esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Proximate Composition  
The proximate composition (moisture, ash, total dietary fiber, protein, fat, and carbohydrates) was significantly affected by the part of the plant under study (Table 1), except energy.  
Regarding moisture, the stems had the highest value in both the fresh and freeze-dried samples, with values of 11.10 and 90.95%, respectively. These values are far higher than those reported by [PERSON] et al. (2019), 60.28%, in similar plants such as _Oldenlandia corymboa_. However, in most of the previous studies, the moisture varied in the range of 80-95% moisture ([PERSON] et al., 2019; [PERSON] et al., 2020).  
The ash content showed a completely different behavior from that of moisture, with leaves showing the highest value (12.93%) as compared to flowers (8.27%) and stems (3.15%). The ash content found in the _Oxalis pes-caprae_ leaves is significantly higher than those reported in _Oxalis corymboa_ (3.91%) by [PERSON], [PERSON], and [PERSON] (2019). Ash values obtained in _Oxalis pes-caprae_ flowers were similar to those reported by [PERSON] et al. (2019) in _Oldenlandia corymboa_ (8.34%). However, a study conducted in Pakistan shows much higher ash results (28.00%) therefore the geographical influence on the results is important ([PERSON] et al., 2020).  
Regarding the total dietary fiber, the highest content was obtained in the stems (36.36%) of _Oxalis pes-caprae_ followed by the flowers (30.68%) and leaves (28.72%). Current results highlight the fact that _Oxalis pes-caprae_ has higher fiber content than other similar plants, such as _Oxalis tuberosa_ (0.87-1.69%) and _Oldenlandia corymbosa_ (7.26%) [29; 30].  
On the other hand, for both protein and fat content, _Oxalis pes-caprae_ leaves showed the highest content, while stems showed the lowest content for both variables. Very similar protein contents were obtained for _Oldenlandia corymbosa_ (10.52%) [29]. Another study on _Oxalis corniculata_ leaves reported a total nitrogen content of 3.56%, which is equivalent to 22.25% [31] after the application of a 6.25 conversion factor [20]; thus, this value is close to that found in the leaves of _Oxalis pes-caprae_. Regarding fat, the leaves had 12.68%, while the stems only had 3.53%. These fat values were lower than those found previously reported in _Oxalis corniculata_, 23.75% [31], although in this study, the same plants collected in different areas had protein contents ranging between 13.4-17.6%. The percentage of carbohydrates in the parts of _Oxalis pes-caprae_ indicated a higher value in the stems (73.34%), followed by flowers (62.57%) and leaves (45.87%); these values were higher than those found by [PERSON] et al. (2010) in _Oxalis corniculate_ (24.67%) but similar to those previously reported in plants of other genera such as Malvaccae and Lamiaceae, 75-85% [31; 32; 33]. Finally, the total energy contents were equivalent in the three parts of the _Oxalis pes-caprae_ plant. These values are quite high as those previously reported for plants of similar genera, such as Fabaceae, Malvaccae, and Lamiaceae, ~1548-1694 kJ [32; 33; 34].
## 3 Results and Discussion
### Comparison of Sensory Characteristics of Red, White, and Black Currants  
As anticipated, comparisons between the red, white, and black variants revealed differences in sensory properties, particularly between black currants (\(R\). _nigrum_) and red/white currants (\(R\). _rubrum_). However, these differences were statistically significant only for astringency (\(p<0.05\)), which was significantly higher in black currants, and for the characteristic \"blackcurrant\" note (\(p<0.05\)). Differences in appearance and color were also observed, but these results are not included as the study focused on sensory characteristics. Texture, primarily determined by firmness, and sweetness are known to significantly influence currant acceptability. Therefore, firmness/crispiness was also evaluated. Allsamples exhibited acceptable texture, with red/white currants demonstrating greater firmness and crispiness than black currants. All samples possessed a pleasant, mildly acidic, sweet flavor. Consistent with [PERSON] and [PERSON] [2], white currants were perceived as sweeter and received higher ratings, while red currants were perceived as more acidic. Acidity, attributed to organic acids, is a key determinant of red currant flavor. The flavor of black currants was somewhat diminished by their distinct, relatively strong aroma, which was not favorably perceived by the panelists. However, it is important to note that the observed differences between red, white, and black currants varied slightly across the two years of the study. The overall acceptability of individual cultivars fell within a relatively narrow range (44.3-72.1%), corresponding to medium to high acceptability. Black currants received slightly lower ratings. These results suggest that panelists did not exhibit a strong preference for any particular type/variant of currant, despite considerable differences in their properties (color, flavor, and odor). Astringency and the \"blackcurrant\" note can be considered the primary differentiating factors between red/white and black currants.
