esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Traits**} & **Phenotypic** & \begin{tabular}{c} **Phenotypic** \\ **Variance** \\ **(or2p)** \\ \end{tabular} & \begin{tabular}{c} **Genotypic** \\ **Coeffent of** \\ **Variation** \\ **(CV \%)** \\ \end{tabular} & \begin{tabular}{c} **Genotypic** \\ **Variance** \\ **(or2c)** \\ \end{tabular} & \begin{tabular}{c} **Genotypic** \\ **Coeffent of** \\ **Variation** \\ **(GCV \%)** \\ \end{tabular} & \
\begin{tabular}{c} **Heritability** \\ **(H)** \\ **(G)** \\ \end{tabular} &
\begin{tabular}{c} **Genetic** \\ **Advance as** \\ **Percentage of** \\ **Mean (GAM \%)** \\ \end{tabular} \\ \hline Flavonoids & 26,615 & 19.56 & 26,406 & 19.48 & 0.99 & 226.61 & 27.16 \\ Phenolics & 14,302 & 20.18 & 13,756 & 19.79 & 0.96 & 161.14 & 27.19 \\ Vitamin C & 110.21 & 56.34 & 105.72 & 55.19 & 0.95 & 14.09 & 75.67 \\ DPPH & 64,266 & 15.32 & 62,133 & 15.06 & 0.96 & 343.13 & 20.74 \\ ABTS & 528,371 & 22.08 & 468,188 & 20.78 & 0.88 & 901.74 & 27.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimates of variance, heritability (H) and genetic advance of fruit phytochemical content and antioxidant activity in 30 blue honeyuckle genotypes. Figure 1: Dendrogram estimating distance among 30 blue honeyuckle genotypes based on phenolic acid, flavonoid, vitamin C content and antioxidant activity. gain with the selection. Moreover, due to the insignificant influence of environmental conditions, they may constitute the basis for direct selection [55]. High heritability coupled with high genetic advance as a percentage of the mean was observed for vitamin C content, and thus selection would be very effective for this trait. For phenolic and flavonoid content and antioxidant activity, satisfactory selection effects can also be obtained, because heritability and genetic gain are still high. Since this is the first report regarding heritability and genetic advance for the content of phenols, flavonoids, vitamin C and antioxidant activity in blue honeysuckle, the results can be applied only to other species of berry plants. For example, in physico-chemical studies of various characteristics of strawberry fruits, [PERSON] et al. [50] observed heritability in the range 0.79-0.31, while [PERSON] et al. [48] estimated ascorbic acid heritability in this species at the level of 0.76. In the red raspberry, heritability (narrow-sense) estimates of [PERSON] et al. [56] were H = 0.54 for antioxidant activity and 0.48 for total phenolic content; these estimates implied that a rapid response to selection was possible. [PERSON] et al. [56] estimated the heritability of blueberries to be 0.43 for antioxidant activity and 0.46 for total phenolics. Antioxidant capacity and total phenolic content (TPC) were at the same level of heritability (0.55) in blackcurrants [57].
