esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Analyses
In particular, Figure 15(a) compares the mean final weights from five independent runs of each optimizer, while Figure 15(b) plots the objective function value over the analysis time. Figure 16: (**a**) Mean final weights for the case B study; (**b**) convergence of the objective function over analysis time for the case B study. Figure 15: Convergence curves for case B obtained with the GA (**a**), FA (**b**), and GSO (**c**) algorithms. Comparing the two graphs in Figure 16, the Genetic Algorithm yields the lightest structures on average but at a substantially higher computational cost. Specifically, GA requires approximately 81% more time than GSO and 24% more time than FA to reach its optimum. FA, in turn, takes about twice as long as GSO to converge. Definitively, GSO achieves a nearly equivalent final weight (within 1%) yet converges in less than half the time of GA and just over half the time of FA. Thus, in this case study as well, GSO proves the most efficient choice when both weight minimization and run time are critical. Figure 17a,b show the optimized structures obtained in ANSYS, respectively, for case study A and case study B. The third case study (case C) presented the greatest challenge, as it involved simultaneous optimization of both member dimensions and internal topology. Figure 18 shows the convergence curves for all three algorithms. The lightest configuration, with a minimum weight of \(W_{min}\) = 242.41 kg, was obtained by the GSO algorithm. Averaged over five independent runs, the GSO result is approximately 12% lighter than that of FA and 8% lighter than that of GA (Figure 19a). As before, the GA remains the slowest to converge (Figure 19b), requiring up to 104% more computational time than GSO and up to 40% more time than FA to reach its optimum. The initial configuration used for comparison in Case C was deliberately set to represent the heaviest feasible structure: all bars were active, and the cross-sectional dimensions were at their maximum values (see Figure 20a). This setup aims to clearly highlight the potential of the optimization process. The final optimized configuration is shown in Figure 20b. In detail, the redesigned structure weighs 95% less than the initial model and exhibits a markedly reconfigured internal topology. Figure 17: Optimized structures obtained in ANSYS (**a**) for the case A study; (**b**) for the case B study. Figure 18: Convergence curves for case C obtained with the GA (**a**), FA (**b**), and GSO (**c**) algorithm.
## 2 Materials and Methods  
### Participants  
This pilot cross-sectional study was approved by the Institutional Review Board of Gachon University (No. 1044396-202403-HR-051-01) and conducted in accordance with the World Medical Association's Declaration of Helsinki. To recruit participants, among the senior welfare facilities located in Incheon, South Korea, 10 facilities with similar geographical and social environments were selected through convenience sampling. The purpose and methods of the study were thoroughly explained to adults aged 65 years or older who visited the selected facilities. Based on the findings of previous studies [25; 26], older adults with one or more of the following diseases or disorders associated with decreased sensorimotor function in the orofacial muscles or the development of dysphagia were excluded from the study: dysphagia, cerebrovascular diseases (e.g., stroke), neurasthenia, neurological diseases (e.g., Parkinson's disease), chronic obstructive pulmonary diseases (e.g., chronic bronchitis), gastrointestinal disorders, amyotrophic lateral sclerosis, esophageal inflammation (e.g., reflux esophagitis), cancer, and depression. Furthermore, two preliminary tests were conducted to assess the eligibility of 90 older adults who were initially willing to participate. First, cognitive status was assessed using the Mini-Mental State Examination--Korean version (MMSE-K), and those with cognitive impairment (score \(<\) 24) (\(n\) = 9) were excluded [31; 32]. The MMSE-K, standardized in Korean by [PERSON] and [PERSON] [32], is one of the most widely used scales in Korea, as it can be easily administered in a short time and its reliability and validity have been proven for screening moderate-to-severe dementia [31; 33; 34]. Next, the Korean Activities of Daily Living scale developed by [PERSON] et al. [35], a valid and reliable instrument for the functional assessment of older adults, was employed to assess the independence of the participants in daily living. All participants responded that they could perfectly perform the basic activities of daily living in all seven items without any assistance. After informed consent was obtained from the 81 participants who fully understood the research process and voluntarily agreed to participate, a survey and several clinical assessments were carried out. Data were collected from 30 April 2024 to 31 May 2024. After collecting the survey data from all participants, questionnaires with at least one missing value (\(n\) = 11) were excluded, and a total of 70 participants were finally analyzed in the study. Using G* Power 3.1.9.4 software (Heinrich-Heine-University Dusseldorf, Dusseldorf, Germany) with a significance level of 0.05, power of 80%, effect size of 0.20, and five predictors [36], the minimum required sample size was calculated to be 70. The flowchart of the participant recruitment process is presented in Figure 1.
# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 2 Preliminaries
### CycleGAN Networks  
The CycleGAN Network is designed to capture special characteristics of one image collection and to figure out how these could be translated into another image collection in the absence of any supervisor, i.e., paired training examples, as finding those is difficult, as well as expensive in the sense of labelling effort that has to be employed simultaneously over two different domains.  
In [17], [PERSON] et al. have proposed invoking the \"_cycle consistency_\" loss in the overall loss function, by designing two domain translators or mappers \(G\) and \(F\), in the mutually opposite domain-wise directions. Namely, by learning:  
\[G:\mathcal{X}\rightarrow\mathcal{Y}, \tag{2}\] \[F:\mathcal{Y}\rightarrow\mathcal{X}, \tag{3}\]  
in the form of GAN networks, and by encouraging both mappings given by Equations (2) and (3) and illustrated in Figure 1, to be as \"close\" as possible to bijection, i.e., by making:  
\[F\big{(}G(x)\big{)} \approx x, \tag{4}\] \[G\big{(}F(y)\big{)} \approx y, \tag{5}\]  
it was shown to be possible to compensate the lack of paired data samples, and effectively learn the complex nonlinear transformations in a fully unsupervised manner.  
Therefore, if we denote the generator networks implementing mappings \(G\) and \(F\) by \(G_{X\to Y}\) and \(G_{Y\to X}\), respectively, and denote the discriminator networks corresponding todomains \(\mathcal{X}\) and \(\mathcal{Y}\), i.e., true distributions \(p_{X}(x)\) and \(p_{Y}(y)\), by \(D_{X}\) and \(D_{Y}\), respectively, from Equation (1) we get the following adversarial optimization objectives:  
\[\mathcal{L}_{adv}(G_{X\to Y},D_{Y}) =\mathbb{E}_{y\sim p_{Y}(y)}\big{[}\ln D_{Y}(y)\big{]}+\mathbb{E}_ {x\sim p_{X}(x)}\big{[}\ln(1-D_{Y}(G_{X\to Y}(x))\,)\big{]} \tag{6}\] \[\mathcal{L}_{adv}(G_{Y\to X},D_{X}) =\mathbb{E}_{x\sim p_{X}(x)}\big{[}\ln D_{X}(x)\big{]}+\mathbb{E}_ {y\sim p_{Y}(y)}\big{[}\ln(1-D_{X}(G_{Y\to X}(y))\,)\big{]} \tag{7}\]  
as well as the cycle-consistency objective:  
\[\mathcal{L}_{cyc}(G_{X\to Y},G_{Y\to X}) = \mathbb{E}_{x\sim p_{X}(x)}\big{[}\,\big{\|}G_{Y\to X}(G_{X\to Y}(x) )-x\big{\|}\,\big{]} \tag{8}\] \[+\,\mathbb{E}_{y\sim p_{Y}(y)}\big{[}\,\big{\|}G_{X\to Y}(G_{Y\to X }(y))-y\big{\|}\,\big{]} \tag{9}\]  
making the full optimization objective given by:  
\[\mathcal{L}(G_{X\to Y},G_{Y\to X},D_{X},D_{Y}) = \mathcal{L}_{adv}(G_{X\to Y},D_{Y})+\mathcal{L}_{adv}(G_{Y\to X},D_{X}) \tag{10}\] \[+\,\lambda_{cyc}\,\mathcal{L}_{cyc}(G_{X\to Y},G_{Y\to X})\]  
the one being optimized during the unsupervised training of the model shown in the left part of Figure 1. Effect of the enforced cycle-consistency during the training (learning) is regulated by the penalty parameter \(\lambda_{cyc}\).