## 2 Materials and Methods
### Volatile Identification and Quantification  
Volatiles were analyzed as described before [PERSON] et al. (2020); [PERSON] et al. (2020). Briefly, 1 mL of sample was injected into a capillary column (RXi-5 SiMS column, 30 m \(\times\) 0.25 mm \(\times\) 0.25 mm) in a splitless mode for GC-MS and GC-FID analysis. The oven temperature program was initiated at 50 \({}^{\circ}\)C for 2 min, raised 5 \({}^{\circ}\)C min\({}^{-1}\) to 180 \({}^{\circ}\)C, and held 2 min; then raised 10 \({}^{\circ}\)C min\({}^{-1}\) to 230 \({}^{\circ}\)C, and held 5 min before returning to 50 \({}^{\circ}\)C for the next sample. Injector, ion source, interface, and FID temperatures were 250, 230, 270, and 230 \({}^{\circ}\)C, respectively. The flow rates of hydrogen, nitrogen, and zero air were 40, 30, and 400 mL min\({}^{-1}\), respectively, and helium was used as the carrier gas with a flow rate of 1.0 and 1.7 mL min\({}^{-1}\) for GC-MS and GC-FID, respectively. Zero air is a highly purified air in which almost all the hydrocarbon is removed. The electron ion source was set at 70 eV, and the chromatograms were recorded by monitoring the total ion current in the mass range of 45-600 m/z. Since each compound has its own ionization and fragmentation in the MS detector [PERSON] et al. (2020), thus, various volatiles cannot be reliably quantified by comparing with a single internal standard. In this study, MS data was used for volatile identification by comparing MS spectral with the NIST14 database, and further confirmed by their reported retention index; volatiles were quantified from FID data by normalizing to the internal standard peak area. To calibrate the FID response differences for individual volatiles, FID response factors relative to the internal standard were calculated and used for further normalization (Supplementary Table S1). The fresh leaf water contents were determined by the National Standard of P. R. China (GB/T 5009-2016), then used to convert fresh leaf weight into dry mass. The volatile contents were normalized to dry mass. To convert the retention time into the retention index, n-alkanes (C9-C25) were analyzed under the same condition, and the data was used to convert the retention time into the retention index (RI) by the formula: RI = 100 \(\times\) [(tR \(-\) tRz)/(tR(z + 1) \(-\) tRz) + z]. Where z is the carbon number of the alkane standard, tRz and tR(z + 1) represent the retention times of z and z + 1 alkane standards, and tR is the retention time of tea volatile that falls between tRz and tR(z + 1).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Inter-Cell Interference Mitigation Techniques
#### 4.3.2 Coordinated Scheduling and Beamforming  
COS enables neighboring BSs to exchange control signaling and schedule non-overlapping transmissions [187]. Sharing BF vectors between cells can also suppress interference from adjacent sectors by steering beams away from vulnerable users.
## 4 Discussion
### Clinical Significance  
Integrating treadmill-based gait analysis across various disciplines such as physical therapy, sports science, orthotics, and footwear design, provides a comprehensive approach to understanding and managing hyperpromation, leading to improved mobility, performance, and quality of life for affected individuals. Within physical therapy contexts, instrumented treadmill-based gait analysis may enable physical therapists to identify individuals with hyperpromated feet and monitor their progress through personalized rehabilitation programs allowing for adjustments in therapy to ensure optimal outcomes. These interventions often include strengthening and flexibility exercise programs targeting the foot and leg muscles to support the arches and prevent tightness in the Achilles tendon [81], as well as gait retraining programs that provide real-time feedback during walking assessments, enabling precise monitoring and adjustment of gait patterns [82]. The use of instrumented treadmills in rehabilitation settings may also facilitate comprehensive gait analysis and monitoring in individuals with hyperpromated foot types during prolonged walking sessions. Unlike short-duration assessments, extended walking induces ground reaction forces that generate external moments on lower limb joints, necessitating internal moments from muscle activation to maintain joint stability [83, 84]. Prolonged walking may alter these dynamics, affecting muscle activation patterns and the alignment of lower extremity joints, pelvis, and spine, potentially influencing gait parameters, and contributing to joint pathology [11].  
