esa-sceva/satcom-chunk-collection huggingface dataset:
# Vitamin D and Vitamin D Receptor Polymorphisms Relationship to Risk Level of Dental Caries
## 3 Results
### Vitamin D Receptor Gene Polymorphisms
Apart from primary and mixed dentitions, a possible correlation between levels of 25(OH)D and dental caries risk has been investigated for permanent dentition. In our study, we report on three studies regarding permanent dentition and one with both mixed and permanent ones. From our findings in all four relevant studies, it seems that permanent dentition presents a higher correlation with VD deficiency than the mixed one. To support this, we can report that in a cross-sectional study with a sample of 2579 American adolescents, VD deficiency in permanent dentition has a limited but statistically non-significant association with caries in permanent dentition of adolescents [52]. A cross-sectional study on 1688 children from Korea found that children with serum 25(OH)D levels lower than 50 nmol/L were 1.29 times more likely to develop dental caries and that 25(OH)D concentration and DMFT were negatively correlated. However, the authors reported that there is difficulty in confirming the association between dental caries experience and 25(OH)D levels [53]. Another study that has investigated permanent dentition and 25(OH)D levels was conducted in the USA with 4244 participants with a mean age of 51.22 years. This study stated that the severely deficient group was strongly correlated with dental caries and that 25(OH)D is significantly associated with the prevalence of dental caries among US adults [54]. Concerning the VD supplementation for caries prevention in permanent dentition, we found data in all three relevant studies. Firstly, a systematic review of control clinical trials studied the influence of VD supplementation in two different forms (vitamin D2, D3) on dental caries prevention [55]. The results showed that VD supplementation reduced the risk of dental caries by about 47%, but with low certainty [55]. In two different mendelian randomization studies, the results failed to find a strong and statistically significant causal relationship between vitamin D concentrations and risk of dental caries [56; 57]. Driven by the analysis performed in this study, we present a relevant model of the VD, VDRP, and dental caries equation. The model was designed with the Vensim program (Vensim PLE 8.1.0), showing factors influencing the equation. Vensim, developed by Ventana Systems (a simulation system employing causal tracing; US Patent Application EP19910909851, 26 February 1991), is precision simulation software. It primarily shows continuous simulation (system dynamics), with agent-based modeling capabilities. This modeling language supports arrays (subscripts) and permits mapping among factors. The built-in allocation functions satisfy, in our case, constraints that may not be met by conventional approaches (Figure 2).
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 2 Related Work  
### Person Re-Identification Method  
Person re-identification methods originated from multi-camera tracking, and they are now considered a sub-direction of image retrieval. In 2014, deep learning methods were first introduced into person re-identification [13; 14]. Later, spatio-temporal representation was used in some person re-identification methods, such as the deep spatio-temporal appearance descriptor [15], the hierarchical spatial-temporal model [16], the Batch Drop-Block network [17], the temporal attention network [18], the non-local video attention network [19] and spatio-temporal representation factorization [20].  
Although the above person re-identification methods improve accuracy by incorporating spatial-temporal representation, they are trained on cropped image datasets, such as Market-1501 [1], CUHK03 [6], and MARS [7]. These datasets are manually annotated or detected by the Deformable Part Model (DBP) [21] to generate bounding boxes, without any real-world surveillance scenarios. Because most of the person re-identification methods do not involve pedestrian detection, they are not suitable for real-world surveillance videos, and they cannot be used in practical applications.
## 3 MPPT Techniques
### Intelligent MPPT Techniques
#### 3.3.3 Particle Swarm Optimization  
To address the aforementioned issue, swarm intelligence and bio-inspired approaches are applied in PVS for extracting maximum power (global peak) from the P-V curve. The application of swarm methods significantly increased interest in PVS research and effectively solved the issue of global MPP extraction. Typically, ant colonies, flocks of birds, and schools of fish served as the inspiration for swarm intelligence algorithms [46; 47; 48; 49; 50]. The three main benefits of these strategies are that they are simple to implement, begin with a random search, and prevent convergence to local minima.  
