esa-sceva/satcom-chunk-collection huggingface dataset:
# Nutritional Analysis of Plant-Based Meat: Current Advances and Future Potential
## 4 Current and Potential Solutions to Improve Plant-Based Meat Nutrition
### Phytase Treatment to Reduce Phytate Content in Plant-Based Meat  
Phytase is the phytate-degrading enzyme found naturally in plants, animals, and microorganisms [50]. It is chemically described as myoinositol(l,2,3,4,5,6)-hexakishosphosphohydrolase. Phytase sequesters orthophosphate groups from the inositol ring of phytic acid to produce free inorganic P and break the phytate complex, thereby freeing minerals and amino acids (Figure 1) [45,46]. In food processing, phytase of microbial origin is the most stable in wide-ranging pH and thermal conditions, and much work has been done with microbial-derived phytase on an industrial scale [70]. In cereal- and legume-based foods, phytase has been used to increase the bioavailability of iron, zinc, and calcium and the quality and digestibility of amino acids [45,71,72,73,74,75].  
The effect of phytase on micronutrient absorption has been assessed in various studies. Table 6 summarizes eight studies that showed statistically significant improvements in iron absorption, ranging from 1.0- to 11.6-fold more absorption [74, 75, 76, 77, 78, 79, 80, 81]. Across the iron absorption studies included in the review, three studies specifically examined the effect of phytase on iron bioavailability in humans using phytase to degrade phytate during food processing [75, 76, 77]. Five studies used phytase as an active enzyme ingredient [74, 78, 79, 80, 81]. Regarding the type of microorganism used, one processing study used Aspergillus niger. In four of the studies that added the active enzyme to meals, a phytase from Aspergillus niger was used, while the last used endogenous phytase. Concerning zinc absorption, six studies were analyzed, all of them showing statistically significant improvements in zinc absorption (1.4- to 2.0-fold greater absorption) [82, 83, 84, 85, 86, 87]. Four used phytase derived from  
Figure 1: Phytic acid, phytate complex, and phytase enzymatic reaction representation (adapted from [46]).  
Aspergillus niger or cereals to support food processing, and two used phytase derived from Aspergillus niger as a food ingredient in fortified porridges.  
**Author Contributions:** Conceptualization, P.I.S. and P.K.; writing--original draft, P.I.S. and P.K.; writing--review and editing, P.I.S. and P.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the philanthropic organization Children's Investment Fund Foundation.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflicts of interest.
## 2 Experiments
### Instrumentation  
For the fire tests, thermocouples were installed on the specimens. Their layout is presented in Figure 1. Three thermocouples were installed in Section 1-1 to monitor the temperature of the joint gap. In addition, four further thermocouples were used in Section 2-2 to measure the concrete temperature. To install the thermocouples in Section 2-2, four holes with a diameter of 10 mm were drilled 40 mm apart along the segment's width. Afterward, the holes were filled with cement paste. Owing to the joint configuration, most of the length of the joint bolts was entirely within the bolt holes and hardly experienced any direct fire exposure. Only the protruding bolt ends were exposed to the fire. To investigate the temperature histories of the joint bolts, one thermocouple was fixed to the end of one of the bolts and another was carefully placed at the same bolt's mid-point (see Figure 1a,b).  
In the loading tests, four linear variable differential transformers (LVDT) were installed on the internal segment edge to measure any joint opening and mid-span deflection during the flexure tests (see Figure 2a). The calibrated range and accuracy of the LVDTs were 100 mm and 0.01 mm, respectively. To obtain the strain profile in the CFST at different load levels, a series of strain gauges was installed on the tubes' surface. The positions and numbers of the strain gauges are presented in Figure 3.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 4 Experimental Results
### Initialization  
In the initialization phase, we take two large 128-bit prime numbers \(p\) and \(q\) validated by Miller Rabin Primality test [30] and then multiply them together to create a modulus \(n\). As shown in Key Generation Algorithm (Algorithm 1), we use RSA cryptosystem to generate \(C_{j}\)'s public key \(e_{j}\) and private key \(d_{j}\), and combine ElGamal cryptosystem to obtain secret key \(SK_{j}\) and derivative key \(DK_{j}\). Let us assume that we compute \(n=3602891806881971\) and \(g=29881307\). The parameters of \(C_{j}\) used in the initialization phase are listed in Table 1.