Sports scientists and trainers can benefit from the controlled environment provided by treadmill-based gait analysis to personalize training programs aimed at enhancing athletic performance and preventing injuries associated with hyperpromation, such as shin splints and knee pain. This approach involves selecting appropriate footwear and designing exercises to correct biomechanical imbalances, thereby maximizing training outcomes [85].  
Gait analysis techniques, such as plantar pressure mapping, enable also specialists to assess the effectiveness of custom or prefabricated orthotics designed to provide necessary arch support, correct foot positioning, and alleviate discomfort associated with hyperpromation. This personalized assessment facilitates the adjustment of orthotic devices to meet each patient's unique needs, enhancing mobility and reducing pain while ensuring optimal support and comfort. Furthermore, analyzing the interaction between hyperpromated feet and various shoe designs under dynamic conditions enables footwear designers to optimize features that provide enhanced support and stability, such as appropriate arch support, effectively managing hyperpromation [86, 87].
# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
## 2 Human-Voice Interface System
### Speech Pre-Processing  
Even if a microphone with a specified pattern is used, the sound signal may still be mixed with background sounds and noises; however, voice recognition only requires segments of human speech. If there is noise or background sound, the accuracy of the recognition will be greatly affected. To remove background sounds, noise, and silent signals for the subsequent ASR, this pre-processing step used voice activity detection (VAD)/speech activity detection (SAD). VAD is mainly used to detect the presence of speech sounds in the current incoming voice signal, and its classical design consists of three steps: first, the sound signal goes through a noise reduction process, such as spectral subtraction [18]; then, it undergoes feature extraction and classifier, which are the focus of VAD.  
In this paper, we used the VAD algorithm in WebRTC, which is a multi-feature integrated evaluation that employs the Gaussian mixture model (GMM) [19]. The GMM solves the clustering problem of the samples by estimating the probability density distribution of the sample; thus, it is suitable for VAD:  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-x_{i})^{2}}{2\sigma^{2}}} \tag{1}\]  
\[f(x_{k}|Z,r_{k})=\frac{1}{\sqrt{2\pi}}e^{-\frac{(x_{k}-x_{k})^{2}}{2\sigma^{2}}} \tag{2}\]  
Gaussian distribution is also known as a normal distribution. If the random variable X serves a Gaussian distribution with a location denominator \(\mu\) and a scale denominator \(\sigma\) then its probability density function can be expressed as Equation (1). The Gaussian distribution in WebRTC is expressed as Equation (2), in which \(x_{k}\) is the selected feature vector that is the energy feature of the six sub-bands just mentioned; and \(r_{k}\) is the combination of the mean \(\mu_{z}\) and variance \(\sigma\) parameters, which determine the probability of the Gaussian distribution. \(Z=1\) calculates the probability of speech, \(Z=0\) calculates the probability of noise, and the final classifier uses the likelihood ratio test. If the likelihood ratio is greater than the threshold value, it is considered as speech, which is then output to the speaker separation system.
# Roles of D-Amino Acids on the Bioactivity of Host Defense Peptides
## 5 Mechanisms of Bioactivities
### Antiviral Activity  
Membrane destabilization by D-AA-containing HDPs has been observed for all forms of cellular pathogens, namely bacteria [1], cancer [13], fungi [3] and parasites [4]. An exception to this general HDP mechanism of action is of course the mechanism for viruses because this pathogen class does not possess cell membranes. Existing results show that HDPs could exhibit strong inhibitory activity at the viral attachment, entry and notably at the post-entry stages, which is a major challenge in antiviral drug design [5]. Unlike the general mechanism of membrane destabilization against virtually all types of cellular pathogens, the mechanism of antiviral HDPs appear to be less uniform. These HDPs were observed to interfere with the viral reproductive cycle [5], to directly inactive viruses via disruption of viral envelopes (i.e., at least for viruses that have envelopes) [72] and to disrupt viral morphology [5]. The natural diastereomer, gramicidin, has been shown to inactivate HIV at an effective concentration that are three orders of magnitude lower than the cytotoxicity dose [73]. Synthetic antiviral D-AA-containing HDPs (i.e., diastereomer and all-d enantiomer) have also been tested. However, no noteworthy performance difference was observed for the synthetic D-AA analogues in vitro [74; 75], which may suggest that the antiviral mechanism is considerably different from the anti-cellular mechanism. The enhanced in vivo performance of the d-analogues could be explained by the increased serum stability rather than the difference in viral inactivation. As of now, there seems to be little if any investigation on the functional mechanism of d-AAs in antiviral HDPs.