The ability of the PSO algorithm has made it the best optimal optimization for nonlinear problems. The PSO algorithm derives its searchability from the behavior of flocks and swarms [50]. PSO has become one of the most popular optimization techniques for MPPT applications over the last decade. The fundamental concept of PSO is that the particles' initial values are randomly chosen within the boundary limits. These particles (duty cycles) are programmed to move around in the search space. The best particle movement in the initial values is referred to as Pbest, and the overall best in subsequent iterations is referred to as Gbest. Furthermore, the particles' position and velocity are updated with each iteration, and the process is repeated until the best position is found. The particle movement in search space is presented in Figure 12.  
A particle status on the search space is characterized by the following two factors:  
- Its velocity position (\(V_{i}\));  
- Position (\(X_{i}\)).  
This will be updated according to the following equations:  
\[V_{i}(k+1)=WV_{i}(k)+C_{1}rand_{1}*(\textit{Pbest}_{i}(k)-X_{i}(k))+C_{2}rand_ {2}*(\textit{Gbest}_{i}(k)-X_{i}(k)) \tag{21}\]  
\[X_{i}(k+1)=X_{i}(k)+V_{i}(k+1) \tag{22}\]  
\(\mathbf{X_{i}(k+1)}\) **is the particle position at \(k\)+1 th iteration, \(\mathbf{X_{i}(k)}\) is the particle position at \(k\)th iteration, \(\mathbf{V_{i}(k+1)}\) is the particle velocity at \(k\)+1 th iteration, \(\mathbf{V_{i}(k)}\) is the particle velocity at \(k\)th iteration, \(\mathbf{C_{1}}\) is the acceleration component associated with Gbest, \(\mathbf{C_{2}}\) is the acceleration component associated with Lbest, \(\mathbf{W}\) is the inertia weight, \(\mathbf{rand1}\) and \(\mathbf{rand2}\) are random numbers from 0 and 1, _Gbest_ is the best position of all particles, and _Pbest_ is the best position of the particle.  
Figure 12: Particle movement in search space for PSO.  
The PSO was the first swarm algorithm to be used in PV MPPT applications. However, as the randomness decreases, PSO loses its diversity. Additionally, because it depends on the starting position of the search agent, convergence is delayed [46; 47; 48; 49; 50; 51; 52]. Furthermore, if the initial duty cycle value is not chosen properly, the PSO approach may become caught at the local MPP under PSCs.
## 2 AP4A Structural Conformation  
All kingdoms of life have a family of nucleotides known as Dinucleoside Polyphosphates (NPnN; N = adenosine, guanosine, uridine, or cytidine, and n = number of phosphates) [9]. Since their discovery, two nucleosides joined by an arbitrary number of phosphate groups have been found in a variety of organisms [10]. Among possible NpNs, diadenosine tetrapphosphate (Ap4A), which consists of two adenosine moieties connected by phosphoester linkages at their 5'-hydroxyl groups to form a chain of four phosphates, is one of the most prominent examples (Figure 1) [11].  
The significance of Ap4A was first recognized in the 1960s, during studies of brine shrimp eggs, where its role in DNA synthesis was observed [12]. Subsequent research  
Figure 1: The structure of diadenosine tetraphosphate (Ap4A).  
