esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Protective Effect of Wheat and Barley Grass Against the Acute Toxicological Effects of the Concurrent Administration of Excessive Heavy Metals in Drinking Water on the Rats Liver and Brain**  
**[PERSON] 1, [PERSON] 2,*,**, **[PERSON] 1, [PERSON] 1,**  
**[PERSON] 1,**, **[PERSON] 3,4,**, **[PERSON] 5,** and [PERSON] 2,**  
1 Nutrition and Food Science Department, Faculty of Home Economics, Al-Azhar University, Tanta 31732, Egypt [EMAIL_ADDRESS] (H.S.E.); [EMAIL_ADDRESS] (A.M.E.); dr_[EMAIL_ADDRESS] (L.M.A.E.-M.); [EMAIL_ADDRESS] (O.I.E.-D.)  
2 Department of Environmental and Biological Sciences, Home Economy Faculty, Al-Azhar University, Tanta 31732, Egypt: [EMAIL_ADDRESS]  
3 Department of Genetics, Faculty of Agriculture, Kaftreshiehki University, Kafr El-Sheikh 33516, Egypt: [EMAIL_ADDRESS]  
4 Department of Plant Production and Protection, College of Agriculture and Veterinary Medicine, Qassim University, Burgdu 31452, Saudi Arabia 5  
5 Department of Plant Production (Genetic Branch), Faculty of Environmental and Agricultural Sciences, Arish University, El-Arish 45511, Egypt; [EMAIL_ADDRESS]  
*Correspondence: [PERSON]@azhar.edu.eug  
**Abstract:** Heavy metal contaminated water is a great concern because of its high toxicity, non-biodegradability, and bioaccumulation. Therefore, non-contaminated water is fundamental for a healthy life. Special attention is paid to the health-promoting ingredients of germinated whole cereal products. This study aimed to (1) examine the potentially harmful effects of Cu, Mn, and Zn on rat livers and brains, and (2) the potentially protective action of wheat and barley grasses against the expected harmful effects of these metals. The rats were treated with water contaminated by heavy metals (HMs) and germinated wheat and barley for 60 days. The rat liver functions and histopathological examinations were analyzed. Comet assay was evaluated to assess the damage in the DNA of rat livers and brains. The results indicated a significant alteration in liver functions in rats exposed to HMs; however, wheat and barley grasses at high doses decreased the harmful effects. An insignificant difference was noticed in total protein, albumin, and globulin of rats treated with HMs compared with the control. A significant increase in the serum and liver levels of HMs was recorded; however, they were reduced by wheat and barley grasses. Rat livers treated with HMs exhibited severe histological effects. The groups treated with wheat and barley grasses showed a normal liver architecture. A significant increase in DNA damage in the livers and brains was observed in rats treated with HMs, which was reduced when treated with wheat and barley grasses. Thus, using germinated seeds is promising to avoid damaging of HMs.  
**Keywords:** drinking water contamination; heavy metals; rat liver; rat brain; germinated wheat; germinated barley; protective action
## 5 Numerical Results
### Multi-Objective Function Optimization  
For the multi-objective optimization, we consider that the cost of the energy losses, i.e., \(f_{1}\), is an objective function in conflict with the investments in STATCOMs, i.e., \(f_{2}\). In this sense, we adopt the Pareto front building strategy reported in [29], which is based on the weighting factors associated with the objective functions. Table 9 reports all the solutions obtained for the multi-objective approach using the GAMS optimization package and the BONMIN solver. Note that the third and fourth columns demonstrate the multi-objective nature of the optimization problem associated with the optimal installation of STATCOMs in power systems. For example, Solution 1 shows that the solution with no STATCOMs installed has a higher energy loss cost value, i.e., MUS $24.9904, while Solution 19 shows the minimum energy loss cost value, i.e., MUS $15.3480 with a total inversion of MUS $3.6919. However, these solutions are the extremes of the Pareto front and are expected solutions, as between more investments in STATCOMs, more reduction in the cost of the energy losses is expected.  
Figure 6: Reactive power behavior of the STATCOMs in nodes 6 and 10 for the IEEE 24-bus system.  
