esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Dielectric Resonator Antenna for Wideband Sub-6 GHz Range
## 4 Discussion  
The following Table 2 reports a comparison summarizing the performances obtained by numerical results of different DRAs, such as cylindric (C-DRA), rectangular (R-DRA), star-shaped (S-DRA) and Spiral shell (SsDRA). All antennas were modelled by considering the same low-permittivity material, Resin V04, suitable for stereolithography [36; 37]. As noticeable, the optimized SsDRA displays relevant wider bandwidth behavior in the sub-6 GHz range compared to other geometries, and it also covers two frequency bands, 3.5 GHz and 5.5 GHz. Additionally, good gain values are calculated at both frequencies, despite the significant reduction of dielectric material volume used for the antenna realization. In this viewpoint, the choice of bio-inspired geometry allows us to have bandwidth improvement, without affecting gain, yet reducing the required material quantity.  
Concerning the realization of a prototype, if the antenna was thinner than 2 mm and taller than 29 mm, the antenna would be structurally weak. Therefore, the set t and h\({}_{\text{DRA}}\) values set the threshold corresponding to an aspect ratio AR = 14.5, enabling the actual realization of the antenna and ensuring its structural robustness. Additionally, referring to Figure 2, it can be noticed that, at the end of the spiral, the bigger circle, which approximates the curve, has a radius of about 9 mm, while the smaller circle in the proximity of the origin has a radius equal to 77 \(\upmu\)m. This minimum round radius represents the actual fabrication challenges also for 3 Dprinting technology. As the technology feature resolution is defined by the diameter of the circular end-effector and being the minimum required radius equal to 77 \(\upmu\)m, this turns into a minimum end-effector diameter of D\({}_{\text{e-e,min}}\)=2 * R = 154 \(\upmu\)m. This consideration allows us to conclude that the SsDRA geometry cannot be feasible for the FDM process, as the majority of FDM machines are typically equipped with nozzle diameters of 200 \(\upmu\)m or 400 \(\upmu\)m. However, the SLA technology can be successfully exploited for the fabrication of the optimized SsDRA, as the laser spot diameter of the machine allows us to accomplish a micro-feature resolution of 85 \(\upmu\)m [37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna** & **Frequency** & **Fractional BW** & **Gain** & **Dielectric Material** \\  & **(GHz)** & **(\%)** & **(dBi)** & **Volume (cm\({}^{3}\))** \\ \hline C-DRA [37] & 3.50 & 31.42 & 4.45 & 20.54 \\ \hline R-DRA [37] & 3.50 & 28.57 & 4.40 & 24.12 \\ \hline S-DRA [37] & 3.50 & 32.86 & 4.30 & 3.10 \\ \hline SsDRA & 3.50 & 44.4 & 3.7 & 14.55 \\  & 5.50 & 47.4 & 5 & 14.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of antenna performance and volume of material among the different DRAs, conceived by using same low-permittivity material.  
Figure 9: Radiation Efficiency (%) of the optimized SsDRA.
## 3 Results  
The results of the thermodynamic analysis showed that the net production of the cycle with working fluids of R114 and R123 was 4172 and 4129 kW, respectively. The thermal efficiency of the hybrid cycle with R114 and R123 was 14.40% and 14.25%, respectively, while the use of R134a generated power of 5923 kW and efficiency of 14.55%. Another important point is that the amount of energy received from the geothermal source for the R114, R123 and R134a were 21,266, 21,266 and 32,734 kW, respectively. This indicated that although R134a had a higher production capacity, its efficiency was equal to the other two working fluids. Moreover, it received more energy from the geothermal source. Exergetic evaluation of system showed that the exergetic efficiency of the drive cycle with fluids of R114 and R123 was 24.15% and 23.90%, respectively, which decreased by 6.08% and 6.33% compared to R134a. As a result, it was observed that the use of R134a performed better from a thermodynamic point of view. Exergo-conomic evaluation of the system showed that the total cost of the hybrid power plant for R134a, R114 and R123 (including the cost of equipment and currents flowing into the power plant) was USD.s-1 0.0508, 0.0444 and 0.0426, respectively. Although R134a was more productive, it imposed more costs. An assessment of the given cycle from an exergo-environmental perspective based on life cycle assessment (LCA) showed that R134a had more adverse effects on the environment than the other two fluids. Thus, the environmental impact with this fluid was 6.22e-5 (Pts.s-1), which was 9.51% higher than the environmental impact of R114 and 10.28% higher than the adverse environmental impact of R123. Therefore, the use of R134a, in addition to costing more, will also have more environmental degradation. Thus, R114 was selected as the working fluid for the present paper. Table 8 gives information of temperature, mass flow, enthalpy, pressure and exergy rate in GPP and SGPP cycles. Considering a conventional exergy analysis using the equations for each component of the cycles presented in Tables 9 and 10, the exergy destruction rates were obtained. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**State**} & \multicolumn{4}{c}{**Standalone**} & \multicolumn{4}{c}{**Geothermal Cycle (GPP)**} & \multicolumn{4}{c}{**Second Mode (Hybrid**} \\  & \(\hat{m}\) & **T** & **P** & **h** & \(E_{x}\) & \(\hat{m}\) & **T** & **P** & **h** & \(E_{x}\) \\  & **kg\(\cdot\)s\({}^{-1}\)** & (°C) & **(bar)** & **(k)\(\cdot\)kg\({}^{-1}\)** & **(kww)** & **kg\(\cdot\)s\({}^{-1}\)** & (°C) & **(bar)** & **(0.1)\(\cdot\)kg\({}^{-1}\)** & **(kww)** \\ \hline  
3 & 434.4 & 25 & 0.993 & 104.9 & 307.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Flow information of first and second modes of simulation.
