esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Biopolymer-Based Nanoencapsulation
#### 5.1.2 Protein-Based Biopolymers  
Proteins are promising nanocarriers for delivery purposes due to their interesting functional properties: their molecular structures are dependent on their amino acid sequence and environmental factors such as pH, ionic strength, temperature, good emulsifying capacity, solubility, stability, and so on, as well as their ability to modify the texture, the flavor, and the color of the food matrices into which they will be incorporated [2].  
Casein is an amphiphilic protein that has a strong tendency to spontaneously self-assemble into micelles in aqueous solution, and it can also interact with other proteins or organic compounds to form micellar complexes. Casein or caseinate have been found to be of valuable use as nanoencapsulating natural biopolymers due to their balanced hydrophobic and hydrophilic amino acid moieties. [PERSON] et al. [91] self-assembled omega-3 polyunsaturated fatty acids and casein to prepare nanomicelles with a particle size of 50-60 nm. [PERSON] et al. [92] used a polylactic acid-glycolic acid copolymer to assemble with casein to prepare nanoparticles with a distinct core-shell structure. In another study, the authors used casein micelles and nano-gold particles to obtain casein-nano-gold-conjugated nanoparticles with a particle size of about 200 nm [93]. The interactions between iron oxide nanoparticles and casein micelles were also studied to prepare iron oxide/casein composite nanoparticles with an average particle diameter of 38 nm [94].  
Zein is a group of prolamins that are insoluble in water. It has both hydrophilic and lipophilic properties and good biocompatibility. Its hydrophobic region can be polymerized into colloidal particles with a diameter of 50-550 nm. Using the phase separation technique, [95] encapsulated three essential oils--oregano, red thyme and cassia seed essential oils--into zein nanospheres. Whey proteins have been reported as an effective encapsulation system for different hydrophobic antimicrobial agents, and they were widely used in the design of protein-polysaccharide complex systems [96]. Polysaccharides-proteinencapsulation systems are more valuable than pure single biopolymer systems because of their higher chemical and colloidal protection. [PERSON] et al. [97] embedded orange peel oil into whey protein and pectin matrices by complex coacervation. Their results showed that the obtained nanocomposites had particle sizes of 360, 182, and 185 nm at pH 3, 6, and 9, respectively. Encapsulating materials based on complexed biopolymers are discussed in the next section.
## 1 Introduction  
The presented paper deals with the solution of optimization problems by means of artificial intelligence (AI) techniques. Topology optimization (TO) was chosen as an application example, even though the described method is applicable to many optimization problems and thus has generality.  
TO is a method of calculating a shape (topology). In TO, the material distribution over a given design domain is the subject of optimization by minimizing a given objective function while satisfying given constraints [1]. In most cases, suitable search algorithms solve the optimization problem mathematically.  
The combination of AI and TO in the state-of-the-art research mostly requires optimized geometries or intermediate results generated by conventional TO. For this reason, they are subject to several limitations that affect those techniques, such as large computational effort and the need to prepare representative data.  
The approach proposed here aims at removing those drawbacks by generating all the artificial knowledge required for optimization during the learning phase, with no need to rely on pre-optimized results.
## 6 Conclusions  
We propose an NSVD filter and study its application in adaptive beamforming for high-quality CPWC images. The proposed CPWC+NSVD method is compared to CPWC and CPWC+SVD on simulated and experimental datasets, while CF, GCF, and SLSC are presented to show the image quality improvements when the NSVD filter is combined with adaptive weighting factors. We found that CPWC+NSVD outperforms CPWC and CPWC+SVD in noise suppression, which provides higher CR and CNR. With 25 plane-waves, CPWC+NSVD achieves similar CR, CNR, and sSNR compared to CPWC with 75 plane-waves. When applied in combination with adaptive beamforming methods, the NSVD filter helps to achieve a well-preserved speckle pattern and further reduce noise. We also analyze the influences of the size of the sliding window (i.e., neighbor region) and \(\beta\) values on the proposed methods. We recommend the size of neighbor region about 1 mm and \(\beta=3\) by taking into consideration of image quality and computational complexity. Regarding the optimization of computational efficiency, parallel computation can be adopted to improve imaging speed.  