## 3 Results
### Microstructures
#### 3.2.2 TEM Microstructures
ultrafine grains was 184 nm (Figure 6c). The precipitates in the CR-373K (1200 min) samples are shown in Figure 6d,e. As shown in Figure 6d, densely distributed nanosized second-phase precipitates had formed at this stage as marked by yellow circles. The widths of the precipitates were approximately 2-6 nm and the lengths were 5-30 nm at the higher density of \(\sim\)4.1 \(\times\) 10\({}^{15}\) m\({}^{-2}\). Figure 6e shows the second-phase precipitates at a higher magnification and the strong 3/4[200]\({}_{\rm Al}\) spots in the Fourier transform patterns. The \(<\)011\(>\) zone axis indicated that the precipitate was \(\theta^{\prime}\)-Al\({}_{2}\)Cu phase, which was consistent with the literature [22; 23]. Figure 7 shows HRTEM micrographs and the corresponding inverse fast Fourier transform (IFFT) images from the regions containing \(\theta^{\prime}\) phases from the CR-373K (1200 min) samples before (a,b) and after (c,d) tensile testing. As shown in Figure 7b, prior to tensile deformation, few dislocations existed around \(\theta^{\prime}\) precipitates and no dislocations existed inside the precipitates. However, after tensile deformation dislocation piled up near the \(\theta^{\prime}\) phase causing an increase in dislocation density (Figure 7d). In particular, the IFFT images showed that some dislocations (red mark in Figure 7d) were able to cut into the \(\theta^{\prime}\)-Al\({}_{2}\)Cu precipitates due to tensile deformation. The above experimental  
Figure 5: (**a**) Bright-field TEM micrograph of CR-373K (1200 min) samples; (**b**) high-resolution transmission electron microscopy (HRTEM) micrograph of the white frame in (**a**) and corresponding Fourier transform patterns of the red frame; (**c**) the SAED pattern with the \(<\)011\(>\) zone axis. results showed that the 0\({}^{\prime}\)-Al\({}_{2}\)Cu precipitates could significantly increase the storage capacity of dislocations [24; 25]. Figure 6: TEM micrograph for the CR-373K (1200 min) samples: (**a**) Bright-field TEM image showing ultrafine grains (inset is the SAED pattern taken along the \(<\)011\(>\) zone axis); (**b**) nanoscale grains; (**c**) statistical grain size distribution; (**d**) bright-field TEM image of the precipitates; (**e**) HRTEM and Fourier transform patterns of a typical precipitate. Figure 7: HRTEM micrograph of the CR-373K (1200 min) samples before (**a**) and after fracture (**c**); inset is the Fourier transform patterns taken from the white wireframe; (**b**) corresponding IFFT pattern of (**a**) with lower dislocation density (marked by “T”); (**d**) corresponding IFFT pattern of (**c**) with higher dislocation density. Red dislocations “T” represent the introduction of dislocations into the 0\({}^{\prime}\) precipitates.
## 1 Introduction
In Europe, it is cultivated by eight companies, yielding an annual dry biomass production of around four tonnes. _P. tricornutum_ is rich in PUFAs, specifically EPA, and pigments such as fucoxanthin. Currently, it is used as a source of fucoxanthin and omega-3-rich oil extracts [20]. However, _P. tricornutum_ is not approved, as yet, by the EFSA as a novel food and is still in the authorisation process. Although both _T. chuii_ and _P. tricornutum_ exhibit exceptional functional properties, their application in the food matrix is still limited. In the context of bakery products, there is a need for more knowledge on how the baking process affects the microalgae's bioactive qualities [2; 21; 22]. As far as we know, to date, there is no available data on the incorporation of _P. tricornutum_ into bread products. In fact, microalgae can modify the structure and rheological properties of dough. Previous studies suggest that the presence of microalgae affects starch granules' hydration and protein structure within the gluten network [23], resulting in lower loaf volume and subsequently affecting other quality attributes [2; 10; 24]. Information about dough properties is important for predicting the potential application of the microalgae and the quality of the produced bread [25]. The aim of the present study was to assess changes in the rheological properties of wheat dough resulting from the addition of _C. vulgaris_, _T. chuii_, and _P. tricornutum_, evaluating their respective impacts on the structure, nutritional value, and bioactive compounds of the resulting wheat bread. This study is part of the YUMAlgae project, which aims to incorporate sustainable and nutritive microalgae ingredients into staple food products, namely wheat bread. An incorporation level of 4% of freeze-dried microalgae biomass, related to wheat flour, was considered to obtain an interesting nutritional value and health impact without comprising the mechanical behaviour [12] and sensory profile to a large extent.
## 2 Background and Related Work
### Related Work  
Important research addresses privacy and security challenges associated with the protection of personal information, alongside obfuscation techniques that are applied to visual data.  
In [PERSON] et al. [28], the authors examine security and privacy challenges in AR systems, focusing on how sensitive information can inadvertently be displayed or recorded by AR devices. The study discusses how AR-specific contexts, such as shared visual data and continuous environmental scanning, create privacy risks. The authors propose mechanisms like access controls for data streams, the obfuscation of sensitive elements, and user-consent mechanisms that address privacy without interrupting the AR experience. The study is relevant as it focuses on developing privacy techniques that are uniquely adapted to the immersive and interactive characteristics of AR environments.  
