esa-sceva/satcom-chunk-collection huggingface dataset:
# Usability Measures in Mobile-Based Augmented Reality Learning Applications: A Systematic Review
## 4 Results and Discussion
### Usability Metrics (RQ2)
#### 4.3.2 Within-Subjects vs. Between-Subjects  
According to [PERSON] and [PERSON] in [2], within-subject evaluation refers to studies that performed repeated measures on experimental subjects. Commonly in usability studies, within-subject evaluation refers to having participants evaluating more than one of the tested items. The advantage of within-subject evaluation is there is no such need for a big pool of sample size--however, it risks the possibilities of the participant carryover effect and prior experience biases. Between-subjects evaluation, on the other hand, refers to comparing results for different participants, where every participant evaluates only once [2]. This evaluation type is capable of giving experimenters a clean data collection without the risks of the carryover effect and prior experience biases--however, it requires more effort and time to gather a larger sample pool. In answering another part of RQ2, Table 10 shows that a significant majority of the authors (48) performed between-subjects evaluation, 19 studies performed within-subject evaluation, and only 4 performed a combination of both. From the collected 72 research papers, 18 categories of usability metrics were identified with multiple interchangeable terminologies in each category (Figure 4). In finalizing the answers to RQ2, it can be seen that the highest metric used is satisfaction, which goes in line with the number of majority self-reported data presented earlier. Table 11 presents the commonly used metrics together with other related terminologies within the same group. While most metrics presented below are inspired through \begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Evaluation** & **Fq.** & **Refs.** \\ \hline Within-subjects & 19 & [21, 28, 29, 31, 40, 44, 45, 47, 48, 50, 51, 52, 54, 55, 56, 68, 80, 81, 82] \\ Between-subjects & 48 & [12, 13, 14, 15, 16, 17, 18, 19, 22, 24, 25, 26, 27, 28, 29, 34, 35, 36, 37, 38, 39, 41, 42, 43, 46, 47, 48, 53, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 83] \\ Combination of Both & 4 & [20, 23, 33, 64] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Sample evaluation approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Metrics** & **Fq. ** & **Refs. ** \\ \hline Performance & 2 & [28, 36] \\ Self-reported & 49 & [12, 13, 15, 16, 18, 19, 22, 25, 26, 27, 31, 32, 33, 34, 40, 41, 43, 49, 50, 51, 62, 64, 65, 66, 67, 68, 69, 70, 81, 83] \\ Combination of Both & 20 & [14, 17, 20, 21, 23, 24, 29, 38, 39, 42, 44, 45, 46, 63, 78, 79, 82] \\a derivation of three major ISO 9142-11 metrics, honorable mentions of each metrics' expression are seen important for the uniquely added values in each suggested metric.
## 2 Materials and Methods of Determining the Design of the Proposed Solutions
### Coolant Supply Channels  
To implement the internal and combined coolant supply schemes, the end mill shank, tool holder, cartridges, and cutting inserts were equipped with internal channels. Their shape and location were determined to ensure sufficient flow capacity and relative simplicity of manufacture. The shank was equipped with three drilled channels (Figure 5a). The central channel (1) with a diameter of 10.2 mm was positioned on the tool rotation axis. Two side channels (2, 3) of 6.0 mm diameter, perpendicular to the axis, were created to connect the central one with the coolant circuit of the tooling. The locking bolt had holes (4) to pass the coolant further through the tool holder's channels.  
Two of the holder channels (5, 6) were manufactured by drilling and milling to merge with the cartridges. Another two (7, 8) provided external coolant supply to the flank faces of the cutting inserts through the designated pipes (9, 10) (Figure 5b). Sealing of the channels' machined sections occurs when the cartridges are installed in their seats.  