**Author Contributions:** Conceptualization, S.F.; methodology, S.F. and Y.W.; software, S.F.; validation, Y.W.; formal analysis, S.F., Y.W., and C.Z.; investigation, S.F. and Y.W.; writing--original draft preparation, S.F. and Y.W.; writing--review and editing, S.F., Y.W., C.Z., Z.H., and H.P.; visualization, S.F.; supervision, C.Z., Z.H., and H.P.; project administration, C.Z., Z.H., and H.P.; funding acquisition, Z.H. and H.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 61501163 and 61172037.  
**Acknowledgments:** The authors are grateful to the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS) organizers for sharing imaging data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 21: Effect of the number of compounding angles on (**a**) CR, (**b**) CNR, and (**c**) sSNR in in vivo images.
## 4 Simulation Results and Discussions
### Single-Objective OPF Problem
#### 4.1.3 Statistical Investigation
For the loss objective, MaMPA generated a better minimum loss and standard deviation values than those of SSA while the maximum and average values were slightly worse than those of SSA. Thus, MaMPA also has a high consistency in the large system, and high-quality feasible solutions can be generated in a few runs. So, MaMPA is a high-consistency algorithm for solving SOOPF problems by providing small standard deviation values for each considered objective function in the IEEE 30- and 118-bus system. In the large IEEE 118-bus system, the minimum values of each objective generated by MaMPA are also better than the compared algorithms. The system operators can receive high-quality solutions within a few runs of the algorithm process.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
###### Abstract  
In this manuscript, we present a novel deployment protection method aimed at safeguarding aeronautical radio altimeters (RAs) from interference caused by fifth-generation (5G) telecommunication base stations (BSs). Our methodology involves an integrated interference model for defining prohibited zones and utilizes power control and angle shutoff methods to mitigate interference. First, to ensure reliable protection, we define both horizontal and vertical prohibited zones and investigate their variations to immunize RA against 5G interference. Second, we validate the effectiveness of the model in various operational scenarios, analyzing the influence of factors such as base station types, antenna parameters, flight altitude, and aircraft attitudes to cover a wide range of real-world scenarios. Third, to mitigate interference, we propose and analyze the power control and angle shutoff methods through simulation for the RMa prohibited zone. Our results demonstrate the efficacy of the deployment protection method in safeguarding RAs from 5G interference, providing guidance for interference protection during civil aviation operations and base station deployment near airports.  
radio altimeter; 5G; interference; deployment protection
### Importance of the Study  
An essential innovation of this study is that a simple equation can be used to estimate the variation in lateral stresses, depending on both the foundation size and soil relative density. As stated in the literature on lateral soil theories, the geomechanical properties of the soil were not taken into account in previous studies, and these studies proposed formulas that provided the same results for all soil types.  
The model proposed in this study provides an integrated approach that can estimate lateral stress increases with high accuracy by evaluating multiple parameters, such as foundation dimensions, depth factors, geometric variables, and relative density. Compared to the relations based on elasticity theory, the results of the suggested formulation are more realistic because they are derived from experimental data.
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 6: Two and Four Antennas at BS/20, 30, and 40 Users/MUSA Code Length of 4  
Figure 17 depicts a comparison between the two clustering approaches presented in Section 2, k-means and k-medoids, respectively. In both cases, BS with two antennas and BS with four antennas, respectively, the k-medoids method brings small benefits, regardless of the number of users (K = 20, 30, or 40). This confirms that the main limitation of the clustering approach is not the distance metric, but the fact that we do not assess  
Figure 16: BER vs. SNR for Scenario 5: (a) thresholds for K = 20; 40; 70; (b) new threshold for K = 40.  
Figure 15: BER vs. SNR for Scenario 4.  
the correlation between clusters when assigning the sequences to users. As expected, the overall BER performance increases when the BS is equipped with four antennas for both clustering methods.  
The results from all simulations are better summarized in Table 4.