[PERSON] et al. [29] explore the effectiveness of blurring and pixelation obfuscation techniques in protecting the privacy of individuals depicted in images. The study examines how well these techniques prevent the recognition of sensitive content while balancing the need for interpretability in different contexts. The study indicates that while both blurring and pixelation can obscure facial features, their effectiveness varies depending on the context and the observer's familiarity with the target subjects.  
In [PERSON] et al. [30], the authors explore techniques for person recognition while omitting facial features, such as relying on body shape, clothing, and pose. The authors address  
Figure 1: Visualizations of privacy protection techniques in AR. The “As is” image shows the original image, while masking, pixelation, and blurring can be used to obscure sensitive information on screens within AR environments.  
how these methods can still allow for recognition in social media contexts, raising privacy concerns even when facial details are obscured. Their findings suggest that solely obscuring faces may be inadequate for ensuring comprehensive privacy protection.  
[PERSON] et al. [31] assess the privacy concerns associated with vision-based monitoring systems, which capture video or images for safety monitoring. The study finds that while monitoring enhances safety, privacy concerns arise from the continuous capture of personal spaces. They propose obfuscation techniques that extend beyond face concealment, incorporating methods such as low-resolution imagery and the selective masking of sensitive areas.  
While a few studies have explored the integration of obfuscation techniques within AR systems, to the best of our knowledge, none have conducted surveys with volunteers to empirically measure the effectiveness of these techniques in AR-specific contexts.
## 3 Results and Discussion  
### Results of Feature Selection  
Table 6 shows that the feature subset found by SFS was a lot better than the feature subset found by SBS. It improved the performance and reduced the dimensionality of the original dataset (from 34 to 7); therefore, we chose it to be the final feature subset for the KNN algorithm. However, the feature subset found by SBS failed to improve the performance, so we inferred that some relevant features might be eliminated in early iterations, which means that once those features are excluded, they cannot be included later, even though those features might possibly increase the performance [48]. After all, SBS has no guarantee of finding the optimal feature subset [27].  
As shown in Table 7, the feature subset found by SFS is much better than the feature subset found by SBS. It improved the performance and reduced the dimensionality of the original dataset (from 34 to 7). Thus, we chose it to be the final feature subset for CARTs.  
As seen in Table 8, the best feature subset found by RFECV successfully improved the performance and reduced the dimensionality of the original dataset (from 34 to 13); therefore, it was selected to be the final feature subset for the RF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Feature Set**} & **Model Performance** & \multirow{2}{*}{**Number of Features**} \\  & **(R\({}^{2}\))** & \\ \hline Original feature set & 0.3838 & 34 \\ \hline The best feature subset found by SFS & 0.5709 & 7 \\ \hline The best feature subset found by SBS & 0.3683 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of feature selection using KNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Feature Set**} & **Model Performance** & \multirow{2}{*}{**Number of Features**} \\  & **(R\({}^{2}\))** & \\ \hline Original feature set & 0.5354 & 34 \\ \hline The best feature subset found by SFS & 0.7173 & 7 \\ \hline The best feature subset found by SBS & 0.6140 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of feature selection using CARTs.  
Table 9 shows all feature subsets selected after feature selection using different learning algorithms. Although the relevant features of those feature subsets are different, there are some common relevant features, including \"the mental suffering damages that plaintiff claims\" and \"the age of the victim\", which were selected by all of them. In addition, \"the total number of plaintiffs who are victim's parents in the same case\", \"the total number of plaintiffs who are victim's children in the same case\", \"the defendant's income\", and \"the sum of mental suffering damages that all plaintiffs claim in the same case\" were selected by two of them. Therefore, we can infer that those relevant features are crucial to the assessment of mental suffering damages in fatal car accident cases in Taiwan.
