOSFET and air-core inductor are both reduced to less than 10% of the output power at 1-100 MHz switching frequency and 0.1-10 W power level. Based on the analysis, this paper predicts the efficiency boundary of PSiPs. The efficiency prediction error is 1-15% at 1-100 MHz switching frequency and 0.1-10 W power level. The predicted results are consistent with the findings of state-of-the-art research. To improve the efficiency toward high-frequency PSiP, two technologies are proposed from the perspective of switches and inductors.
* For power switches, a parallel quantity of integrated Si LDMOSFET is designed based on power level. The size of each power switch is optimized based on switching frequency and power level.
* For power inductors, the planar spiral inductor provides a low profile for monolithic integration. An optimal winding structure with narrow inner and wider outer windings dramatically reduces power losses.  
Conceptualization, Y.W. and R.M.; methodology, Y.W. and D.Z.; software, Y.W. and B.H.; validation, L.L. and H.X.; formal analysis, Y.W., H.X. and R.M.; investigation, Y.W. and D.Z.; resources, L.L., B.H and X.Z.; data curation, Y.W. and X.Z.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., D.Z., H.X. and R.M.; visualization, X.Z.; supervision, X.Z.; project administration, Y.W., R.M. and X.Z.; funding acquisition, R.M. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: Prediction and verification of the efficiency boundary [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61].
## 4 Discussion
While telerehabilitation offers many advantages for LVAD patients, it is essential to consider potential challenges. The first one is the technology barriers. Not all patients may have access to the necessary technology or the internet. Providers must ensure that telehealth solutions are accessible and user-friendly. The second is the sufficient training. Healthcare professionals must be trained in telehealth practices to effectively manage telerehabilitation services, ensuring that they can provide quality care remotely. The third is the integration with traditional care. Telerehabilitation should complement, not replace, in-person visits, especially for patients needing close monitoring or experiencing complications.
## 4 Results and Discussion
### Relationship Analysis
#### 4.6.1 Model Parameters  
The developed model was used for the estimation of the output, which was nearest to the desired variables. Altering the weights for each node provided a specific output, which was closest to the actual value. Estimated weights for the model are given in Table 9.  
Usually, the performance of a model is compared using the coefficient of determination (\(R^{2}\)). A classic fit would bring about an \(R^{2}\) of 1, and a poor fit would be almost 0 [45]. The model parameter estimates of the ANN model showed that for the prediction of compressive strength, the ANN model was successful up to 75.5% in the training data and 75.5% in the validation data, as shown in Table 10. For flexural strength prediction, these values were even higher, at 99% for the training data and 97% for the validation data. The accuracy of prediction could be visualized in plots drawn for compressive and flexural strength (between actual and predicted data for both the training and validation data), as shown in Figure 11.  
Figure 10: Artificial neural network structure for brick bond strength estimation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Description** & **Mean** & **SD** & **Min** & **Max** \\ \hline _CS_ & Compressive strength (MPa) & 0.5373 & 0.0932 & 0.415 & 0.7969 \\ _FS_ & Flexural strength (MPa) & 0.0764 & 0.0797 & 0 & 0.3595 \\ _RO_ & Mortar mix ratio (1-13, 2-14, 3-16, 1-1:8) & - & - & 1 & 4 \\ _BT_ & Bond type (1: Englishly; 2: Flemish; 3: header; 4: Dutch; 5: silver) & - & - & 1 & 5 \\ WT & Water (L) & 2.1184 & 0.5487 & 1.152 & 3.231 \\ CT & Cement (Kg) & 4.168 & 1.449 & 1.92 & 7.18 \\ _SD_ & Sand (Kg) & 19.519 & 3.372 & 12.93 & 25.52 \\ W/B & Water/binder ratio & 0.525 & 0.06537 & 0.45 & 0.6 \\ C/S & Cement/sand ratio & 0.2188 & 0.0807 & 0.125 & 0.3333 \\ W/S & Water/sand ratio & 0.11042 & 0.02934 & 0.075 & 0.15 \\ \hline \hline \end{tabular} Note: SD: standard deviation; Min: minimum; Max: maximum.  