revealed its involvement in biochemical processes related to tRNAs and amino acids during protein synthesis. Extensive investigations into Ap4A's structural and chemical properties have demonstrated that different factors such as temperature and pH substantially affect its conformation [13; 14]. Thus, Ap4A can adopt three primary conformations: stacked, folded unstacked, and open [15]. In the stacked conformation, the adenine rings are aligned and stacked while the phosphate chain is folded. In the folded unstacked conformation, the phosphate groups are folded, but the adenine rings are oriented in opposite directions and are not stacked. The open conformation features a fully extended phosphate chain and unstacked adenine rings. For a detailed depiction of the conformational arrangement discussed below, please refer to [PERSON] et al. [15], where the structure is extensively illustrated and described. These conformational changes are influenced by factors such as pH, temperature, and metal ions. At physiological pH, Ap4A predominantly assumes a stacked conformation due to electrostatic interactions between the partially positively charged adenine rings and the negatively charged phosphate groups. In slightly acidic conditions (pH 4-5), Ap4A may adopt an unstacked conformation, while at very low pH (below 3), protonation of the phosphate groups reduces electrostatic attraction, resulting in an open conformation. In addition, the unstacked conformation also occurs with increases in temperature. Thus, these conformations enable Ap4A to interact with various purinergic receptors, which may be present in environments with different pH and ion concentrations [16].
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 7 Discussions and Open Research Issues
### Open Research Issues  
It has been proven that the optimal sink moving trajectory problem is an NP-hard issue [34; 35] that is, by definition, hard to solve. More importantly, the sink mobility issue is tightly related with routing algorithms used therein. Thus, jointly optimizing mobile sink strategies and routing algorithms is one of the primary research challenges. Not only does a proper moving trajectory, cluster head selection, sink number, speed, moving pattern, and sojourn positions need to be carefully designed, but also the corresponding routing algorithms need to be selected for different applications.  
Security is always an important and challenging research issue for WSNs. Restricted to the limitation of resources and deployment environment, it is difficult to implement strong security algorithms [36]. Besides, due to the potential threatens like data modification and malicious attacks, communications between source and destination nodes may have serious influence on the network security and increase the energy consumption during data transmission. Using sink mobility technology in WSNs, various types of data reports may get lost or modified during data transmission due to the change of the location of the sink and selective forwarding. Therefore, security as an essential component for data transmission in dynamic sensor networks environment needs further research attention. It is likely that the security for store and forward [37] will help in this regard.  
In many location-based applications, data obfuscation technologies can be used to protect location privacy to avoid the damage caused by having the position leaked. By reducing location accuracy, data obfuscation technology can achieve the fuzzy effect of the locations of multiple data information. However, the safety of this technology remains to be further researched. For example, a general three-tier security framework has been proposed [38] where two separate key pools are respectively designed for a mobile sink node and pair-wise key establishment between sensor nodes. This scheme can effectively protect sensor networks from stationary access node replication attacks.
## 2 Materials and Methods
### Gene Cloning, Expression, and Purification  
_A. bisporus_ As2796 mycelia cultured in nutrient-rich potato dextrose agar medium [46] were collected. Total RNA was extracted from the mycelia by using an RNAiso Plus kit (Code No. 9108; Takara, Shiga, Japan) following the manufacturer's instructions. cDNA synthesis from total RNA was performed using the RevertAid First cDNA Synthesis Kit (Thermo Scientific, Fair Lawn, NJ, USA). The four putative genes involved in the biosynthesis of 1-octene-3-ol in _A. bisporus_ were cloned by PCR as follows. The reaction system comprised a 100 ng template cDNA, 1 \(\upmu\)L primer (10 \(\upmu\)M), 1 \(\upmu\)L 2 \(\times\) pfu PCR Master Mix (GenStar, Beijing, China), and ddH\({}_{2}\)O up to 20 \(\upmu\)L. The primers used are listed in Table S1. The PCR was run at 94 \({}^{\circ}\)C for 5 min, 32 cycles of 94 \({}^{\circ}\)C for 30 s, 58 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 2 min followed by a 72 \({}^{\circ}\)C extension for 10 min. The PCR products were purified by using a DiaSpin DNA Gel Extraction Kit (Sangon Biotech, Shanghai, China) and sequenced by Henan Shangya Biotech (Zhengzhou, China).  