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.5 Class B: Synchronized Reception Window  
Class B device specification aims to solve limited DL message in Class A by using a synchronized reception slot. In Class B, GWs transmit a broadcast message to the nodes a timing reference. End-nodes use this reference to open the reception window which is called \"ping slots\" to initiate DL reception process.  
All nodes in the network have to implement Class A and start the network with the same joining process. If an end-device decides to operate as Class B devices it has to follow these steps:  
* The node makes a request to operate as Class B mode and searches for a beacon. The beacon can be found \(BEACON\_LOCKED\) or not \(BEACON\_NOT\_FOUND\) with the help of BeaconTimingReq message.
* The node selects an appropriate ping slot data rate and slot period depending on the signal strength and battery level.
* Class B mode is controlled over FCTRL field of every UL frame transmitted by end-device.
* End-device periodically reports its location and DL route to the network server.
* If no beacon frame received for given time [7], devices fall back to Class A device.  
Figure 16: LoRaWAN NewChannelAns message frame format.  
Figure 14: LoRaWAN NewChannelReq message frame format.  
Figure 15: LoRaWAN NewChannelReq data range frame format.  
Beacon periods and timing references are briefly presented in Figure 17. Class B MAC commands are described in Table 6.
## 3 Results
### Access to Educational Centers
#### 3.2.2 Access to Public Middle Schools  
Middle school students' ages usually vary between 11 and 13 years old. Therefore, in many cases, they are still too young to commute fully by themselves to schools. Hence, their commuting patterns are similar to primary school students, where their parents usually drop them off at school, with a car or with public transport, or they arrive by school bus.  
Figure 14 shows the five public middle schools located within the Stanford District. These are located in the eastern and western areas of the polygon. Only 20.25% of the block groups have a high level of accessibility with the remaining 79.75% a medium level (see Table 10).  
Figure 12: Stanford District average travel time to the 3 closest public elementary schools by foot.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accessibility Level** &
\begin{tabular}{c} **Stanford District** \\ **(Block Groups)** \\ \end{tabular} & **Distrito Tec (AGEBs)** \\ \hline Low (\(>\)30) & 33/79–41.77\% & 0/11–0\% \\ Medium (16–30) & 46/79–58.23\% & 3/11–27.27\% \\ High (0–15) & 0/79–0\% & 8/11–72.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accessibility level to elementary schools traveling by foot per aggregation area.  
Stanford District clearly has a reduced density of middle schools and, consequently, a lower level of accessibility than Distrito Tec. Once again, before suggesting any interventions, it is necessary to further understand the conditions of both study areas to determine if the demand for such destinations exists. In the case that it does, the destinations should be located in the vicinity of the areas that have a considerable population of children between 11 and 13 years old.  
Children between 11 and 13 years old are able to use a bike to commute, but for parents to allow them to do so, street and city safety must be ensured. If the city does not align with people's perception of safety, other sustainable transport alternatives can be promoted such as school buses.
# _Vaccinium_ Species (Ericaceae): From Chemical Composition to Bio-Functional Activities
## 5 Biological Properties of _Vaccinium_ Species
### Vaccinium and Endothelial Dysfunction  
Endothelial dysfunction is an early predictor of cardiovascular diseases, and it is well known that oxidative stress and low grade of inflammation contributes to endothelial cell activation, priming it for adhesion, infiltration, and immune cell activation [126].  
In this context, data from the literature indicate that _Vaccinium_ extracts and derivatives may prevent or delay cardiovascular diseases due to their capability to revert endothelial dysfunction. Very recently, [PERSON] et al. [127] showed that one cup of blueberries/day, for six months, promotes 12-15% reductions in cardiovascular disease risk, demonstrating that higher intakes of blueberries improve markers of vascular function and ameliorate lipidstatus. Similarly, the intake of blueberry acutely improved peripheral arterial dysfunction in smoker and in non-smoker subjects [128, 129], improved endothelial function over six weeks in subjects with metabolic syndrome [130], and improved endothelium-dependent vasodilation in hypercholesterol individuals through the induction of the NO-cGMP signaling pathway [131].  