The cartridges were equipped with channels of complex configuration. The first channel (11) is to supply coolant to the cutting insert. After that, via the second channel (12), coolant may be discharged from the cutting zone or forwarded to the insert's rake face through the pipe (13) (Figure 5c). From this point, the aforementioned channels (7, 8) of the tool holder are begun as well.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Notes** & **Cutting Angle Explanation** \\ \hline Full length \(L\), mm & 200.0 & From the spindle to the insert tip & \\ Mill diameter \(D\), mm & 63.0 & Between the inserts tips & \\ Number of teeth \(z\) & 2 & = number of cartridges & \\ Rake angle, \({}^{\circ}\) & \(-6.0\) & \\ Radial rake angle, \({}^{\circ}\) & \(-9.0\) & Angles explained with an unmodified PSSN cartridge & \\ Lead angle, \({}^{\circ}\) & 45.0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: End mill geometric parameters.  
Figure 5: Tool shank (**a**), tool holder (**b**), and cartridge (insert + carbide plate) (**c**) supply channels.  
The geometry of the cutting insert channel was determined by two factors. First, the maximum thermal efficiency of the supplied coolant had to be ensured. Second, there had to be no risk of the insert body experiencing catastrophic failure caused by the cutting loads (verified by the stress simulation, described in Section 2.8).  
The selected cartridges were equipped with a carbide plate installed under the cutting insert. This plate was modified in two ways. For the tool to function as the proposed internal and combined supply schemes, inlet and outlet holes were created in the body of the plate (14a). The position of those coincided with the cutting insert channel. To supply coolant only externally, an insert with no channel is meant to be used. Therefore, to keep the coolant flow, a bypass channel was created in the carbide plate (14b).
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
## 4 Results and Analysis
### Particle Swarm Optimization
After each accelerated and positioned update, the region's size is determined based on the size of the region, and the operation is repeated until it is complete. Based on the above regression model, the minimum cutting force combinations such as point angle, helix angle, and edge radius are obtained. Figure 8 shows the iterative approach of PSO, and we can see that after 25 iterations, its most optimal point has become stable. The final minimum drilling force is 30.31 N, and the corresponding point angle, helix angle, and edge radius of the twist drill are \(95^{\circ},35^{\circ}\), and 0.02 mm, respectively. In order to test the accuracy of the PSO algorithm, the numerical simulation experiments are carried out with the above-obtainedbit shape parameters, and the maximum drilling force is 29.5 N. The deviation of the optimal solution is 2.75%. The results show that the edge shape parameters obtained by the PSO algorithm can reduce the cutting force to the maximum extent within the range of experimental parameters. Under the original drilling parameters, the drilling force of the swine femur specimen is 53.93, and the cutting force of the new cutting edge can be significantly reduced. Therefore, it is vital to study the influence of bit shape parameters on the drilling force and reduce the drilling force. Figure 9 shows the regression surface diagram between the cutting force and the point angle, helix angle and the edge radius. From Figure 9a-c, it can be found that the most critical parameter affecting the cutting force is the edge radius, followed by the point angle and finally the helix angle. This is because the edge radius directly participates in the whole drilling process. With the increase in the edge radius, the contact area with material increases, which leads to the increase in drilling force. With the increase in the point angle, the contact area between the flank and the material increases, which also affects the drilling force. Figure 8: The iterative process of particle swarm optimization. Figure 9: _Cont._
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 3 Proposed Framework: Hybrid STL-Deep Learning IDS  
This section details the architecture and operation of our proposed hybrid IDS framework, designed to leverage the strengths of STL and DL for enhanced IoT intrusion detection. The framework's core principle is to exploit the predictable nature of normal network traffic by pre-filtering it using STL, allowing the DL models to focus on classifying potentially malicious traffic more effectively.  
Framework Overview  
The architecture of the proposed framework is depicted in Figure 1. The data flow through the system proceeds as follows:  
1. Data Ingestion: Raw network traffic data are collected and ingested.
2. Preprocessing: The raw data undergo a comprehensive preprocessing phase, as detailed in Section 4.1.
3. STL Analysis: The processed data are then passed to the STL analysis module, where the time series data are decomposed, as further detailed in Section 4.2.