## 3 Technical Briefing of M*
### MAPP Problem Definition  
In this problem, we have \(m\) agents indexed by the set \(I=\{1,\ldots,m\}\). Let the free configuration space of agent \(j\) be represented by the directed graph \(G^{j}=\{V^{j},E^{j}\}\). For any agent \(j\), graph \(G^{j}\) is the same. The joint configuration space, which describes the set of all possible states of the multi-agent system, is defined as the _tensor product_ of the graphs of all individual agents: \(G=G^{1}\times\cdots\times G^{m}\). \(G\) consists of a _joint vertex_ set \(V\) and a _joint edge_ set \(E\). As an example, in a 2-D joint configuration space given by the agents \(j\) and \(k\), the two 2-D joint vertexes \(v_{p}=(v_{p}^{j},v_{p}^{k})\) and \(v_{q}=(v_{q}^{j},v_{q}^{k})\) is connected by the joint edge \((e_{pq}^{j},e_{pq}^{k})\). Note that \(v_{p}^{j}\in V^{j}\) and \(e_{pq}^{j}\in E^{j}\). Let \(\pi^{j}(v_{p}^{j},v_{q}^{j})\) denote a sequence of joint vertexes, called a _path_ in \(G^{j}\) from \(v_{p}^{j}\) to \(v_{q}^{j}\). The cost of a path \(\pi(v_{p},v_{q})\) in \(G\) is defined as  
\[g(\pi(v_{p},v_{q}))=\sum_{j=1}^{m}g(\pi^{j}(v_{p}^{j},v_{q}^{j})), \tag{1}\]  
where \(g(\pi)\) is the sum of all edge costs involved in the joint path \(\pi\).  
The goal of MAPP is to find a collision-free path, which is optimal with respect to minimal cost, from the source configuration \(v_{s}=v_{s}^{1}\times\cdots\times v_{s}^{m}\) to the destination configuration \(v_{d}=v_{d}^{1}\times\cdots\times v_{d}^{m}\). To determine the collision between agents, a collision function \(\psi(v_{p})\) is defined to return the set of conflicting agents at \(v_{p}\).  
Most fundamental MAPP approaches use hard collisions, where no intersection is allowed between any two agents in terms of the occupation of any _resource_, such as a workspace. This implies that the capacity of each resource can support only one agent at a time (i.e., a collision happens immediately once agents intersect at any resource). Suppose we have a set of resources \(A=\{A_{1},\ldots,A_{L}\}\) requested by each agent in the multi-agent system, where \(A_{i}\) is defined as the set of resource of type \(i\) on all edges and vertexes in \(G\). \(A_{i}\) is a continuous set because only continuous resources are considered in the paper. A traditional hard-collision constrained MAPP problem is formulated as follows:  
\[\begin{split}\min_{\pi}&\;g\left(\pi\left(v_{s},v_{ d}\right)\right)\\ s.t.&\\ &\bigcup_{\forall i\
eq j\in I}\left(A_{k}(v_{p}^{j})\cap A_{k}(v _{p}^{j})\right)=\emptyset,\;\forall A_{k}\in A,\;\forall v_{p}\in\pi,\end{split} \tag{2}\]  
where \(A_{k}(v_{p}^{j})\) denotes the subset of resource \(A_{k}\) occupied by the agent \(j\) at the joint vertex \(v_{p}\). One state-of-the-art solver to this problem is M*, which uses the sub-dimensional expansion strategy to dynamically increase the dimensionality of the search space in regions featuring some agent collisions. M* enables a relatively cheaper graph search under the strict hard-collision constraint.
# Plasma-Activated Tap Water Production and Its Application in Atomization Disinfection
## 3 Experimental Results
### Aqueous RONS Measurement  
We measured the concentration of NO\({}_{3}^{-}\), NO\({}_{2}^{-}\), and H\({}_{2}\)O\({}_{2}\) in PATW and PADW as a function of plasma activation time (Figure 8). During the plasma activation process, we also measured the pH and conductivity of PATW and PADW (Figure 9).  
Figure 8: The concentration of aqueous RONS in PATW and PADW as a function of activation time. (a) NO\({}_{3}^{-}\), (b) NO\({}_{2}^{-}\), (c) H\({}_{2}\)O\({}_{2}\).  