# A Classification Model Based on Interval Rule Inference Network with Interpretability
## 4 Interval Rule Inference Network
### IBRB Establishment
#### 4.1.1 Data Transformation  
In the interval value conversion, numerical values are converted into interval values. This process ensures the equivalence and rationality of the information before and after the data conversion.  
Assume the \(i\)th sample in the dataset is \(x_{i}=\{x_{1,i},\ldots,x_{t,i},\ldots,x_{T,j};y_{i}\}\), in which \(x_{t,i}\) is general numerical representation, \(T\) denotes the total number of the attribute, \(y_{i}\) represents the category of the \(i\)th sample, and \(y_{i}\in\{y_{1},\ldots,y_{I_{D}}\}\), where \(J_{D}\) indicates the total number of categories. The method for converting a numerical value into an interval value \(x_{t,i}\in[x_{t,i}^{-},x_{t,i}^{+}]\) is as follows:  
Figure 1: IRIN network framework.  
\[\left\{\begin{array}{ll}x_{t,i}^{-}=x_{t,i}-\text{rand}()\sigma_{t,j}\lambda,\\ x_{t,i}^{+}=x_{t,i}+\text{rand}()\sigma_{t,j}\lambda,\end{array}\right.\quad \text{if }y_{i}=y_{j} \tag{16}\]  
where \(\lambda\) is the step size, and \(\sigma_{t,j}\) is the standard deviation of \(x_{t}\)in the same class \(y_{j}\).  
\[\sigma_{t,j}=\sqrt{\frac{1}{|y_{i}|-1}\sum_{x_{t,i}\in y_{j}}\left(x_{t,i}- \bar{x}_{t,j}\right)^{2}} \tag{17}\]  
with  
\[\bar{x}_{t,j}=\frac{1}{|y_{J_{D}}|}\sum_{x_{t,i}\in y_{j}}x_{t,i} \tag{18}\]  
The category of the sample should be converted to interval values \([y_{i}^{-},y_{i}^{+}]\) as follows:  
\[\left\{\begin{array}{ll}y_{i}^{-}=y_{i}-\frac{1}{2}\gamma\\ y_{i}^{+}=y_{i}+\frac{1}{2}\gamma\end{array}\right. \tag{19}\]  
where \(\gamma\) is random number and \(\gamma\in[0,1]\).  
A given input \(x_{t}\) is transformed into an interval belief distribution of the antecedent attribute \(U_{t}\) as follows:  
\[E(x_{t})=\left\{\left(A_{tj},[a_{t,j}^{-},a_{t,j}^{+}]\right)|,j=1,\ldots,J \right\},t=1,\ldots,T \tag{20}\]  
where \(A_{tj}\) is the \(j\)th reference value of the \(t\)th antecedent attribute, and \([a_{t,j}^{-},a_{t,j}^{+}]\) is the interval belief degree to which the input \(x_{t}\) belongs to the referential value \(A_{tj}\). \([a_{t,j}^{-},a_{t,j}^{+}]\) will be obtained in different cases from Equations (3) and (4) according to the nature of the antecedent attribute.  