The sequences of the PCR products were codon-optimized for _E. coli_ expression using the genscript online software. The optimized sequences were synthesized by Shangya Biotech and ligated with the prokaryotic expression vector pET28a. The generated vectors were transformed into _E. coli_ BL21 and expressed under the following conditions. When the cells grew to OD\({}_{600}\) = 0.4-0.6 in LB liquid medium at 37 \({}^{\circ}\)C, IPTG was added to a final concentration of 0.4-0.6 mM, and then the cells were cultivated at 16 \({}^{\circ}\)C and 180 rpm for 12 h. The cells were collected and disrupted by using ultrasonic waves at 4 \({}^{\circ}\)C. The supernatant of the cell-disrupted solution was collected by centrifugation at 5000 \(g\) for 5 min at 4 \({}^{\circ}\)C and purified by using a nickel-ion affinity chromatography column (Sangon Biotech, Shanghai, China). The concentration of the purified protein was measured by using a Pierce(r) BCA Protein Assay Kit (catalog number: 23225; Waltham, MA, USA).
## 3 Characteristics of the code ALE-PSO
### Inequality constraints  
ALE-PSO can deal also with constrained problems. The strategy for constraint handling in ALE-PSO is essentially a barrier method: the swarm must be feasible at each iteration, _i.e._ it must be composed by particles respecting all the constraints imposed to the computation. For this reason, the initial swarm is composed by feasible particles and then its updates are guaranteed to be feasible by the following procedure (without lack of generality, we consider here constraints of the form \(g(\mathbf{x}^{k})\)\(<\)0):  
\[\begin{split}\mathbf{x}^{k}_{t+1}=\mathbf{x}^{k}_{t}+\frac{ \mathbf{u}^{k}_{t+1}}{2^{\alpha}},\quad\alpha=0,1,2,.....\\ \text{stop when }\ g\big{(}\mathbf{x}^{k}_{t+1}\big{)}<0.\end{split} \tag{5}\]  
As \(\mathbf{x}^{k}_{t}\) is feasible, this bisection method ensures that, for a sufficiently large \(\alpha,\ \mathbf{x}^{k}_{t+1}\) will be feasible; this procedure is simple, robust and numerical tests have shown that also minima on the boundary of the feasible domain can be reached; it is used also for the control of box constraints. It must be said that ALE-PSO works on a \(n\)-dimensional box where all the particles are located at the beginning:  
\[\mathbf{a}\leq\mathbf{x}^{k}_{0}\leq\mathbf{b}\,, \tag{6}\]  
Inequalities (6) being intended to act on each component of the particle \(\mathbf{x}^{k}\). The vectors \(\mathbf{a}\) and \(\mathbf{b}\) represent the lower and upper bounds of the particles position in the \(n\)-dimensional search space. The above strategy allows respecting conditions (6) at each iteration.  
The use of a barrier method is rather important for a PSO, because the evolution of the swarm depends upon two points, \(\mathbf{p}^{k}\) and \(\mathbf{p}^{g}\), which must be feasible, but also for the analysis of the convergence of the swarm. In fact, an index of the effectiveness of the algorithm is the control of the average of the objective function over the whole swarm, at each iteration: a good PSO makes all the swarm, and not only the best particle, evolve towards a feasible good point. If the swarm is composed also by unfeasible particles, these can alter significantly the average of the objective, in one sense (decrease) or in the other (increase), which renders difficult to judge about the effectiveness of the algorithm.
## 2 Methodology
### Extracting \(t_{A}\)
#### 3.1.1 Pulse Compression  
Pulse compression is performed to compress the wide pulse into a narrow one, making the output signal appear at the target's range cell, which increases the signal to noise ratio.  
The SAR system is linear and the resulting image after pulse compression is represented by the sinc function.3.1.2. Interpolation  
The signal after the pulse compression is discrete, with unsmooth transitions and some steep edges at the peak. Interpolation is used for smoothing in order to accurately find the peak position. The interpolation process is as follows:  
1. Fourier transform the compressed signal to the frequency domain;
2. Perform zero-added (power of 2) in the noise region outside the signal bandwidth;
3. Perform inverse Fourier transform to complete the time-domain interpolation so that the time-domain signal is smooth and continuous.  