\end{table} 
Table 8: Description of variables for brick bond strength estimation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measures** & **Training Value** & **Validation Value** \\ \hline \hline \multicolumn{3}{c}{Compressive Strength (MPa)} \\ \(R^{2}\) & 0.7557954 & 0.755093 \\ RMSE & 0.0459783 & 0.0445664 \\ Mean abs dev & 0.0370928 & 0.0393228 \\ Sum frequency & 48 & 12 \\ \multicolumn{3}{c}{Flexural Strength (MPa)} \\ \(R^{2}\) & 0.99054 & 0.9661535 \\ RMSE & 0.0083136 & 0.008139 \\ Mean abs dev & 0.0057855 & 0.0055983 \\ Sum frequency & 48 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model parameter estimates for training and validation. Figure 11: Training and validation plots for (**a**) compressive strength and (**b**) flexural strength.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Data Analysis with Different Pesticides in Fruits
#### 3.1.2 E-Tongue  
In the PCA analysis, the data obtained by E-tongue were used to achieve high discrimination and separation in the samples. The results from the E-tongue show less scatter in the data than those from the E-nose. This indicates that the compounds detected by the E-tongue are generally more stable, as the interactions between soluble chemicals and sensors are more direct and susceptible to environmental or experimental variability than VOCs. This means that once a sample is prepared and measured, the detected compounds will likely remain more constant, resulting in more reproducible data. Nevertheless, in the case of the apple, an error was generated due to an \"outlier\" for one of the samples, between the organic fruit category and that contaminated with Funlate (see Figure 5), causing an alteration in the separation between these groups in the PCA space, making it appear as an overlap between the categories. Potential causes of the outlier could include experimental error (such as measurement errors, data recording issues for that specific sample,  
Figure 4: LDA plots of organic and contaminated fruit using E-nose: (**A**) plum, (**B**) strawberry, (**C**) apple, and (**D**) gooseberry.  
Figure 3: PCA plots of organic and contaminated fruit using E-nose: (**A**) plum, (**B**) strawberry, (**C**) apple, and (**D**) gooseberry.  
equipment problems with the E-tongue, cross-contamination, or human errors in sample preparation), biological variability (significant natural variations in chemical profiles due to factors such as ripening, growth conditions, or storage), and chemical interference (presence of a previously undetected compound or an undesired chemical reaction).  
It is important to highlight that in all the results obtained with the E-tongue, PC1 explains more than 97% of the variance, which means that a single feature or a set of features is dominant in differentiating the samples. Figure 6 shows the result in the discrimination of the fruits using the E-tongue, where it is possible to observe that the same behavior is maintained in the classification of the four fruits using LDA.  
Figure 5: PCA plots of organic fruit vs. contaminated fruit analyzed using E-tongue: (**A**) plum, (**B**) strawberry, (**C**) apple, and (**D**) gooseberry.  
Figure 6: LDA plots of organic and contaminated fruit were analyzed using E-tongue: (**A**) plum, (**B**) strawberry, (**C**) apple, and (**D**) gooseberry.  