In addition, the results presented in Table 9 allow the following observations: (i) the most attractive nodes for the location of STATCOMs are nodes 6 and 10, respectively; (ii) in Solutions 6-13, the annual operational costs of the grid are lower than MUS $18, which implies that the solution of the linear combination is contained in this range of solutions; and (iii) if the last column in Table 9 is selected as the performance indicator, then Solution 9 corresponds to the minimum annual operative cost with a value of MUS $17.3578, which implies a total reduction with respect to Solution 1 (without the location of STATCOMs) of about 30.58%. Note that this solution differs from the linear combination by less than 0.02%.  
Regarding the processing times, the solution of the single-objective optimization problem with the BONMIN solver in the GAMS software takes about 11.26 s, and the multi-objective optimization model with 20 solutions in the Pareto front takes about 242.20 s. These values demonstrate the effectiveness and robustness of the GAMS optimization package for dealing with large-scale MINLP models.
# Chinese Event Detection without Triggers Based on Dual Attention
## 3 Event Detection without Triggers Based on Dual Attention
### Embedding Vector  
In the feature construction layer, the feature vector \(W\) is composed of three parts: the word embedding vector, the entity type embedding vector, and the lexical annotation embedding vector, which is shown in Figure 3. The word embedding converts text into mathematical representations so that similar words have similar vector representations. The word embedding can capture meaningful semantic patterns in words [30; 31] and assist neural networks in processing text. In this paper, we use ALBERT [32] as a word embedding model to transform the Chinese sentences containing event information and the document where the sentences are located into feature vectors. Unlike word2 vec [33], ALBERT generates embedding vectors by pre-training on a large corpus first and then fine-tuning training with specific small datasets. ALBERT dynamically learns context so that word embedding vectors have richer semantics and solves the polysemous problem in word2 vec. Compared with BERT [34], ALBERT has fewer training parameters, which means it is faster and could work better after expanding the model's depth. It is one of the best word-embedding models.  
The purpose of event detection is to classify events through the identification of keywords (i.e., event trigger words). Both Chinese and English words have the polysemous problem. The embedding vectors generated by ALBERT are rich in meaning, which can alleviate the polysemy problem and play an important role in event detection.  
Entity types can be converted to low-dimensional vectors by looking up a randomly initialized embedding table. Event trigger words are mostly verbs with a few nouns, while entities are the opposite. Therefore, embedding vectors of entity types can improve the probability of trigger word recognition. Word annotation can be done by word meaning and context. In this paper, we use the Stanford CoreNLP to label the lexical properties of each word and embed a table to convert them into low-dimensional vectors. Since event trigger words are mostly verbs or nouns, the lexical annotation embedding vector can help the model filter out unnecessary words.  
The events in sentences are pre-trained by incorporating dual attention layers. The event type (Table Map) is transformed into two embedding vectors, \(t_{1}\) and \(t_{2}\), and is used to assist local attention in capturing key information in a sentence. \(t_{1}\) is used to simulate hidden event trigger words. \(t_{2}\) is used to support global attention by capturing context in documents and avoiding the ambiguity of trigger words.
# Elliptical Structures of Gravity Waves Produced by Typhoon Soudelor in 2015 near Taiwan
## 1 Introduction
Previously, Reference [28] described GWs in the eye of [PERSON] (1992) as a typical feature during the development of TCs. Pronounced GW fluctuations, with a period of 3 h, were identified in the eye and the eyewall during the deepening stage of the simulated asymmetric inner core. Reference [29] revealed oscillations, with periods of about 2 h, in wind speed and pressure surface around the eye and eyewall, more significant at the side with higher wind speeds in asymmetric TCs. Indeed, the TC vortex was rarely symmetric during its lifecycle due to asymmetric instability resulting from interactions between the TC and its environment, especially during intensity changes. Asymmetries near the eyewall were mostly dominated by the wave number one (WN1) component [30]. The WN1 asymmetry, in the dynamic and thermodynamic field, is mainly caused by the environmental vertical wind shear among, for example, land-sea contrast during landfall, topography blocking, \(\beta\)-effect, sea-surface temperature, and interactions with upper-level troughs [31]. During the intensification of TC Ivan (2008), prior to landfall over Madagascar, Reference [20] diagnosed a WN1 asymmetry with evidence of a quasi-inertia stratospheric GW with horizontal and vertical wavelengths of 300-600 km and 2-3 km respectively and a mean period of 13 h. The TC-induced GW was found to propagate eastward at a speed of about 13 m/s and upward in the westward stratospheric background wind. Finally, previous numerical modelling indicates that the coarse vertical resolution of simulated GWs does not ensure the coupling between vertical and horizontal scales. This coupling is crucial to the spectral characteristics of short-scale GWs with vertical wavelengths \(<\)5 km, which are mostly derived from measurements of high-vertical resolution sounding. Therefore, in this study, a realistic mature TC with high-vertical resolution was simulated to investigate the elliptically polarized GWs in the troposphere and LS when the Category 3 TC Soudelor (2015) approached Taiwan, starting early on 5 August and going until late on 7 August, prior to landfall. The description of the model and methods of analysis are described in Section 2. Section 3 provides an overview of TC Soudelor and validates the simulation. The analysis of the simulated TC-induced GWs prior to the landfall of TC Soudelor and the observations are reported in Section 4. Conclusions are drawn in Section 5.