In animal models, blueberry anthocyanin-enriched extracts were shown to be able to increase Bcl-2 protein expression, as well as to decrease interleukin 6, malondialdehydedehyde, endothelin 1, and angiotensin II levels and to reduce Bax protein expression after rat exposure to fine particulate matter [132]. Blueberry consumption was also able to protect endothelial function in obese Zucker rats, through the attenuation of local inflammation in perivascular adipose tissue (PVAT) [133]. In diabetic rats, the _Vaccinium_ treatment decreased markers of diabetic retinopathy, such as retinal VEGF expression and degradation of zonula occludens-1, occludin and claudin-5 [134]. Finally, in experiments of hypoperfusion-reperfusion in rats, the administration of the extract of _Vaccinium myrtillus_ protected pial microcirculation by preventing vasoconstriction, microvascular permeability, and leukocyte adhesion [135].  
The endothelium protective role of _Vaccinium_ has also been reported in in vitro experimental models. Human aortic endothelial cell (HAECs) treated with palmitate exhibited elevated ROS levels, and increased expression of several markers of endothelial dysfunction including NOX4, chemokines, adhesion molecules, and IxB\(\alpha\).  
The effects of palmitate were ameliorated in HAECs previously treated with blueberry metabolites [136]. In human umbilical vein endothelial cells (HUVEC), pterostilbene, an active constituent of blueberries, is able to induce a concentration-dependent nitric oxide release via endothelial nitric oxide synthase (eNOS) phosphorylation, mediated by activation of the PI3K/Akt signaling pathway [137]. Similarly, blueberry anthocyanins protect endothelial cells from oxidative deterioration by decreasing the levels of ROS and Xanthine Oxidase -1 (XO-1) and increasing the levels of superoxide dismutase and HO-1 [138].
# Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove
## 4 Results and Discussion
### Performance Test of the Glove Prototype  
#### 4.2.1 Wi-Fi Data-Transmission Circuit  
The ESP32 development board was set to AP mode. The maximum transmission speed of the board was 150 M/S; however, in the AP mode, the maximum transmission speed was 2 MB/s. The analog-to-digital conversion converted the actual voltage into a digital quantity within 0-4096. Each datum occupied 2 B, so theoretically, 1,000,000 digital data could be read in one second. However, actual testing indicated that one datum per second could form a very smooth curve between the striking force and time, which also improved the data preservation. To test the performance and portability of the portable smart glove, a professional boxing coach was asked to test the prototype in the boxing gym of Hunan University of Arts and Sciences. The results of the on-site test are shown in Figure 14.  
Figure 13: (**a**) Changes in the cyclic tensile resistance with time under 10% strain; (**b**) changes in the cyclic tensile resistance with time under 70% strain; (**c**) changes in the sensitivity with strain; (**d**) 70% strain-extension cycle test results.  
For easy portability, two 3.7 V lithium batteries were used in series to supply power to the development board. After testing for approximately 1 h, the power supply voltage dropped below 5 V. At this time, the measurement and data transmission circuit stopped working. After 20 min of recharge, the lithium battery continued to work. Thus, the smart boxing glove developed in this article had a battery life of 1 h and a charging time of 20 min.
## 4 Use Cases Analysis  
AI for Network scenarios, employing AI technology to enhance network performance, have been extensively researched in both industry and academia. This chapter concentrates on Network for AI solutions, examining three representative use cases, wearable devices, embodied intelligence, and generative AI, to demonstrate how AI-driven networks facilitate smarter, more secure, and efficient communication and AI services through AI collaboration across the cloud, the edge, and devices. Wearable devices address human health and entertainment needs, and embodied intelligence extends AI applications to robotic systems. Meanwhile, generative AI has the potential to introduce new capabilities and revenue models for future networks.