The category of samples can be expressed as the interval belief distribution representation. Assuming that the sample belongs to the \(i\)th category, then it can be expressed as  
\[\left\{(D_{1},b_{1}),\ldots,(D_{J_{D}},b_{J_{D}})\right\} \tag{21}\]  
\[\text{where }b_{j}=[b_{j}^{-},b_{j}^{+}],b_{j}^{-}=b_{j}^{+}=\left\{\begin{array} []{ll}1,j\in i\\ 0,j\
otin i\end{array}\right.\]
## 2 Results  
### Optimization of Solid-Phase Microextraction (SPME) Parameters  
#### 2.1.1 Selection of Fiber Coating  
In the first stage of experiments, the selection of fiber coating was performed to obtain the best signals for tested VSCs. Five different coatings were evaluated: 100 \(\upmu\)m PDMS, 60 \(\upmu\)m PEG, 85 \(\upmu\)m PA, 85 \(\upmu\)m CAR-PDMS, and 50/30 \(\upmu\)m DVB/CAR/PDMS. The most important parameters considered during selection of fiber are the thickness of the film and its polarity. Fibers covered with thicker film require a longer time to achieve extraction equilibrium but may provide higher sensitivity because of the greater mass of extracted analytes. Moreover, it is recognized that volatile compounds require a thick coating, and for semi-volatile compounds, a thin coating is more effective [20]. According to the literature, the mostly commonly used fibers for extraction of VSCs are CAR/PDMS [4; 21; 22] and DVB/CAR/PDMS [2; 26; 27], but other coatings were also tested. The ethanolic solution (5% \(\eta\)/\(\upsilon\)) of stock standard mixture in the total volume of 10 mL in 20 mL vials was preincubated at 50 \({}^{\circ}\)C for 15 min and then extraction took place at the same temperature for 15 min. After the GC-MS analysis, the total peak area as well as number of identified peaks were calculated, and the results are presentedin Figure 1. The highest peak areas were obtained for both CAR/PDMS and DVB/CAR/PDMS fibers; however, we were not able to identify ethanethiol and dimethyl sulfide using the CAR/PDMS fiber, as these components were covered by ethanol peak. Therefore, the DVB/CAR/PDMS fiber was selected for further experiments. Interestingly, regardless of the fiber coating used for analysis, the presence of 2-(methylthio)ethanol, 3-(methylthio)propionaldehyde, and 3-(methylthio)-1-propanol was not established, although these compounds were present when direct injection of the standard mixture was performed (data not shown). This may be caused by the low affinity of these compounds for fiber coatings or their degradation under the conditions used; however, further work is needed.
## 4 Conclusions  
This paper addresses the contribution of waste glass as a partial substitution for ordinary Portland cement (OPC) in mortar and concrete prepared with water binder ratios of 0.40 and 0.42, respectively. The characteristics of the setting time, workability  
Figure 16: Residual strength of glass blended concrete exposed to elevated temperature.  
Figure 17: Residual density of glass blended concrete exposed to elevated temperature.  
and compressive strength and thermal resistance (550 \({}^{\circ}\)C) were observed in concrete and mortar, while model equations predicting the density and compressive strength were also developed. The following are the conclusions:  
1. Glass reduced the setting time and enhanced the workability of both glass blended mortar and concrete.
2. Presence of glass caused an interfacial transition zone within the binder matrix, and this led to weak microstructural stability that prevented early strength development.
3. Cement content, glass content and age constituted key elements in the models predicting the strength and density of mortar/concrete.
4. Presence of glass affected the vibrational frequencies of hydroxyl (-OH), C=O, water molecules, Si-O and S-O, as observed in the Fourier infrared spectroscopy results.
5. The maximum 28-day compressive strengths in mortar and concrete were 33 MPa and 37 MPa, prepared at water/binder ratios of 0.4 and 0.42 with the optimum OPC partially substituted glass content of 10 wt.% and 20 wt.%, respectively. In addition, the maximum 90-day strength achieved in concrete was 47 MPa.
6. Generally, OPC (C\({}_{100}\)G\({}_{0}\)) and glass blended concrete (C\({}_{80}\)G\({}_{20}\)) subjected to thermal exposure of 550 \({}^{\circ}\)C had strength reductions of 28.2% and 16.8%, while the loss of density was 16.9% and 4.2%, respectively.
7. Glass blended concrete could be easily used in fire-resistant structures such as kithens and incinerators to achieve better durability.  
Conceptualization, M.O.Y.; Formal analysis, A.A.A.; Funding acquisition, K.A.A.A.-S.; Investigation, M.O.Y., A.A.A. and G.D.S.A.; Methodology, M.O.Y.; Project administration, K.A.A.A.-S., A.H.A., S.M.I.S. and U.Y.Q.; Resources, K.A.A.A.-S., A.H.A. and M.M.H.A.-T.; Writing--original draft, M.O.Y.; Writing--review & editing, K.A.A.A.-S. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project No: IFP-A-2022-2-1-06.  
The authors would like to appreciate the continuous support of University of Hafar Al Batin.  
The authors declare no conflict of interest.