4. Feature Selection (PCA): After STL, the features are selected using PCA, as described in Section 6.2.
5. Deep Learning Classification: Finally, the selected features (or the STL remainder; see below) are fed into a chosen deep learning model (LSTM, RNN, or DNN) to classify each network packet as either normal or belonging to a specific attack class. This process is detailed in Section 4.4.
6. Alerting/Response: Upon detection of a potential intrusion (malicious traffic), an alert is generated, and a pre-defined response action can be initiated.  
Rationale: This hybrid approach leverages STL to model and remove predictable regularities in network traffic, thereby reducing the computational burden on the deep learning models and focusing their analysis on the remaining, potentially malicious, patterns. This strategy is designed to improve both detection accuracy and operational efficiency.  
From a computational perspective, the framework involves sequential stages: data preprocessing, STL decomposition, feature selection (PCA), and deep learning inference. The overall processing time is a composite of the computational cost of each stage, with STL decomposition and deep learning inference being the most significant contributors, especially for large datasets.
###### Abstract  
This study aimed to evaluate the depth of cure (DoC) of bulk-fill composite resins, measured by the bottom-to-top Vickers microhardness ratio, using different light-curing units (LCUs): single-wave LED, polywave LED, and halogen. Six bulk-fill composites--Tetric EvoCeram Bulk Fill, X-tra base, SonicFill, Venus Bulk Fill, SDR, and Fittek Bulk Fill--were tested. Four LCUs, including one halogen (Elipar Trilight) and three LEDs (Demi Ultra, Valo, and Bluephase style), were employed for polymerization. Vickers hardness measurements were taken at depths of 1 mm to 5 mm. One- and two-way ANOVA (\(\alpha=0.05\)) were used for data analysis. The results revealed significant differences in microhardness and microhardness ratios among the composites at depths of 4 mm and beyond, depending on the LCU used. It was observed that most bulk-fill composites showed an adequate DoC up to 4 mm, but the effectiveness varied with different LCUs. Importantly, polywave LED LCUs did not exhibit a superior advantage in achieving depth of cure compared to monowave LED LCUs for composites containing multiple photoinitiators. These findings suggest that while several factors affect the DoC, the type of LCU plays a crucial role, and polywave LEDs may not offer additional benefits over monowave LEDs.  
composite resin; bulk fill; microhardness; microhardness ratio; light-curing units (LCUs); monowave LED; polywave LED; halogen curing light; photopolymerization; Vickers hardness test; depth of cure; post-cure +
Footnote †: journal: applied sciences
## 2 Theoretical Basis
### Wavelet Packet Decomposition and Damage Index  
To improve signal resolution, [PERSON] and [PERSON] et al. have conducted numerous studies [19; 20; 21; 22]. With the deepening of research, wavelet packet analysis has gradually become widely used. Wavelet packet analysis can accomplish the effective time-frequency analysis of monitoring signals, by performing the multi-layer and refined decomposition of both the low-frequency and high-frequency parts of the signal. Additionally, it can au  
Figure 1: Dispersion curve of the layered pipe structure.  
tonomously select frequency bands that match the spectral characteristics of the monitoring signal, thereby improving the time-frequency analysis effect of the signal.  
Due to the many advantages of wavelet packet analysis, it has been widely used in damage identification and localization in structures. [PERSON] et al. [23] conducted a study on the damage level of a bridge deck, and combined intelligent aggregates with damage indices. They found that this approach not only allowed for damage localization, but also enabled the identification of the degree of damage. [PERSON] et al. [24] investigated the damage state of reinforced concrete shear walls under cyclic loading, using a damage index based on wavelet packet decomposition. [PERSON] et al. [25] applied a wavelet packet decomposition-based damage index to identify damage in single-layer pipe structures, and demonstrated that multiple damage locations could be accurately detected by detecting abrupt changes in the damage indices, within specific regions of the damage index matrix.  