## 3 Development of a Mixed Prehabilitation Program Model  
According to the previous Sections 2.1-2.4 the length of a prehabilitation program for cancer patients is typically four to six weeks [5; 8]. Physical activities are the main component of the prehabilitation program. A clinician can prescribe the patient one or more of the nine physical activities (walking, running, cycling, treadmill, cross-trainer, step up, leg press, rowing, and staircase ascending/descending) that are most commonly used in prehabilitation programs [4; 5; 8; 9]. The prescribed exercise intensity, duration, and frequency varies across the documented studies [18; 19]. The total weekly thresholds should be at least two 10 min sessions per day that equate to a total of 150 min at a moderate intensity or 75 min at a vigorous intensity, or a combination vigorous and moderate intensity that equates to a similar volume of exercise [8; 18; 30; 31]. These target threshold values may be adjusted based on the initial program parameters prescribed by the clinician. Bed rest or prolonged periods of inactivity are considered to have negative impacts on the prehabilitation program [13; 14; 26]. Table 2 shows prehabilitation physical exercise parameters and their boundaries.  
\begin{table}
\begin{tabular}{c c c} \hline
**Prehabilitation Elements** & **Prehabilitation Boundaries** & **Remarks** \\ \hline Time frame of prehabilitation program & Four weeks or six weeks & May be less or more based on the patient status and surgery scheduled. \\ Number of sessions per week & Minimum two sessions & The patient could perform any number of sessions according to the health supervisor guidance. \\ Threshold time of physical activity per week & 150 min moderate intensity or equivalent & 75 min of vigorous intensity is considered equal to 150 min moderate intensity [30; 31]. \\ Intensity level & Light, moderate, vigorous & Light \textgreater{}50\% of maximal heart rate, moderate & 50-70\% of maximal heart rate, vigorous \\ Minimum time of each session & 10 min or more at a moderate intensity & 70-80\% of maximal heart rate [4]. \\  & 10 min or more at a moderate intensity & 5 min of vigorous is equal to 10 min of moderate and approximately 15 min of light intensity [28; 29]. \\ Physical exercise types & Walking, running, cross trainer, rowing, treadmill, step up, leg press, cycling, staircase ascending/descending & This could be any pair or more of these nine activities, and is based on the physician’s and physiotherapist’s recommendations. \\ Location of the prehabilitation program & Indoor, GYM, outdoor & According to the availability of physical resources and the patient status. \\ Negative impact & Bed rest & Each day of bed rest will produce negative credit and eliminate the effort of one day of recommended physical activity [14; 25]. \\ \hline \end{tabular}
\end{table}
Table 2: Key prehabilitation elements, boundaries, and rules for the model.
## 3 Materials and Methods
### Proposed Method
#### 3.3.3 User Attention Mechanism  
The user attention mechanism is one of the core technologies proposed in this user-centered data privacy protection framework, as shown in Figure 4.  