Another function of the interpolation is to make the characteristic of range migration more obvious. The range migration is approximately 1-2 cells, which is relatively small. Through interpolation, the migration is magnified, which might reach the interpolation multiplier, making the measurement of the \(t_{A}\) more accurate.
## 3 Results and Discussion
### Elemental Composition of Medicinal Plants
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Zn (ppm)** & **Fe (ppm)** & **Mn (ppm)** & **K (ppm)** & **Ca (ppm)** & **Mg (ppm)** \\ \hline MP & 22.21 \(\pm\) 0.96 \({}^{\text{a}}\) & 416.77 \(\pm\) 3.35 \({}^{\text{a}}\) & 46.10 \(\pm\) 1.18 \({}^{\text{a}}\) & 5286.30 \(\pm\) 27.07 \({}^{\text{a}}\) & 4256.71 \(\pm\) 14.27 \({}^{\text{a}}\) & 381.14 \(\pm\) 3.65 \({}^{\text{a}}\) \\ \hline TS & 17.35 \(\pm\) 0.81 \({}^{\text{b}}\) & 489.00 \(\pm\) 3.64 \({}^{\text{b}}\) & 27.56 \(\pm\) 1.07 \({}^{\text{b}}\) & 3400.10 \(\pm\) 13.11 \({}^{\text{b}}\) & 4423.89 \(\pm\) 13.77 \({}^{\text{b}}\) & 357.61 \(\pm\) 3.46 \({}^{\text{b}}\) \\ \hline SO1 & 24.32 \(\pm\) 0.94 \({}^{\text{c}}\) & 271.85 \(\pm\) 1.89 \({}^{\text{c}}\) & 26.53 \(\pm\) 0.85 \({}^{\text{b}}\) & 7215.30 \(\pm\) 19.44 \({}^{\text{c}}\) & 6115.06 \(\pm\) 16.03 \({}^{\text{c}}\) & 462.88 \(\pm\) 3.37 \({}^{\text{c}}\) \\ \hline AM & 11.33 \(\pm\) 0.65 \({}^{\text{d}}\) & 220.22 \(\pm\) 1.38 \({}^{\text{d}}\) & 23.23 \(\pm\) 0.79 \({}^{\text{c}}\) & 3459.00 \(\pm\) 16.48 \({}^{\text{b}}\) & 4067.74 \(\pm\) 14.86 \({}^{\text{d}}\) & 364.52 \(\pm\) 3.92 \({}^{\text{b}}\) \\ \hline OV & 17.47 \(\pm\) 0.74 \({}^{\text{b}}\) & 286.80 \(\pm\) 1.75 \({}^{\text{e}}\) & 22.28 \(\pm\) 0.87 \({}^{\text{c}}\) & 3492.77 \(\pm\) 17.54 \({}^{\text{b}}\) & 2748.35 \(\pm\) 9.79 \({}^{\text{e}}\) & 361.54 \(\pm\) 3.64 \({}^{\text{b}}\) \\ \hline EP & 19.52 \(\pm\) 0.87 \({}^{\text{e}}\) & 160.62 \(\pm\) 1.00 \({}^{\text{f}}\) & 17.21 \(\pm\) 0.77 \({}^{\text{d}}\) & 4825.90 \(\pm\) 19.65 \({}^{\text{d}}\) & 4708.79 \(\pm\) 15.83 \({}^{\text{f}}\) & 382.42 \(\pm\) 3.82 \({}^{\text{a}}\) \\ \hline HO & 14.25 \(\pm\) 0.85 \({}^{\text{f}}\) & 198.53 \(\pm\) 1.10 \({}^{\text{g}}\) & 23.22 \(\pm\) 0.17 \({}^{\text{c}}\) & 8468.50 \(\pm\) 20.00 \({}^{\text{e}}\) & 5664.99 \(\pm\) 16.08 \({}^{\text{g}}\) & 536.67 \(\pm\) 3.56 \({}^{\text{d}}\) \\ \hline SO2 & 33.10 \(\pm\) 1.17 \({}^{\text{g}}\) & 320.66 \(\pm\) 2.15 \({}^{\text{h}}\) & 26.04 \(\pm\) 0.97 \({}^{\text{b}}\) & 5013.20 \(\pm\) 19.98 \({}^{\text{e}}\) & 5371.23 \(\pm\) 16.40 \({}^{\text{h}}\) & 454.79 \(\pm\) 3.47 \({}^{\text{e}}\) \\ \hline LA & 35.07 \(\pm\) 1.27 \({}^{\text{g}}\) & 298.25 \(\pm\) 1.79 \({}^{\text{i}}\) & 22.26 \(\pm\) 0.82 \({}^{\text{c}}\) & 9045.20 \(\pm\) 21.96 \({}^{\text{f}}\) & 7575.43 \(\pm\) 19.18 \({}^{\text{i}}\) & 605.26 \(\pm\) 4.44 \({}^{\text{f}}\) \\ \hline MO & 28.35 \(\pm\) 1.14 \({}^{\text{h}}\) & 360.77 \(\pm\) 2.42 \({}^{\text{i}}\) & 23.5