For this study, the guided wave signals received at the piezoelectric element were decomposed into five levels using wavelet packet analysis, with the mother wavelet being Doubechies 5 (also known as db5). This resulted in 2\({}^{n}\) subsets of high- and low-frequency bands. The wavelet packet decomposition tree is shown in Figure 2.  
The relationship between the original signal S, and the subset of frequency band signals obtained from the decomposition, can be expressed by the following equation:  
\[S=X_{1}+X_{2}+\cdots+X_{2^{n}}\ \Big{(}i=1,2,\cdots,2^{5}\Big{)} \tag{2}\]  
where \(i\) is the frequency based; and \(X_{i}\) is the subset of signals in band \(i\) obtained after decomposition.  
[PERSON] et al. [25; 26] applied wavelet packet decomposition to study the damage width in single-layer pipe structures, and proposed a new damage parameter, the damage index. The damage index was calculated for different damage scenarios, and was used to construct a damage index matrix, which is formulated by the following equation:  
\[E_{i}=\sum_{k=1}^{k=m}X_{i,k}^{2} \tag{3}\]  
Figure 2: Wavelet packet decomposition tree at level 5.  
\[I=\sqrt{\frac{\sum_{i=1}^{2^{n}}(E_{k,i}-E_{1,i})^{2}}{\sum_{i=1}^{2^{n}}E_{1,i}^{2 }}} \tag{4}\]  
where \(k\) is the number of signal samples in the band; \(E_{i}\) is the signal energy in band \(i\); \(E_{1,i}\) is the wavelet packet energy in the healthy state of the pipe structure; and \(E_{k,i}\) is the wavelet packet energy in the damaged state of the pipe structure.
# Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
2. The outage probabilities of the proposed system model are obtained and investigated with the consideration of interference power constraints over mixed fading channels. 3. The effect of various factors that could influence the system performance such as the number of relays, fading severity parameters, fading model, PU location, and mobile end user speed are investigated as well. The remaining sections of this paper are organised as follows. First, Section 2 presents the system model considered in this work, along with the channel models. The mathematical analysis for the outage probability of the proposed system model is presented in Section 3. Section 4 provides the numerical and simulation results along with logic explanations. Section 5 summarizes and concludes this work. Finally, Section 6 provides the limitations of this work and presents a few ideas that can be explored as future work.
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 1 Introduction
A 7-stage Villard rectifier [24] made of HSMS 2850 Schottky diode is presented and investigated for its functionality at 550 MHz and 900 MHz. The properties of the rectifier are examined based on the number of stages, effects of capacitance, and stage capacitance variations. The rectified output voltage of 9.17 V and 3.78 V are observed for a 0 dBm input at 900 MHz and 550 MHz with 44.4% rectifier efficiency. It is examined that the performance of the rectifier increases up to a certain stage and then degrades. A two-stage cascade L-section rectifier [25] is designed for broadband applications from 870 MHz to 2.7 GHz. The first stage of the L-section is the high pass filter which rectifies lower band frequencies up to 1.5 GHz, and the second stage of the L-section is the inductance to rectify the frequencies beyond 1.5 GHz. The optimized rectifier conversion efficiency is 30% over the frequency range from 870 MHz to 2.5 GHz at 0 dBm input and 2 K\(\Omega\) load resistance. In all the above rectenna designs, the receiving antenna models possess a degree of design complexity, and it is observed that the rectifier design of the single stage voltage doubler is widely used because it is compact and requires less components, which reduces the losses during conversion and results in greater efficiency. Here, a simple compact circular broadband antenna that has integrated with an L-section matching network and a voltage doubler rectifying network is designed using an HSMS2852 Schottky diode for RF-energy scavenging. The rectifying antenna is proposed to harvest and change RF-energy to DC at the ISM (2.45 GHz) band which has become widespread due to the advancements in communication means. A prototype of the rectenna was fabricated and measured to validate the performance at the ISM band.