Its purpose is to protect user privacy while ensuring the efficiency and accuracy of data processing. To achieve this goal, the user attention mechanism improves upon the traditional Transformer self-attention mechanism, enabling a more precise identification and protection of sensitive user information. The user attention mechanism incorporates user privacy weights into the traditional self-attention mechanism, dynamically adjusting attention weights to protect sensitive user information. Specifically, the user attention mechanism consists of the following components. The first is the introduction of user privacy weights: To protect user privacy, user privacy weights \(\alpha\) are introduced into the attention calculation. Each input position has a privacy weight representing the sensitivity of the data at that position. The second step is the dynamic adjustment of attention weights. When calculating attention scores, the user privacy weights are used to adjust the attention scores, ensuring that sensitive data receive appropriate protection. The last step is the multi-level attention mechanism, which is introduced to enhance privacy protection. Different levels of attention mechanisms can capture various features of the data, enabling finer-grained privacy protection. The specific mathematical formulation of the user attention mechanism is as follows:\[\text{UserAttention}(Q,K,V,\alpha)=\text{softmax}\bigg{(}\frac{QK^{T}+\alpha}{\sqrt{d_{ k}}}\bigg{)}V \tag{18}\]  
Here, \(\alpha\) is the user privacy weight matrix, whose elements represent the privacy sensitivity of each position in the input sequence. When calculating attention scores, \(\alpha\) is added to \(QK^{T}\), ensuring that sensitive data receive higher or lower attention, thus achieving privacy protection.  
To demonstrate the effectiveness of the user attention mechanism in protecting user privacy, assume that the input data \(X\) contain both sensitive information and non-sensitive information, which are denoted as \(X_{s}\) and \(X_{n}\) respectively. By introducing the user privacy weights \(\alpha\), the attention score calculation becomes  
\[\text{Attention}_{s}=\text{softmax}\bigg{(}\frac{Q_{s}K_{s}^{T}+\alpha_{s}}{ \sqrt{d_{k}}}\bigg{)}V_{s} \tag{19}\]  
\[\text{Attention}_{n}=\text{softmax}\bigg{(}\frac{Q_{n}K_{n}^{T}+\alpha_{n}}{ \sqrt{d_{k}}}\bigg{)}V_{n} \tag{20}\]  
In these equations, \(Q_{s},K_{s}\), and \(V_{s}\) represent the query, key, and value matrices for sensitive information, with \(\alpha_{s}\) denoting the privacy weights for sensitive information. Similarly, \(Q_{n},K_{n}\), and \(V_{n}\) represent the query, key, and value matrices for non-sensitive information, with \(\alpha_{n}\) denoting the privacy weights for non-sensitive information. The presence of \(\alpha_{s}\) ensures that the attention scores for sensitive information are adjusted, providing better protection during the model processing.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 2 Numerical Modeling and Verification
### Verification of Numerical Model  
SHPB apparatus relies on two basic assumptions, namely, one-dimensional stress wave assumption and stress uniformity assumption. It is crucial to achieve stress uniformity in rock specimens to ensure the validity of numerical simulation. To satisfy the stress uniformity assumption, the dynamic stresses at both ends of the rock specimen must be approximately equal [35; 36]. Therefore, stress uniformity can be verified by comparing the stress-time histories at both ends of rock specimens during cyclic impacts. As illustrated in Figures 3 and 4, stress balance verifications are conducted using the initial impact in different conditions. In Figures 3 and 4, In, Re, and Tr represent incident stress waves, reflected stress waves, and transmitted stress waves, respectively. It can be observed that the sum of the incident stress wave and reflected stress wave aligns with the transmitted stress wave prior to reaching the peak stress, indicating the achievement of stress balance during the impact.  
Figure 3: Stress balance under 0 MPa confining pressure. (**a**) 170 MPa half-sine incident stress wave, (**b**) 180 MPa half-sine incident stress wave, (**c**) 190 MPa half-sine incident stress wave.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking
### Measurement-Track Association  
Measurement to track association is the process of assigning measurements to established tracks. The measurement gating is performed by the chi-square hypothesis test assuming Gaussian measurement residuals [21]. The measurement in the validation region is considered candidates for the target \(t\) at frame \(k\) as  
\[Z_{t}(k)=\Big{\{}\mathbf{z}_{m}(k)\Big{|}\mathbf{v}_{tm}(k)^{T}[S_{t}(k)]^{-1}\mathbf{v}_ {tm}(k)\leq\gamma_{g},m=1,\ldots,M(k)\Big{\}}, \tag{15}\]  
\[\mathbf{v}_{tm}(k)=\mathbf{z}_{m}(k)-H\mathbf{x}_{t}(k|k-1), \tag{16}\]  
where \(\mathbf{z}_{m}(k)\) is the \(m\)-th measurement vector at frame \(k\), \(\gamma_{g}\) is the gating size for measurement association, and \(M(k)\) is the number of measurements at frame \(k\). The NN association rule assigns track \(t\) to the \(\hat{m}_{tk}\)-th measurement, which is obtained as  
\[\hat{m}_{tk}=\underset{m=1,\ldots,m_{t}(k)}{\arg\min}||\mathbf{v}_{tm}(k)^{T}[S_{ t}(k)]^{-1}\mathbf{v}_{tm}(k)||, \tag{17}\]where \(m_{t}(k)\) is the number of valid measurements for target \(t\) at frame \(k\). Any remaining measurements that fail to associate with the target go to the initialization stage in Section 3.2.