## 2 Data and Overall System  
### Dataset  
The dataset was provided by National Cheng Kung University using a pulse diagnosis instrument with 24-channel (\(5\times 5-1\)) tactile sensors and a data acquisition system [6]. There were 37 subjects (including four females and 33 males) aged 62.52 \(\pm\) 11.85 years (mean \(\pm\) SD). All subjects were hypertensive patients and their family members in National Cheng Kung University Hospital, who were selected randomly. Specifically, 24 subjects of the dataset were hypertensive patients (systolic/diastolic blood pressure: 148.08/85.08 \(\pm\) 17.51/11.69), and the other 13 subjects were healthy persons (sp/dp: 118.68/73.62 \(\pm\) 8.69/6.90). The multi-channel pulse wave signals were recorded under IRB approval (IRB#: B-ER-103-263). The recording length was about 8-9 s per subject with a sampling rate of 50 Hz. The information details of the dataset can be found in a previous study [6].  
An example of a 3 DPI with stereoscopic (upper left) and planar (upper right) views is shown in Figure 1. The PPS \(5\times 5\) tactile sensor records spatiotemporal information of the radial artery. The \(x\)-axis of the 3 DPI represents the pulse length along the artery blood flow direction, while the \(y\)-axis is perpendicular to the artery blood flow direction. Pulse amplitude on the \(z\)-axis is normalized to 0-1 with color settings (red = 1 and violet = 0). The amplitude changes periodically to form a dynamic 3 DPI.
# Bluetooth Low Energy Mesh Networks: A Survey
## 6 Discussion and Open Issues
### Discussion
#### 6.1.3 Reliability: Advertising Channels vs. Data Channels  
BLE mesh network solutions make use of advertising channels and/or data channels in different ways. The choice of each channel type has an important impact on performance in terms of data transmission reliability.  
Flooding-based solutions (which use advertising channels) [9; 10; 19; 20; 23; 26; 28], being based on Bluetooth 4.x, will not be able to exploit frequency hopping over the 37 BLE data channels. Therefore, such solutions will not benefit from a good mechanism for robustness. This is particularly critical in smart home environments, where interference from devices using other technologies and multipath propagation is a common issues [4]. However, the use of data channels (and their frequency hopping mechanism) requires the establishment of Link Layer connections between neighboring devices. Another approach, only available in Bluetooth 5.0, is using secondary advertising channels, which allows frequency hopping to be exploited for such channels.  
On the other hand, routing-based solutions that use advertising channels to send routing protocol messages but use data channels for data transmission [13; 14; 15; 18], may suffer the issue of finding routes based on channels for which conditions (in terms of link qualities) may be different from the ones that will be encountered by data transmission.  
Finally, the use of data channels allows Link Layer reliability to be exploited, which provides Link Layer acknowledgments and retries. In contrast, advertising channels are limited to broadcasting, which does not use any mechanism for enhanced reliability.
# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 3 Context-Aware Packet Forwarding
### Distance-Based Forwarding Control
We add the position of the previous node to record the node from which the Interest packet was received. Thus, the origin of the Interest packets can be traced. Note that, first, the same Interest (i.e., both the Nonce and the Name are the same) may be received several times by the same node due to the wireless broadcasting of data. In this case, only the position of the first node is recorded in the PIT table, since the possibility that the Data packet can reach the requestor through this node is at its highest. Second, Previous_position is set to null at the original node where the Interest is sent. Third, due to the movement of the vehicles in VANET, the Previous_position field can only denote the rough position where the Interest comes from. Nevertheless, the PIT can be used to limit the rough direction of Data packets, which can greatly reduce the number of Data packets broadcasting in the networks. Through the deferral functions, a greedy geo-position-based forwarding algorithm can be realized. In other words, the vehicular nodes furthest away from the current node, in the direction of the requesting node, forward the packet first. This shortens both the data request and response time. Together with the modified PIT, the total number of packets transmitted in the network is reduced. Figure 4: Control fields of Interest packets. Figure 5: Control fields of Data packets. Figure 1:0 Pending Interest Table (PIT) and an example entry.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 4 Experimental Result Analysis
### Experimental Results
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Aim** & **Methodology** & **Evaluation Metrics** & **Accuracy** \\ \hline \multirow{4}{*}{[32]} & Investigating how COVID-Net makes predictions using an explainability method to gain deeper insights into critical factors associated with COVID cases and validate COVID-Net & Tailored CNN & Confusion matrix and accuracy & 92.60\% \\ \cline{1-1} \cline{2-4}  & Proposal of using a deep feature plus support vector machine (SVM) methodology for detecting coronavirus-infected patients using X-ray images & ResNet50 + SVM & Confusion matrix and accuracy & 95.38\% \\ \cline{1-1} \cline{2-4}  & Designing an automated diagnosis system of the Coronavirus disease, an X-ray image collection from patients with common bacterial pneumonia, verified COVID-19 disease, and normal occurrences were used & CNN based on transfer learning & Accuracy, sensitivity, and specificity & Accuracy 96.78\%, sensitivity 98.66\%, and specificity 96.46\% \\ \cline{1-1} \cline{2-4}  & Using the publicly available COVID-19 chest X-ray dataset, quantify uncertainty using deep learning methods to improve the diagnostic performance of the human–machine combination & Drop-weights-based Bayesian convolutional network & Accuracy and uncertainty prediction & 95\% confidence interval \\ \hline \multirow{4}{*}{[39]} & Proposal of using 5 pre-trained DL models to identify coronavirus- and pneumonia-infected patients & DL Models - InceptionV3 and Inception-ResNetV2 along with ResNet50 & Confusion matrix and accuracy & ResNet50 with the highest accuracy value of 96.1\% \\ \cline{1-1} \cline{2-4}  & Presenting an alternative modeling framework based on Capsule Networks (COVID-CAPS) capable of handling small datasets due to the rapid emergence of COVID-19 & CNN-based Capsule Networks & Confusion matrix and accuracy & 95.70\% \\ \hline \multirow{4}{*}{[51]} & Using drop-weights-based Bayesian Convolutional Neural Networks (BCNN) to estimate uncertainty in deep learning solutions for improving diagnostic performance with COVID-19 chest X-ray dataset & Bayesian CNN (ResNet50) & Confusion matrix and accuracy & 88.39\% \\ \cline{1-1} \cline{2-4}  & Using the drop-weights-based Bayesian Convolutional Neural Networks (BCNN) to estimate uncertainty in deep learning solutions for improving diagnostic performance with COVID-19 chest X-ray dataset & Bayesian CNN (ResNet50) & Confusion matrix and accuracy & 88.39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between currently developed COVID-19 detection methods.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Discussion
#### 7.4.1 Research Impacts  
The research impacts of PPFS methods are important and deserve to be addressed:They contribute to the scientific society by providing a new PPFS model that overcomes the limitations of the existing FS methods.
* They provide four FS techniques that enhance the classification accuracy by an average of 6.64%.
* They contribute to technological development by providing EPP-Lab software handling FS.
* They contribute to decision making by providing four PPFS methods yielding different but equivalent results
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 1 Introduction
Furthermore, the PUA-LightGBM model is being proposed for the first time. The structure of the paper is as follows: The LightGBM framework and the PUA-LightGBM model are described in Section 2. Section 3 describes the methodology and process of the experiments and the workflow of the PUA-LightGBM model in detail. The results and discussions of the experiments are presented in Section 4. Section 5 provides a conclusion.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis
#### 4.6.5 Validation Using Publicly Available Datasets  
To further evaluate the reliability of the dataset and the robustness of the proposed model, we employed the CIC-Bell-DNS-EXF-2021 dataset [27, 28], published by the Canadian Cybersecurity Institute, to validate the trained model. This dataset encompasses a diverse range of DNS traffic records that reflect the complexity and variability of real-world network environments.  
To ensure compatibility with the model input, we developed a dedicated preprocessing pipeline that extracted the same 23-dimensional feature set used during training. All features were normalized using the same min-max scaling procedure adopted in the training phase.  