## 2 Materials and Method
### Notations  
We introduce the notations used in the results section to facilitate understanding of various approaches and their criteria for selecting tuning parameters. The NNG and ALASSO methods, with OLS (O), ridge (R), and lasso (L) as initial estimates and CV for selecting tuning parameters, are denoted as follows: NNG (O, CV), NNG (R, CV), NNG (L, CV), ALASSO (O, CV), ALASSO (R, CV), and ALASSO (L, CV). In these notations, the first element in the brackets indicates the choice of the initial estimate, while the second element indicates the criterion for selecting the tuning parameters.  
Additionally, the lasso, RLASSO, BE, BSS, and FS, with tuning parameters determined through CV, are denoted as Lasso (CV), RLASSO (CV), BE (CV), BSS (CV), and FS (CV), respectively, as these approaches do not require initial estimates. When AIC or BIC was used instead of CV for parameter selection, \"CV\" was replaced with \"AIC\" or \"BIC\" in the respective method notations. For example, Lasso (AIC) indicates that the AIC criterion was used to select the tuning parameter.
# Fluoride Ion Release Characteristics of Fluoride-Containing Varnishes--An In Vitro Study
## 2 Materials and Methods
### Fluoride Concentration Measurements  
The fluoride release was determined using a fluoride ion electrode (Orion 9609 BNWP, Thermo Fisher Scientific Inc., Chelmsford, MA, USA). In order to obtain reliable measurements for specimens with fluctuating ionic strength, a relative ionic strength should be set before the measurement. For this purpose, a special buffer solution, TISAB III (Total Ionic Strength Adjustment Buffer III Standardized, Thomas Scientific, Swedesboro, NJ, USA), was used as a total ionic strength adjustment buffer. TISAB III (5 mL) was diluted with 45 mL of bi-distilled water. This ensured a low pH value (5-5.5) with constant ionic strength and prevented the ions in the reagents from complexing and simulating a higher fluoride concentration. The measured fluoride value is given in parts per million. To calibrate the electrode, a two-point calibration was performed using standard solutions of sodium fluoride (2000 ppm) and diluted TISAB III solutions containing 0.4, 4, and 40 ppm. For two-point calibration, two standard solutions of the described series were chosen comprising the expected value, which was assessed by preliminary tests. The fluoride electrode was immersed in the respective standard solution and the value of the fluoride ion concentration was measured. If the measured values differed from the values of the standard solutions by more than 5%, the calibration had to be carried out again. The required slope of the fluoride electrode was given when the difference between the two measurements was between 54 and 60 mV. After calibration, the fluoride measurement was performed.
## 3 Materials and Methods
### Experimental Design  
The experimental design scheme is illustrated in Figure 3. The first stage (i) involves preparation and pre-processing of Sentinel-1 images as described in Section 3.2. The second stage (ii) pixel-based PCA image fusion, which reduces the dimensions of the multi-temporal Sentinel-1 images into only 3 bands (i.e., principal components 1, 2, and 3). The bands describe more than 70% of the information contained in the multi-temporal images (Figure 4). The selection criteria were motivated by reducing the computational demands on the system, without compromising on the accuracy of the results. The third stage (iii) entails model-level data fusion and application of SVM and Xgboost classification algorithms. The last stage (iv) is the generation of the classification map. The second stage (ii) is necessary to reduce computational demands and Random Access Memory (RAM) requirements. The third stage (iii) involves model-level data fusion using machine learning algorithms as described in Section 3.3. The data were separated into training (80%) and testing (20%) [49]. The performance and results of the algorithms in different experiments are compared using well-known evaluation metrics. This experiment was implemented using a Python programming platform on a Ryzen 9 3900, 12 cores processor at 3.8 GHz and 128 GB RAM computer.  
Figure 3: Schematic illustration of the experimental design.