###### Abstract  
The by-products obtained after the extraction of vitamin B\({}_{2}\) from fermented soybean (VBP), primarily consisting of soybean meal, soybean oil, vitamin B\({}_{2}\), and _Bacillus subtilis_, may serve as a cost-effective and recycled resource in animal feed. This research aimed to assess the impact of VBP on the growth performance, nutrient utilization, blood parameters, and meat quality of pigs in the growing-finishing phase. In this 16-week feeding experiment, 140 pigs of mixed breed [Duroc \(\times\) (Landrace \(\times\) Yorkshire)] were used, with an initial average body weight of 26.05 \(\pm\) 1.32 kg. The pigs were assigned randomly to one of four dietary groups, each consisting of five pigs, with two gills and three barrows per pen, and a total of seven repetitions per treatment. The animals were fed different levels of by-products after vitamin B\({}_{2}\) extraction (0, 10, 20, and 30 g/kg as the CON, VBP10, VBP20, and VBP30 dietary groups, respectively) in a three-phase feeding trial (wk. 0-4, 4-8, and 8-13). When higher levels of VBP were added as supplements, the outcomes indicated that there were no notable variations (\(p>0.05\)) in growth performance and nutrient digestibility among the treatment groups throughout the trial. In phase II (4-8 weeks), the inclusion of VBP in the diets showed a reduction (\(p=0.011\); with linear and quadratic effects, \(p=0.003\)) in serum norepinephrine concentration. The sensory evaluation of meat color was linearly improved (\(p=0.043\)) in pigs fed with graded levels of VBP supplementation. The percentage of lean meat showed a significant improvement (\(p=0.016\)) with the gradual levels of VBP supplementation. The addition of 30 g/kg (on an as-fed basis) of vitamin B2 by-products in the diet can simultaneously mitigate the negative impacts of acute stress without compromising growth performance and enhance the percentage of lean meat. These findings suggest that VBP could be a viable and economical alternative to corn and soybean meal and partially replace it in pig feed.  
vicamin B\({}_{2}\); by-product; finishing pig; meat quality +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Influence of Ultrasound on Extraction  
Ultrasound is frequently used to extract bioactive substances from various types of samples or to improve the extraction efficiency. Using the MSM-UAE (described in Section 2.7.3, the influence of ultrasound on total extraction time and lutein extraction was examined. Using direct ultrasound, the extraction and saponification time was reduced from 2.5 h (MSM) to 25 min (MSM-UAE). In addition to reducing the time of extraction, the effect of ultrasound on lutein extraction efficiency was examined. During direct ultrasound extraction, the average temperature reached was 50 \({}^{\circ}\)C, and the determined concentration of lutein was 17.728 mg/kg feed mixture, which is 8.66% lower than the concentration determined using the MSM. The resulting chromatograms are presented in Figure 4. The lower lutein concentration obtained with the MSM-UAE method suggests that the shorter waiting time after sonication may be a contributing factor. The waiting time after extraction and saponification is 2 h for MSM, whereas it is only 10 min for the MSM-UAE. Nevertheless, the results obtained in our research leave ample room for further investigation of the method. [PERSON] et al. investigated the influence of indirect and direct ultrasound on the extraction of lutein from Australian Sweet Lupin Flour, and they determined that the use of indirect ultrasound gave better results than direct ultrasound [34]. [PERSON] et al. used direct ultrasound for the extraction of lutein from the peel of Citrus reticulate. They observed that the lutein yield increased up to 44 \({}^{\circ}\)C, then decreased with further increases in temperature [44]. [PERSON] et al. also used direct ultrasound for extraction of lutein from corn with ethanol as the solvent. In their study, a longer period of sonication yielded a higher concentration of lutein. The temperature during sonication was 38-40 \({}^{\circ}\)C [45]. [PERSON] et al. used indirect ultrasound and ethanol as the solvent for extraction of lutein from corn gluten meal. Extraction was conducted for 40 min at a temperature of 60 \({}^{\circ}\)C [20]. [PERSON] et al. extracted lutein from pistachio waste with indirect ultrasound during 45 min, using ethyl acetate as the solvent at a temperature of 50 \({}^{\circ}\)C [46]. [PERSON] et al. used a 1% surfactant aqueous solution as the solvent and ultrasound-assisted extraction to obtain lutein from marigold flowers, with an optimal temperature of 35 \({}^{\circ}\)C [47].  
Figure 4: Comparison of lutein content obtained by MSM-UAE (—) and MSM (—).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Sample** & **Lutein (mg/L)** \\ \hline \multirow{2}{*}{theoretically} & S1 + standard & 0.46521 \\ \cline{2-3}  & S2 + standard & 0.47900 \\ \hline \multirow{2}{*}{recovery (\%)} & S1 + standard & 95.68 \\ \cline{2-3}  & S2 + standard & 98.38 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._