###### Abstract  
Aerodynamic roughness (AR) is an important parameter that influences the momentum and energy exchange between the earth's surface and the atmosphere. In this study, profile wind data observed during the vegetation growing period (April-September) in 2013 and 2014 at the A'rou grassland station, which is in the upstream of the Heihe River Basin (HRB), were used to determine the relationship between the field AR and the Moderate-resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) R index, the normalized difference vegetation index (NDVI), and a combination of these indices. In addition, the relationship between the average wind speed at a height of 1 m and the field AR is also presented. The results indicate that the MODIS NIR BRDF_R index and the NDVI are both sensitive indicators of the AR over grassland (\(R^{2}\): 0.5228 for NIR BRDF_R; \(R^{2}\): 0.579 for NDVI). Moreover, the combined index shows a significantly increased \(R^{2}\) value of 0.721, which is close to the result inferred from the wind speed (\(R^{2}\): 0.7411). The proposed remote sensing-based combination index (CI) has the potential for use in evaluations of the AR over grasslands during growing season and its sensitivity can reach levels that are comparable to considering the effects of wind speed, which usually requires ground-based observations.  
a 2017 8, 16; doi:10.3390/atmos8010016
## 3 Results
### Monthly Solar Energy  
The presentation of the annual and the seasonal solar irradiation comparisons between the ISO- and the HAY-modelled results takes place in this section. Figure 8 shows the intra-annual variation of H\({}_{\rm g}\) for the three configuration modes and both transposition models. In the case of a solar system with configuration type-(i), the monthly values of H\({}_{\rm g,g,ISO}\) and H\({}_{\rm g,g,g,HAY}\) are almost identical (see the blue symbols and the lines in Figure 8). This means that the L-J model provides satisfactory results for a flat-plate solar collector with a fixed-tilt angle and southward orientation. This isotropic model has been used by many authors in studies for estimating solar insolation on mode-(i) solar collectors, e.g., in [44].  
Figure 7: Seasonal solar energy yields, H\({}_{\rm g}\), averaged across the 82 sites in Saudi Arabia and the period 2005-2016 under all-sky conditions. The values are shown for the 3 solar system configuration modes and both ISO- and HAY-modelled results. The shaded bars indicate the ISO estimates, and the solid bars the HAY ones. The seasons on the x-axis are spring (1) to winter (4).  
For the other two modes, the order of higher to lower monthly solar irradiation values is \(\rm H_{g,t,H\rm{AY}}\), \(\rm H_{g,\beta,t,H\rm{AY}}\), \(\rm H_{g,t,ISO}\), \(\rm H_{g,\beta,t,ISO}\). Nevertheless, the difference between the HAY- and the ISO-estimated solar radiation in the same configuration type lies in the range \([100,200]\) kWhm\({}^{-2}\) month\({}^{-1}\). This means that if any of the two transposition models is used by the interested solar energy engineer, the result may not be acceptable as the difference is in the range \([3.33,6.66]\) kWhm\({}^{-2}\) day\({}^{-1}\). Indeed, these differences are not negligible compared to the daily solar energy gained by a flat-plate solar collector working in Saudi Arabia in mode (ii) with a fixed-tilt angle tracking the Sun or in mode (iii) with a varying-tilt angle tracking the Sun, i.e., a daily average of \(\approx\)8.5 kWhm\({}^{-2}\) day\({}^{-1}\) throughout the year. In this case, the error introduced when considering the difference \(\rm H_{g,H\rm{AY}}-\rm H_{g,ISO}\), is in the range \([39.2\%,78.4\%]\), which is deemed quite high. Therefore, the solar energy engineer must be cautious about what model to choose for assessing the solar energy potential at a location. On the other hand, it is common practice for solar energy engineers to use an anisotropic model for assessing the solar energy potential on inclined solar collectors; see [45]. This is true for configuration type-(ii) and -(iii) solar collectors, and for mode-(i) solar systems, the L-J model can safely be used.