The overlap between the samples of gooseberry treated with Bricol and Curzate in the LDA may be due to several factors related to the fruit's ripeness, the chemical composition of the pesticides, the fruit's physiological response to these treatments, the extraction temperature of the VOCs, or the detection capability of the E-nose, as the sensors may not be sufficiently specific to differentiate between these compounds. On the other hand, the LDA classifier obtained 92.65% accuracy.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 2 Related Works
While there is no normal information stored, some techniques need to be applied to compute normal vectors [6; 9; 27], such as local plane fitting, principal component analysis (referred to as PCA), and tensor voting. In PCA-based methods, the normal vector of point is computed using the local covariance matrix based on its neighborhood [27], and different dimensional geometric features can be interpreted from eigen decomposition of the matrix; besides, the optimal size of neighborhood is the critical point and is affected by the quality of point cloud dataset [10]. In tensor-voting-based methods, it can deal with the noisy dataset using the tensor encoding and revolving process and has the capability of N-dimensional geometric feature representation [6; 15; 16; 28; 29]. Using tensor voting, normal vector of the point is computed from its neighborhood and subsequently revoted to get the refined result. Then, geometric features of different dimensional are provided, and the subset of the point cloud dataset with the label of line feature is extracted [29]. However, the next step of extracting line structure from the selected line subset, still remains to be a challenge. Compared with geometric-model-based shape detection methods, the topological relation-based method can deal with the graph connection of line structures. One of the widely used topological methods is the persistent homology [30; 31; 32], where topological relations are computed and captured based on N-dimensional holes during growth of the distance to merge all points. Using the persistent homology theory, [PERSON] [19] computed topological patterns contained in a point cloud dataset and built pattern signatures by barcodes. Another form of topological features computed based on the persistent homology is studied by [PERSON] et al. [20] and [PERSON] et al. [33], and topological features are presented by the persistent image, which is further computed using the persistent diagram. Moreover, [PERSON] et al. [34] presented a 3D point descriptor according to birth and death of 0-cycles and 1-cycles and took it as topological signatures to classify categories for noisy point cloud datasets. These topological methods can deal with quality-unstable point cloud dataset using the topological feature and preserve graph connections with each line segment, while it is difficult to maintain geometric shapes in the dataset. To deal with the quality-unstable point cloud dataset and obtain connection relation preserved line structures, a line structure extraction method is proposed in this paper, and point cloud datasets are collected using the iPhone-based LiDAR sensor. In addition, structure refinement based on the assumed geometric rules is not going to be conducted in this paper, since line structures are extracted with no predefined geometric models and no manually selected training datasets.
## 4 Parametric Study by Computational Analysis
### Coupled Effect of Two or More Parameters in \(h\), \(t\), and \(a\)
Figure 21: The stress and displacement of concrete overlay vary with the variation of \(t\) and \(a\). Figure 20: The stress and displacement of concrete overlay vary with the variation of \(h\) and \(a\). Hence, it can be concluded that adjusting parameter \(a\) holds greater significance in managing the maximum tensile stress of the concrete overlay, while modifying parameter \(t\) plays a crucial role in controlling the maximum vertical displacement. These observations provide valuable insights for engineers and designers to optimize and fine-tune the values of parameters \(a\) and \(t\), ensuring desirable stress and displacement performance of the concrete overlay in various structural applications. Based on the analysis of Figures 10 and 11, it is evident that the independent size parameters for a single U-rib include \(a\), \(b\), \(h\), and \(t\). However, to ensure compatibility between the BUSP and crossbeams, this study considers \(b\) as a constant value. Consequently, the dimensional parameters under investigation are \(a\), \(h\), and \(t\). To explore the combined effects of these dimensional parameters on the stress of the concrete overlays, this study introduces the area parameter \(A\) for a single U-rib. This parameter, expressed by Equation (2) using the values of \(a\), \(b\), \(h\), and \(t\), plays a vital role in the analysis. \[A=\frac{(a+2y+b+2x)h}{2}-\frac{(a+b)(h-t)}{2} \tag{2}\]  
\[x=t/\tan\alpha \tag{3}\]  
\[y=t/\sin(\pi-\alpha) \tag{4}\]  
In Figure 22, the variation of the maximum tensile stress in the concrete overlays with respect to \(A\) is depicted. Notably, as the value of \(A\) increases, the maximum tensile stress in the concrete overlays initially decreases, reaching a minimum at approximately \(A=11,000\) mm\({}^{2}\), before subsequently increasing.
## 4 Results  
The full lens is simulated in the Time domain solver of CST. Waveguide ports are used to excite a quasi-TEM mode on the matching circuits which in turn are connected to the lens with vias. The imaging properties of the lens are illustrated in Figure 7a with the absolute value of the electric field sampled at 4.8 GHz. The lens is excited at probe 1, which is connected to the matching circuit through a hole in the bottom conductor of the PPW. An image is created at the antipodal point, where probe 2 is placed, similarly connected to a matching circuit.  