# Implications of Rigid Gripping Constraints on Clubhead Dynamics in Steel Golf Shafts
## 1 Introduction  
Golf robots are an integral part of the process of testing golf clubs as they overcome many of the limitations imposed by player tests. Despite much research into their operation and application, little effort has been dedicated to following up on recommendations to evaluate the effect of the robot gripping mechanism [1; 2; 3]. The gripping mechanism commonly adopts the form of a rigid 'clamp', which has been shown to impose very different constraints on a club's dynamic response when compared to both human hand-held and freely-suspended conditions [4]. Given the brevity of a typical impact between club and ball in golf, the influence of the shaft on the dynamics of the clubhead during impact has long been considered negligible [5; 6; 7], however there is little empirical evidence to support this assumption. Although the rigid gripping mechanism will undoubtedly have some effect on the dynamics of the club when considering a robot swing in its entirety, the dynamic response of theshaft during the contact period between clubhead and ball is considered to be more pertinent. Whilst some research has been dedicated to achieving a robot swing that is a closer representation to that of golfers' swings [8; 9], the period during which clubhead and ball are in contact has not specifically been investigated during robot testing. Measurement of contact time in golf has been well documented (typically around 0.5 ms at realistic inbound velocities [10; 11; 12]), however rates at which various modes of vibration travel from the clubhead to the grip are not as widely reported. Experimental results for steel shafts suggest that this duration is around 1 ms [13], although other studies suggest that this would be an order of magnitude smaller for carbon fibre composite shafts [14]. Studies modelling golf club shafts have reported durations of 1-2 ms before any effect of impact is 'felt' by the golfer [15; 16], although what was meant by 'felt' with respect to measurement times was not explicitly stated. It is difficult to make judgements as to whether strain propagates at a great enough speed along a golf shaft to influence impact dynamics based on the reported evidence; the majority of studies suggest it does not [15; 16; 13], although this is likely to depend largely on material properties.  
Thus, the purpose of the present study was to evaluate the potential for the rigid clamping at the grip of a golf club to influence clubhead dynamics during ball impact through the measurement of shaft strain propagation rates. It was hypothesised that the gripping mechanism would not exert greater influence on the clubhead during impact than that associated with the hands of a golfer. The hypothesis was tested by measuring the time taken for vibration to travel from the clubhead to the gripping mechanism and subsequently return to the clubhead.
# Analysis of Flow Field Characteristics in the Three-Phase Jet Fire Monitor Head
###### Abstract  
To enhance the jet performance of the three-phase jet fire monitor (TFJFM), an analysis was conducted on the internal flow field (IFF) characteristics of the monitor head. Using the volume of fluid method, the impact of key structural parameters, such as the powder-pipe bending angle and the supporting blade length, on the turbulent kinetic energy (TKE) of the IFF, the velocity distribution uniformity at the nozzle outlet, and the pressure drop (PD) between the inlet and outlet of the internal flow field, was analyzed. The study revealed that increasing the bending angle of the powder pipe will lead to a significant improvement in the uniformity of velocity distribution in the IFF. Extending the supporting blade length helps reduce the average TKE at the nozzle outlet but has a minimal impact on the velocity distribution uniformity and the PD between the inlet and outlet. Reasonable design of the distance between the supporting blades and the bending section of the powder pipe can improve the IFF characteristics, reducing local pressure losses and peak TKE. The research results can effectively improve the IFF characteristics, enhance jet performance, and provide theoretical basis and technical support for the design and optimization of the TFJFM.  
three-phase jet fire monitor; internal flow field; powder pipe; supporting blade +
Footnote †: journal: Applied Sciences
# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 2 Fingerprint-Based SSL  
As shown in Figure 1, the process of fingerprint-based SSL consists of two phases: the offline sampling phase for database construction and the online positioning phase for vocal target location estimation.
## 4 Proposed Methodology of Hybrid Channel Modeling
### Power Delay Profile of Mine's Gallery  
For wideband transmitted signals, distortion is caused by dispersion in the environment of underground mines which can be a source of inter-symbol interference (ISI). This work investigates the power delay profile (PDP) by characterizing the propagation channel. Let a signal \(s(t)\) have the bandwidth \(B\) and central frequency \(f_{o}\), generating an expression \(f\in(f_{o}-B/2,f_{o}+B/2)\), so the actual signal can be obtained by the addition of each sine wave that belongs to this band.  