## 2 Related Work
### Semi-Supervised Learning Methods  
Additionally, we consider introducing semi-supervised learning methods [20; 21; 22] to reduce the dependency of deep learning models on labeled datasets. Mean Teacher [23] is a general framework for semi-supervised learning that enhances model robustness and adaptability by leveraging unlabeled data, providing a new solution for encrypted traffic identification. [PERSON] et al. [24] designed a lightweight encrypted traffic classifier based on CNN. By using a semi-supervised learning framework to convert traffic data into grayscale images as input to the model, they improved the accuracy of MT-CNN while using only limited labeled data. [PERSON] et al. [25] combined CNNs with autoencoders to develop unsupervised machine learning approaches for detecting anomalies in network traffic. Although these methods reduce the model's dependency on labeled data to some extent, they do not consider the potential relationships between traffic payload features and sequence features.  
Therefore, this study aims to propose a more effective encrypted traffic recognition method by combining the advantages of CLSTM (a fusion of 2-conv CNN and BiLSTM) and the Mean Teacher framework, which aim to solve the limitations of traditional methods in encryption traffic identification. Specifically, we first utilize CNN and BiLSTM to automatically learn the feature representations of traffic from encrypted traffic data and then fuse them. The Mean Teacher framework is employed to strengthen the model's generalization ability by leveraging a large part of unlabeled data in addition to the limited labeled data. By detecting the fusion features of traffic load characteristics and sequence features, the accuracy and robustness of encrypted traffic recognition are improved while reducing the model's reliance on labeled data.
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 2 Materials and Methods
### Random Forest Classification  
The R package'mlr' [21] was used to set the RF hyperparameter combination (i.e., _mtry_, _min.node.size_, _ntree_) through a 5-fold cross-validation with 20 repetitions and selected those with a higher Cohen's kappa coefficient. The tuned hyperparameters were used to calibrate the classification models from the NDVI and LAI predictors using the R package 'ranger' [22]. The variables' importance for the final set of selected predictors used in the models was calculated using the Gini index.  
A stratified sampling method was applied to the crop type reference map of the year 2019 in order to select the pixels which represented all 8 classes of crop types and could be used as training samples for the classification and as test samples to verify the accuracy of the classification obtained. Here, 70% of the pixels were used as training samples and the remaining 30% as the test samples.  
The results of the classifications obtained were evaluated by means of confusion matrices according to the test samples. Overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA), and [PERSON]'s kappa coefficient (K) were assessed.  
Finally, the crop type map product for the year 2019 was predicted using the calibrated supervised machine learning models.