The simulated and measured scattering parameters are presented in Figure 7b. The simulation and measurement agrees well, apart from a slight frequency shift. However, since the distance between peaks in the \(|S_{11}|\) is similar in the simulations and measurements, the reflections occur at the same location in the structure. The frequency shift is accounted for by the error margin in the relative permittivity of the substrate. The measured \(|S_{21}|\) has a peak value of \(-0.5\) dB and it is above \(-3\) dB from 4.63 to 5.03 GHz. The bandwidth depends on the refractive index variation and the feed design. Two more probes are connected to the lens at the same radius as the feed probe, but displaced \(22.5^{\circ}\) and \(45^{\circ}\) from the image point. The measured transmission coefficients to these probes are also included in Figure 7b and are below \(-25\) dB.  
Figure 6: Simulation results of the matching circuit: (**a**) 3D model and (**b**) reflection coefficient when loaded with a dielectric PPW. The manufactured prototype is presented in the inset. The dimensions are \(w=2.27\) mm, \(D=1.0\) mm, \(\Delta r=0.7\) mm, \(d=6.5\) mm, \(l=19.8\) mm and \(s_{1}=32.9\) mm. The circuit is manufactured on a 0.8 mm thick Teflon substrate (\(\epsilon_{r}=2.53\) and \(\tan\delta=0.001\)).  
Figure 7: (**a**) Absolute value of the electric field distribution at 4.8 GHz obtained with CST simulation. (**b**) Simulated (solid lines) and measured (dashed lines) scattering parameters.
# Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Runtime [hh:mm]** & **NGI** & **SSIM** & **NRMSE** \\ \hline Coarse estimate & 01:26 & 0.123 * & 0.532 * & 0.7019 * \\ Refined estimate & 01:31 & 0.205 & 0.626 & 0.6359 \\ Est. + FORCASTER & 03:43 & 0.240 * & 0.641 * & 0.6283 * \\ Est. + CMA-ES \& NGI & 07:23 & 0.259 * & 0.643 * & 0.6284 * \\ \hline Correct cal. CBCT traj. & 00:35 & 0.294 & 0.377 & 0.7894 \\ Est. + FORCASTER CBCT traj. & 03:28 & 0.286 & 0.392 & 0.7817 \\ Correct cal. sinus traj. & 00:10 & 0.194 & 0.486 & 0.7291 \\ Est. + FORCASTER sinus traj. & 00:37 & 0.183 & 0.481 & 0.7311 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the evaluation on the continuous arc trajectory. Here, the forward projection is compared to the acquired data, and the evaluation results for the other two trajectories are also included if the forward projections from the correct calibration were compared to the acquired data. Values marked with an * had significant (\(p<0.05\)) changes compared to the refined estimate. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Runtime [hh:mm]** & **NGI** & **SSIM** & **NRMSE** \\ \hline Coarse estimate & 00:24 & 0.259 * & 0.885 * & 0.4513 * \\ Refined estimate & 00:27 & 0.662 & 0.992 & 0.0851 \\ Est. + FORCASTER & 00:54 & 0.836* & 0.997 & 0.0369 * \\ Est. + CMA-ES \& NGI & 01:50 & 0.955 * & 0.999 * & 0.0106 * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the evaluation on the sinusoidal trajectory. Values marked with an * had significant (\(p<0.05\)) changes compared to the refined estimate. NGI metrics were not as high as the results from the calibration of the CBCT trajectory. In addition, the misaligned projections generated a few artifacts.
## 4 Experimental Test Bed and Measuring Equipment  
In this section, we explain how the SD-PLC transceiver was paired with experimental measurement instruments to create a reliable test bed and the experimental method used to measure impulse noise errors in this study.  
Reliable data collection in an experimental study is achieved by using appropriate test equipment and meticulously experimenting. Consequently, after considering the best approach to measuring impulse noise error sequences, a test bed for experiments related to this study was established in the University of Johannesburg's Auckland Park Communications Laboratory.  