The strength of each signal can be obtained by taking a Fourier transform of signal \(s(t)\) and the resultant \(S(f)\) which is an even function of frequency \(f\) as described below  
\[s(t)=\int_{f_{o}-B/2}^{f_{o}+B/2}S(f)2\cos(2\pi ft)df \tag{24}\]  
The mode intensity parameter \(C_{mn}(f)\), EM field distribution \(E_{m,n}^{sign}(x,y,f)\), attenuation coefficients \(\alpha_{mn}(f)\) and phase-shift coefficients \(\beta_{mn}(f)\) is transformed into frequency-dependent functions and the variation can be obtained with the frequency in the transmission delay of any specific mode. The transmission delay function \(EH_{mn}(f)\) can be computed by \(\tau_{mn}(f)=z/v_{mn}(f)\), where \(v_{mn}(f)\) denotes the group velocity as given below  
\[v_{mn}(f)=c\sqrt{1-\left(\frac{c\sqrt{\left(\frac{m\pi}{2\pi}\right)^{2}+ \left(\frac{m\pi}{2\pi}\right)^{2}}}{2\pi f}\right)^{2}} \tag{25}\]  
As shown in Equation (25), the group velocity depends on frequency and mode order. Different frequency signals from the same mode can have different propagation delays. Conversely, multiple modes of identical frequency exhibit different propagation delays. Dispersal among the frequencies and modes are considered for computing power delay profile. The relative received power \(P_{WB}\) for a wideband signal can be obtained by the summation of the entire contribution from all particular modes and frequency elements for a certain time \(t\) at any position \((x,y,z)\) using  
\[\begin{split} P_{WB}(x,z,t)&=P_{t}G_{t}G_{r}\Big{\{} \frac{1}{E_{m,n}}\int_{f_{o}-B/2}^{f_{o}+B/2}[C_{mn}(f)E_{m,n}^{sign}(x,y,f) \\ &\quad e^{-\alpha_{mn}z}S(f)\delta(t-\frac{z}{v_{m}n}(f))\cos(2 \pi ft-\beta_{mn}z)]df\Big{\}}\end{split} \tag{26}\]  
where  
\[\delta(x)=\begin{cases}1,&ifx\geq 0\\ 0,&otherwise\end{cases} \tag{27}\]
## 4 Discussion
Some aquatic insects are known to communicate by ultrasounds. Water boatmen (Hemiptera: Corixidae: Microneta), for example, produce courtship songs which are partially in the ultrasonic range (about 5-22 kHz) [59]. Therefore, further research is required to understand the impact of ultrasound-based mosquito larvicidal treatments on the mating outcomes of aquatic Hemiptera. It should be noted that ultrasound may cause negative effects on some amphibians, since [PERSON] et al. [60] tested a variety of frequencies inducing striking mortality of _Rana temporaria_ L embryos after 5 to 15 min at 0.88 MHz and 0.2-0.7 W/cm\({}^{2}\). However, compared to the latter, our results are highly promising, since the Larvasonic device tested in this study at a frequency range of 18-36 kHz did not lead to noticeable mortality on non-target cyclopoid crustaceans, which are also of interest for mosquito biocontrol [61]. Overall, in the present work we presented selected ultrasonic frequencies from 18 to 30 kHz leading to high mortality of different young instars of _Ae. aegypti_. This supports the approach using acoustic waves for the management of mosquito larval populations. The present results showed that mosquito mortality is related to exposure time and distance from the transducer. The chemical and physical forces provided by ultrasonic cavitation lead to severe damage to cell membranes that subsequently kills developmental instars of the targeted mosquito species. Based on our results, ultrasound-based devices can be considered as a viable technology for mosquito control. Ecofriendly characteristics include that they are harmless to non-target invertebrates, such as the copep _M. formosanus_. This particular copep species could be used as an additional biocontrol agent, since we preliminarily also observed that its predation rates against mosquito larvae were not affected in our trials.