## 3 Results  
### Strength  
As described above, a bar-type extrusion experiment was carried out prior to the panel formation in order to examine whether the mix using WCP resulted in a strength level that satisfied the prescribed level for extrusion concrete panels. Table 4 shows the mix for the specimen production, and the number in the ID signifies the silica powder-WCP substitution ratio in terms of weight. The results of measuring the strength for the averaged 3 specimens for each mixing are shown in Figure 8. The flexural strength of the plain specimen was found to be 18.8 MPa. The flexural strength decreased with an increased substitution ratio, and it was measured to be 12.1 MPa at a substitution ratio of 100%. However, up to 50% substitution, the flexural strength was higher than 14 MPa, which is the prescribed level for extrusion concrete panels in KS F 4735. This showed the possibility of using a mix of silica powder and WCP. Accordingly, a mix in which 50% of the silica powder was substituted with WCP was used in the panel production process to fabricate panel-type specimens that include a void section, as shown in Figure 9. The specimens were then assessed for the flexural strength by trisection loading, as shown in Figure 9. The results of measuring the strengths of the 6 specimens are shown in Figure 10. In the case of the bar-type specimen, its strength was measured to be above 16 MPa, while the strength of the panel-type specimen was slightly lower at 14.1-15.2 MPa. Nevertheless, the strengths of both types were above 14 MPa, as prescribed by KS, and based on these results, it is deemed that the desired strength may be achieved more stably by adding a fiber material that can reinforce the flexural strength.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Unit Weight (kg/m\({}^{3}\))**} \\ \cline{2-9} ID & \multicolumn{3}{c}{**Binder**} & \multicolumn{3}{c}{**Fiber**} & \multicolumn{3}{c}{**WollastoniteHPMC \({}^{*}\)**} & **Water** \\ \cline{2-9}  & **Cement** & **Silica Powder** & **WCP** & **Sepiolite** & **Pulp** & & \\ \hline Plain & 604.2 & 604.2 & 0.0 & 9.1 & 22.7 & 37.8 & 8.3 & 490.9 \\ R *25 & 595.5 & 446.6 & 148.9 & 9.0 & 22.6 & 37.6 & 8.3 & 488.5 \\ R50 & 598.4 & 299.2 & 299.2 & 9.0 & 22.4 & 37.4 & 8.2 & 486.1 \\ R75 & 595.5 & 148.9 & 446.6 & 8.9 & 22.3 & 37.2 & 8.2 & 483.9 \\ R100 & 592.7 & 0.0 & 592.7 & 8.9 & 22.2 & 37.0 & 8.1 & 481.5 \\ \hline \hline \end{tabular}  
* HPMC: Hydroxy Propyl Methyl Cellulose;
* R: WCP replacement and number is replacement ratio.  
\end{table}
Table 4: Mixture design.  
Figure 7: Compounds in C-S-H system.  
Figure 8: Flexural strength of bar type specimens.  
Figure 10: Flexural strength of panel type specimens.  
Figure 9: Shapes of panel specimens (**left**) and flexural strength testing set-up (**right**).
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 5 Conclusions  
This paper proposes an enhanced cyclic queue forwarding mechanism, TPC-CQF, to address the efficient forwarding and scheduling of TSFs in LEO satellite networks. By introducing a differentiated forwarding mechanism and dynamic priority queue scheduling, TPC-CQF effectively improves TSF transmission efficiency and quality of service. Simulation results show that compared to traditional CQF, TPC-CQF excels in reducing packet loss rates and enhancing throughput while having a minimal impact on average delay. Future research will focus on further optimizing the relationship between queue resources and network performance, with an emphasis on adaptive queue management strategies to enhance the algorithm's robustness in mixed-traffic environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite Node Count** & **TSF Packet Loss Rate** & **Average Latency (ms)** & **Throughput** & **Timeout Rate** \\ \hline
64 & 0.0002 & 2.05 & 78.2\% & 7.0\% \\
100 & 0.0008 & 2.08 & 67.8\% & 9.3\% \\
225 & 0.0036 & 2.12 & 55.6\% & 13.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics across constellation scales.  
Figure 9: Throughput.  
**Author Contributions:** Conceptualization, W.L. and N.X.; formal analysis, W.L. and B.L.; investigation, W.L.; methodology, W.L. and Y.Z.; software, W.L.; supervision, N.X. and T.L.; validation, W.L. and T.L.; visualization, W.L.; writing--original draft, W.L.; writing--review and editing, W.L. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.61801474).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 5 Results
### AWGN Channel  
Figure 4 displays the results of the NAC learning process on an AWGN channel at a static SNR level of 15 dB. The agent converged towards choosing action 5 (MCS5), resulting in the reward of approximately 15.3 Mbps. For comparison, the theoretical data rate for MCS4 and MCS5 lies at 12.0 Mbps and 18 Mbps, respectively.  
A second experiment was performed, in which the SNR of the AWGN channel increased by 1 dB after each step. At the end of each episode, the SNR was set to zero. After 500 episodes, the agent achieved an effective data rate of approximately 16 Mbps. Figure 5 shows the proposed episodical combination of MCS5, MCS6, and MCS7 with theoretical limits of 18 Mbps, 24 Mbps, and 27 Mbps, respectively. This result hints that the algorithm recognizes the benefits of applying a lower MCS index to lower SINR observations, while for sufficient channel conditions, the highest MCS index can be used to provide an increased data rate.  