## 3 Rare Earth Flotation from Primary Resources
### Flotation Reagents
#### 3.1.3 Depressants  
Depressants are needed because common gangue minerals found in REM deposits like carbonates, silicates, and iron-bearing minerals have similar adsorption affinities to collectors as REM during flotation [62; 96; 123; 124].  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multicolumn{1}{c}{**Collectors**} & **Samples** & **pH** & **Depressants** & **Recovery [\%]** & **Grade [\%]** & **References** \\ \hline \multirow{2}{*}{Phophoric acid ester (Flotinor 1682)} & Bastnaesite & 4–9 & – & \(>95\) & – & [32] \\  & Monazite & 4–9 & – & \(>95\) & – & [32] \\  & Monazite and dolomite & 7 & – & 31 & – & [34] \\ Flotinor SM15 & Bastnaesite and quartz & 9 & Sodium & \(>90\) & – & [84] \\ Flotinor 1682; mixed & & & & & & \\ with A845 & REMs & 7 & Citric acid & – & 6 & [110] \\  & Bastnaesite & 4–5 & – & 89–91 & – & [31; 106] \\ Lauryl phosphate & Bastnaesite, calcite, and quartz & 4–7 & – & 90 & – & [111] \\  & Bastnaesite, calcite, and quartz & 5 & – & 80 & 52 & [112] \\
2-ethylhexyl phosphate & Bastnaesite, calcite, and quartz & 5 & – & 75 & 95 & [112] \\ Di(2-ethylhexyl) & Bastnaesite & 8 & – & 96 & – & [113] \\ phosphate & Bastnaesite & 8 & – & 37 & – & [113] \\ Dibutyl phosphate & Bastnaesite & 8 & – & 22 & – & [113] \\ Thibutyl phosphate & Bastnaesite & 8 & – & 96 & – & [114] \\
1-Hydroxydodecylidene- & Bastnaesite and calcite & 8 & – & 96 & – & [114] \\  & Synthetic (Ce,La)\({}_{2}\)O\({}_{3}\), & & & & & \\  & synthetic calcium fluorite, & 5 & – & 90 & 72 & [115] \\  & synthetic fluorapatite & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of recent studies on REM flotation using organo-phosphoric acid collectors.  
[PERSON] and [PERSON] [52] and [PERSON] et al. [86] successfully depressed silicate minerals (e.g., zircon, schorl, staurolite) and carbonates (at 75 \({}^{\circ}\)C) using sodium silicate. The depression mechanism proposed by these authors was the physical adsorption of negatively charged silicate to positively charged alkaline hydroxylated complexes, which led to hydrophilic film formation on silicate and carbonate minerals. In addition, combining sodium silicate and starch was suggested by [PERSON] et al. [49; 50; 51; 56; 96], which lowered sodium silicate consumption without affecting efficiency. Ox oxalic acid could also depress quartz, dolomite, and calcite at around pH 3.8-7.2 due to the formation of Mg/Ca oxalate precipitates on gangue mineral surfaces that inhibit collector adsorption [123].  
Other depressants have also been investigated, but their mechanisms of depression were not reported. Ammonium lignocellulosic could depress iron-bearing minerals (imemite) [52], while sodium fluorosilicate [100] and/or EDTA [76] suppressed fluorite floatability. Meanwhile, sodium hexametaphosphate [80] and/or xanthan gum [48] were found to depress calcite and sodium metasilicate, preventing the flotation of quartz and feldspar [57]. Finally, [PERSON] et al. [46] reported the depression of iron-bearing minerals by sodium lignin sulfonate.