Through this consistent preprocessing, the external dataset was adapted to match the input format of the model. Validation experiments demonstrated that the DBM-ENSec model exhibited strong generalization and maintained high detection accuracy across varied scenarios. Notably, it achieved stable performance in identifying DCC attacks under real-world-like conditions. The results of this evaluation are summarized in Figure 19.
## 5 Results and Discussion
Taking into consideration our resolution capacity, we observe that the greater displacement in zone 4 affects only a corridor limited by the fault zone and the shale of the Volcano-sedimentary Complex and the Culm Group (Figure 13). No other relevant movements are observed in the pit apart from minor displacements in the south wall coinciding  
Figure 12: Zoom in Zone 4, convergent slope in the east wall of Atalaya pit. The block slide seems to be limited by the fracture zone (black lines) and the contact of the massive sulfides with the silicidastic sediments of the Culm Group (thick blue line). The GNSS measurements are represented by colored squares. GNSS displacement vectors are marked with dip symbols (azimuth direction), and numbers (tilt degree). The circles are the calculated velocities from Sentinel-1 (Figure 8). The color scale is valid for both squares and circles. The white arrows show the GNSS—Sentinel-1 correlation points. The graph compares cumulative displacement from May 2015 to November 2017 between the Sentinel-1 (black dots) and GNSS (colored lines) displacement. with terraces that collapsed before the surveying period, which we interpret as debris slides. It is remarkable how Sentinel-1 is capable of measuring such small displacements even though they occur towards the north, which is a direction of low sensitivity for the sensor. The anomaly in the base of the wall covers the whole debris zone (\(\sim\)120 m) and shows displacements up to 3 cm/y. The anomaly in the upper wall is smaller (\(\sim\)50 m) and with values less than 1 cm/y, in good agreement with the GNSS measurements in this zone.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Structure
#### 4.2.3 Antenna-Filter-Antenna (AFA) FSSs
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**FSS Ref.**} & **Conductive** & \multirow{2}{*}{\(\mathbf{f_{f}}\) (GHz)} & \multirow{2}{*}{
\begin{tabular}{c} **Array Element** \\ **Size** \\ \end{tabular} } & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**FBW \%**} & \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Patch and Aperture Elements**} \\  & & & & & & & & \\ \hline
[17] & 3 & 10.8 & 0.144\(\times\) 0.125\(\lambda\) & 0.075\(\lambda\) & 75 & 2 &
\begin{tabular}{c} Connected Capacitive patches and \\ slots of modified triples \\ \end{tabular} \\ \hline
[100] & 2 & 1.42 & 0.0378\(\lambda\) & 0.0013\(\lambda\) & 37 & – &
\begin{tabular}{c} Four-fold symmetric meander \\ lines and convoluted dipole \\ \end{tabular} \\ \hline
[95] & 4 & 21 & 0.21\(\lambda\) & 0.273\(\lambda\) & 5 & 2 & All wire grid layers \\ \hline
[101] & 3 & 10 & 0.1\(\lambda\) & 0.067\(\lambda\) & 21 & 2 &
\begin{tabular}{c} Double concentric square loops \\ patch with wire grid \\ \end{tabular} \\ \hline
[102] & 3 & 8.5 & 0.2\(\lambda\) & 0.257\(\lambda\) & 15 & 3 &
\begin{tabular}{c} Wire grid and Hybrid \\ (I-shaped) resonator \\ \end{tabular} \\ \hline
[103] & 2 & 15 & 0.3\(\lambda\) & 0.545\(\lambda\) & 13 & 2 &
\begin{tabular}{c} Gridded square loop and \\ cross-loop aperture embedded in \\ five dielectric substrate layers \\ \end{tabular} \\ \hline
[104] & 2 & 7.95 & 0.12\(\lambda\) & 0.042\(\lambda\) & 23 & – &
\begin{tabular}{c} Crossed slot element convoluted \\ three circles with vertical vias \\ \end{tabular} \\ \hline
[105] & 2 & 12.24 & 0.245\(\lambda\) & 0.03\(\lambda\) & 96 & – &
\begin{tabular}{c} Patch of four square loops at four \\ corners and non-resonant \\ inductive layer with a square loop \\ \end{tabular} \\ \hline
[97] & 2 & 45.91 & 0.53\(\lambda\) & 0.055\(\lambda\) & 28.4 & – &
\begin{tabular}{c} Fractal Koch elements cascaded \\ with air gap in between \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._A comparison of recently demonstrated AFA based FSS structures is presented in Table 5, elaborating layers count, patch/apertures fractal shapes, and functionality. It can be inferred that AFA based FSSs are helpful in the enhancement of BW, especially when the fractal geometries are utilized in the array element. However, such structures appear to increase the thickness and/or periodicity of the element.