Figure 4: The behavior of the NAC reinforcement learning approach on an AWGN channel at a static SNR level of 15 dB.
## 4 Discussion
### Limitations  
Limitations of the present study are our lack of biological maturity assessment as relative growth spurts may influence PA and SB. Similarly, relative growth spurts, the short duration of intervention (two months), previous knowledge of the content of the subjects, repetition of pupils, extracurricular activities related to these subjects could influence academic achievement. These could be reasons for confuse associations between PA and academic performance. A second limitation is the self-reporting nature of PA monitoring, and future investigations may wish to utilize objectively measured PA using technological advances [96,97]. Furthermore, using MET thresholds does not account for different physical performance levels of the participants. For example, a moderate MET of 4 could be detraining for one participant but approaching maximal aerobic capacity for another.  
In addition, the physical activity was measured during the October-November months in Qatar when the average outdoor temperature and humidity is 28-29 \({}^{\circ}\)C. We expected hot weather to be a barrier; therefore, more studies of longitudinal design are needed to determine seasonal variation and patterns in physical activity among school children. An additional limitation to this study is that we only collected school-time physical activity.  
This was done in order to avoid low compliance and loss of activity monitors. The compliance on valid wear-time physical activity is lower in children of this age group, and there is an additional risk of the device being lost. Finally, we recruited a convenience sample from one middle school in Qatar. Consequently, results are not necessarily valid in other ages or other countries.
## 2 Preliminaries
### K-Medoid Algorithm  
A popular medoid-based technique is the K-medoid algorithm, commonly referred to as the partitioning around medoid (PAM) algorithm [45]. This algorithm consists of three steps which are as follows:  
1. Initial medoid selection;
2. Medoid swapping;
3. Object labeling.  
The first medoid is selected at random. Then, medoids are exchanged for randomly chosen \((q-k)\) potential medoids, where \(k\) is the number of clusters, and \(q\) is the number of data points. A medoid is a location in the cluster whose differences from all other points in the cluster are at their lowest.  
The dissimilarities of the medoid (\(M_{i}\)) and data points (\(P_{i}\)) is calculated by \(|P_{i}-M_{i}|\). The cost of K-medoids is given by \(\sum\limits_{M_{i}}\sum\limits_{P_{i}\in M_{i}}|P_{i}-M_{i}|\). For more details about the K-medoids algorithm, one may refer to the work of [PERSON] and [PERSON] [46].
## 2 Theoretical Formula  
### Geometry and Material Properties  
A schematic of the sandwich plate considered, with length \(L_{a}\), width \(L_{b}\) and thickness \(h\) in a Cartesian coordinate system, is shown in Figure 1, in which \(h_{c}\) refer to the thickness of core and the thickness of top face sheet \(h_{t}\); the bottom face sheet \(h_{b}\) can be represented as \(h_{t(b)}=(h-h_{c})/2\). The top and bottom sheet of the composite sandwich plate were orthogonally embedded with pre-strain SMA wires respectively, while the core layer was an isotropic functionally gradient structure composed of graphite and epoxy resin.  
The core layer of the composite sandwich plate had a symmetrical gradient distribution form. Graphite was continuously distributed in the core layer along the \(Z\) direction from the middle plane of the sandwich plate to the interface of the top (bottom) face sheet in the form of a gradient. With a certain graphite content in the core layer, the distribution of graphite tended to the outer edge of the core layer, which is a positive gradient distribution; on the contrary, the distribution of graphite tended to the central region, which is a negative gradient distribution. Non gradient distribution refers to the uniform distribution of graphite in the core layer. The distribution function of the volume fraction of graphite in the core layer can be expressed as  
\[\text{Positive gradient}:\ V_{\text{g}}(z)=(p+1)\Big{(}\frac{2|z|}{h_{c}} \Big{)}^{p}V_{g}\,-\frac{h_{c}}{2}<z<\frac{h_{c}}{2} \tag{1}\]  
\[\text{Non gradient}:\ V_{\text{g}}(z)=V_{g}\,-\frac{h_{c}}{2}<z<\frac{h_{c}}{2} \tag{2}\]  
\[\text{Negative gradient}:\ V_{\text{g}}(z)=(p+1)\Big{(}1-\frac{2|z|}{h_{c}} \Big{)}^{p}V_{g}\,-\frac{h_{c}}{2}<z<\frac{h_{c}}{2} \tag{3}\]  
where \(p\) is the gradient index and \(p>0\), terms \(V_{g}\) and \(V_{\text{g}}(z)\) denote the volume fraction of graphite in the core layer and the volume fraction of graphite on \(Z\) coordinate. By applying the two-phase material mixing law, the effective Young's modulus \(E\), [PERSON]'s ratio \(v\) and density \(\overline{\rho}\).  