Figure 5 depicts the architectural layout of the experimental test bed, while Figure 6 depicts a snapshot of the experimental test bed. Collectively, these two figures show the  
components of the PLC measurement test bed. Components that make up these parts are the isolation transformer, the line impedance stabilizing network (LISN), the power line topology, the transmitting and receiving coupling circuits, the transmitting and receiving USRP, and the transmitting and receiving host PCs.  
In power line networks, the impedance fluctuates with the main voltage, making the impedance non-constant. As a direct result, there is an impedance mismatch between the measurement device and the line. To examine the effect of impulsive noise caused by electrically powered home appliances on data transmitted over a power line channel, we set up a test system that isolated the measurement equipment from the mains, removed ground loops, and filtered out electrical noise from the power line.  
Using a line impedance stabilization network (LISN) prevents impedance mismatches caused by a fluctuating network impedance, which can compromise the measurement reliability. The LISN is used in conjunction with an isolation transformer for this study. Because the LISN produces large earth leakage currents, it cannot be directly connected to the main circuit, which circuit breakers protect from earth leakage currents.  
Figure 5: The schematic of the experimental test bed.  
Figure 6: Picture of the experimental test bed.
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 3 Data Privacy in the Internet of Things
### IoT Data  
The IoT generates massive volumes of real-time, heterogeneous data from various connected devices and sensors. These data have high volume, velocity, and variety, including structured sensor readings, semi-structured logs, and unstructured multimedia content. Also, they are often time- and location-dependent, leading to additional temporal and spatial analysis complexity [21]. Furthermore, IoT data are frequently noisy, incomplete, or redundant due to sensor limitations and environmental variability, requiring preprocessing to ensure accuracy and reliability [22].  
From a privacy perspective, the pervasive and continuous IoT data collection, often occurring without user awareness, raises significant concerns. These include difficulties enforcing access control, susceptibility to inference attacks, and lack of transparency in data flows. The seamless integration of physical and digital environments further amplifies privacy risks. Additionally, the lack of universal data standards complicates interoperability, and issues surrounding data governance, ownership, and regulatory compliance (e.g., GDPR) must be addressed. Together, these factors highlight the urgent need for decentralized privacy-preserving mechanisms that provide individuals with control over their data.
###### Abstract  
This study investigated the impact of tobacco exposure, specifically through heating and burning, on periodontitis in the subgingival microbiome among clinically healthy individuals and those diagnosed with periodontitis. The sample comprised 66 subjects (26-56 years, median 38 yrs; 64% females) classified as non-smokers, classic cigarette smokers, and tobacco heating system (THS) smokers (each \(N=22\)). Full-mouth periodontal examination was performed, and 330 paperpoint samples from periodontal pockets were collected. Next-generation sequencing of 165 rRNA genes was conducted to identify the composition of subgingival microbiome. Periodontitis prevalence among the groups was ranked as THS (41%) \(<\) non-smokers (44%) \(<\) cigarette smokers (68%), without statistically significant differences between the groups. The number of perio-pathogenic species was higher in subjects with periodontitis compared to those without (median 7 vs. 6 species; \(p=0.005\)) but without significant differences between exposure groups: non-smokers (6) \(=\) smokers (6) \(<\) THS (6.5). When combining exposure and periodontal status, each smoker group had more perio-pathogenic species than non-smokers: non-smokers without periodontitis (5) \(<\) smokers without periodontitis (5.5) \(<\) THS without periodontitis (5.5) \(<\) THS without periodontitis (6); non-smokers with periodontitis (6.5) \(<\) THS with periodontitis (7) \(=\) smokers with periodontitis (7). Multiple linear regression indicated periodontitis as the sole predictor of perio-pathogenic species quantity, irrespective of the type of tobacco consumption, sex, age, or oral hygiene (R\({}^{2}=0.163\); \(p=0.005\)). In conclusion, the quantity of perio-pathogenic species in the subgingival microbiome was more influenced by periodontitis than by exposure to tobacco smoke, regardless of whether it was heated or burned.  
16S rRNA; next-generation sequencing; cigarette smoking; dysbiosis; electronic nicotine delivery system; periodontal pathobitions; periodontitis; subgingival microbiome +
Footnote †: journal: Applied Sciences