## 3 Results and Discussion
### HPLC-PDA Method Validation
#### 3.2.5. Stabilities of Analyte Solutions
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Compound a**} & \multirow{2}{*}{**Original Conc.**} & \multirow{2}{*}{**Spike Conc.**} & \multirow{2}{*}{**Found Conc.**} & \multirow{2}{*}{**Recovery b**} \(\pm\) SD** & **RSD** \\  & & **(\(\mu\)g/mL)** & **(\(\mu\)g/mL)** & **(\(\mu\)g/mL)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{L7 GC} & 0.81 & 5.00 & 5.70 & 106.04 \(\pm\) 1.29 & 1.22 \\  & & 50.00 & 49.89 & 99.24 \(\pm\) 0.35 & 0.35 \\  & & 125.00 & 130.02 & 105.62 \(\pm\) 0.18 & 0.17 \\  & 4.16 & 6.25 & 10.42 & 113.18 \(\pm\) 0.87 & 0.77 \\ CDR & & 62.5 & 64.63 & 99.55 \(\pm\) 0.54 & 0.54 \\  & & 156.25 & 161.07 & 104.80 \(\pm\) 0.23 & 0.22 \\  & 18.81 & 5.00 & 23.75 & 119.35 \(\pm\) 1.84 & 1.54 \\ C7 dGlu & & 50.00 & 67.44 & 96.83 \(\pm\) 0.84 & 0.86 \\  & & 125.00 & 145.75 & 104.01 \(\pm\) 0.21 & 0.20 \\  & 22.78 & 6.25 & 29.06 & 127.07 \(\pm\) 2.20 & 1.73 \\ DSM & & 62.50 & 87.85 & 103.07 \(\pm\) 0.86 & 0.83 \\  & 156.25 & 189.95 & 109.68 \(\pm\) 0.15 & 0.14 \\ \hline \end{tabular}  
* Luteolin-7-O-\(\beta\)-D-glucurononopyranosyl (1 \(\rightarrow\)2)-O-\(\beta\)-D-glucurononopyranosyl, L7 GC; clerodendrin, CDR; chrysoreiol-7-O-diglucuronide, C7 dGlu; diosmin, DSM.  
\end{table} 
Table 3: Recoveries of L7 GC, CDR, C7 dGlu, and DSM for the developed HPLC-PDA method (\(n\) = 3).
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review
### Sliding Window-Based Chunking Techniques  
Content-defined chunking (CDC) schemes are considered highly flexible in terms of redefining boundary chunks as they rely on the content instead of the size, providing a highly dynamic method for data organization. Ensuring the fulfillment of breakpoints forms the foundation of the CDC technique; this is considered a critical component in the procedure of deduplication. This vital element has a significant effect on the ratio of data deduplication and data repetition performance. Sliding window-based techniques have been employed for the past 15 years; however, their efficiency is compromised as they need byte-by-byte data deduplication. This work provides the basis for the construction of an innovative and effective solution. Leap CDC is presented, which is guaranteed to increase the data deduplication ratio. Data deduplication usually consists of four main stages, which include chunking, the fingerprinting of chunks or fingerprinting calculation, fingerprint indexing, and querying, along with the storage and handling of data [22]. This procedure involves instances where the chunks surpass the specified size, and subordinate solutions are utilized. If the maximum limit of the chunks is not met, an imposed breakpoint can still occur. The leap-based algorithm also makes use of the same idea in secondary situations, and this helps to overcome the imposed proportion of breakpoints for the data chunks. A rolling hash cannot be applied, so, as an alternative, the concept of pseudo-random transformation is used. This study contrasts SW and leap-dependent CDC in terms of CPU and resource utilization and the data deduplication ratio. The main problem in this approach is that it sets some predefined criteria, which ultimately lead to imposed breakpoints that are unsuitable for large data chunks. The Double Sliding Window (DSW) algorithm is an improvement over the CDC mechanism. It is considered to increase the performance of CDC in the context of deduplication. The DSW focuses on the main criteria in deduplication, comprising the RT, the number of chunks, and the re-deletion effectiveness. The DSW increases the threshold, permitting more flexible and improved data processing. This scheme makes use of double windows with variable sizes. These windows are placed at the initial part of the data stream and send the data further. Hash values identify breakpoints. The Markov model is used to find cut points in data and resources, being well utilized within the deduplication mechanism [23].