\[E^{(c)}(z)=E_{e}+\Big{(}E_{g}-E_{e}\Big{)}V_{g}(z) \tag{4}\]  
\[v^{(c)}(z)=v_{e}+\Big{(}v_{g}-v_{e}\Big{)}V_{g}(z) \tag{5}\]  
\[\overline{\rho}^{(c)}(z)=\rho_{e}+\Big{(}\rho_{g}-\rho_{e}\Big{)}V_{g}(z) \tag{6}\]  
where subscripts '\(g\)' and '\(e\)' represents graphite and epoxy resin.
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 4 Experiments and Analysis of Results
### Sensitivity Analysis
#### 4.3.1 Effect of Trust Threshold \(\theta\)  
An experiment was performed to determine the appropriate trust threshold \(\theta\); \(\theta\) ranges from 0 to 1 in increments of 0.1. Figure 2 shows the effect of \(\theta\) in the DBTS method.  
We first examine the effect of trust threshold \(\theta\), ranging from 0 to 1 with increment of 0.1. Figure 2a shows the accuracy and Figure 2b the diversity of the DBTS algorithm for different values of \(\theta\). The optimal value of \(\theta\) leads to the best performance in terms of accuracy and diversity. From Figure 2a we can see that \(\theta\) has very little influence on either precision or recall. The highest accuracy happens when \(\theta=0.4\). From Figure 2b we can see that the algorithm also has the highest diversity when \(\theta=0.4\). Therefore, the optimal value of \(\theta\) is set to 0.4.
## 2 Materials and Methods
### Methods
#### 2.3.5 In Vitro Cytotoxicity Tests  
Mouse fibroblast cell line NCTC, clone L929, achieved from ECACC (Porton Down, Salisbury, UK) was used to evaluate in vitro cell cytotoxicity of the PLA bionanocomposite films by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazol homide (MTT) and lactate dehydrogenase (LDH) assays. The bionanocomposites conditioned as films were cut in discs of 5 mm diameter and sterilized under UV light for 4 h. Cells were seeded at a density of 5 \(\times\) 10\({}^{4}\) cells/mL in 24-well plates in Minimum Essential Medium (MEM) supplemented with 10% fetal bovine serum (FBS) (Biochrom, Berlin, Germany) and 1% antibiotics (penicillin, streptomycin and neomycin), and grown at 37 \({}^{\mathrm{o}}\)C in humidified atmosphere with 5% CO\({}_{2}\). After 24 h, culture medium was replaced with fresh medium, samples were added into the wells (1 disk/well) and cells were further incubated in standard conditions for 24 and 48 h. At the end of these periods, the medium was discarded, cells were washed with phosphate buffer saline solution (PBS) and 500 \(\upmu\)L of MTT working solution (0.25 mg/mL in MEM without FBS) was added to the cells and incubated for 3 h at 37 \({}^{\mathrm{o}}\)C. The insoluble formazan crystals were dissolved with 500 \(\upmu\)L isopropanol. After 15 min of incubation at room temperature with gentle stirring, the absorbance was quantified at 570 nm using a Mithras LB 940 microplate reader (Berthold Technologies, Bad Wildbad, Germany). The optical density registered directly corresponds to the number of metabolically active cells. The results were reported as the percentage of viability compared to the control sample (untreated cells) considered 100% viable. All samples were tested in triplicate per experiment and data were expressed as mean of three experiments \(\pm\) SD.  
LDH activity assay quantifies the amount of lactate dehydrogenase (LDH) delivered in the culture medium when cells are damaged or under stress and allows the evaluation of both cell membrane integrity and cell viability [27; 28]. LDH activity was determined using CytoTox96 kit (Promega, Madison, WI, USA) according to the manufacturer's instructions. Briefly, after 24 h and 48 h of cells incubation in the presence of the bionanocomposites, enzymatic measurements of LDH released intothe culture medium were spectrophotometrically recorded at 490 nm using a 96-well plate reader Tecan Sunrise (Tecan, Grolog, Austria). Results were calculated using the following equation:  
LDH released (%) = (LDH medium (OD490) / LDH positive control (OD490)) \(\times\) 100  
All samples were tested in triplicate per experiment, and the experiments were performed 3 times. The results were compared with the LDH positive control considered 100% cytotoxic, all samples showing low cytotoxicity percentages (below 20%), and correlated with those obtained by the MTT assay.