Figure 7: Optical emission Spectroscopy of the plasma.  
The RONS concentrations in PATW and PADW were within 90% of each other. However, the pH and conductivity of PATW and PADW considerably differed. The concentration of NO\({}_{3}^{-}\) and H\({}_{2}\)O\({}_{2}\) increased, the pH decreased, and the conductivity increased with the treatment time, while the concentration of NO\({}_{2}^{-}\) increased first and then decreased. NO\({}_{2}^{-}\) accumulated in the PATW and PADW in the initial stage of plasma treatment. After 30 min of plasma treatment, due to the accumulation of more oxidizing substances, NO\({}_{2}^{-}\) reacted with hydrogen peroxide and ozone to generate NO\({}_{3}^{-}\) and peroxnitrite [32], and its consumption rate was greater than the production rate, resulting in a decrease in concentration.  
The differences of concentration of the three aqueous RONS between PATW and PADW were less than 5%, but unlike distilled water, due to the presence of various impurities in tap water, most of the acids produced during plasma treatment in the first 30 min were consumed by carbonates and biconates (707-935 mg/L CaCO\({}_{3}\)) [33], causing the pH value to drop from 8 to 7 during the first 30 min, and afterward, due to the generation and accumulation of hydrogen peroxide, nitric acid, and peroxnitrite, the pH value dropped from 7 to 2.5 during the last 30 min. The initial conductivity of PATW and PADW was 360 uS/cm and 0 uS/cm, respectively. The conductivity of the two treated liquids increased to \(\sim\)1800 uS/cm at 60 min (Figure 9b). NO\({}_{3}^{-}\) and NO\({}_{2}^{-}\) produced by underwater discharges increased the conductivity (Figure 8).
# On the Optimal Calculation of the Rice Coding Parameter
## 4 Performance on Different Datasets Using Single-Parameter Rice Coding
### Estimation of the Output Bit-Length  
We conjecture that an estimation of the minimum output bit-length after Rice coding can be derived from (9), by substituting the value of the Rice parameter given by the value provided in (20), as follows:  
\[\hat{f}(\boldsymbol{\mu},\boldsymbol{\sigma})=N\cdot\left[\log_{2}(\ln(2)\cdot \mu)+2+\left|\frac{\sigma}{2^{\ln(2)\cdot\mu}}\right|\right],\text{ where} \tag{27}\]  
\(\mu\) and \(\sigma\) are the mean and standard deviation of the samples to be encoded.  
Figure 4 shows the output bit-length found after using the algorithm proposed in this article for finding the minimum output length and the estimated output bit-length based on (27). The size of the points is proportional to the ratio of the output bit-length based on Shannon theorem and the optimal output bit-length after Rice coding.  
Figure 4: Estimation of the minimum bit-length after Rice coding when the first preprocessing method is used.  
A linear approximation was found with the equation \(f=1.126\cdot\hat{f}-9781.446\), with a standard deviation error of 0.056 and a \(t\)-value of 19.91 for the estimated slope value, for the input sequences after being preprocessed with the first preprocessing method.
## 4 Results and Discussion
### Verification of CFIP Model
#### 4.3.2 Multi-Frequency AE  
The observed angular dependency led us to conclude that the displacement fields generated by a gas leakage in the cylindrical geometry is \(2\pi\)-periodic: \(m=0\). Therefore, Equation (64) is rewritten as  
\[\begin{array}{l}u_{df}=P_{f}\Big{[}(t-\tau_{p})\Big{(}A_{0f}F_{df}^{1}+F_{ df}^{4}\delta_{dr}\Big{)}\\ +(t-\tau_{5})\Big{(}B_{0f}F_{df}^{2}+C_{0f}F_{df}^{3}+F_{df}^{4}\delta_{d \vartheta}\Big{)}\Big{]}e^{-i\omega t},\end{array} \tag{68}\]  
The displacement field, expressed by Equation (68), indicates that the AE wave is active if \(F_{df}^{4}\) is nonzero. From Equations (40), (42), (45), (49), (53) and (57), it can be found that \(F_{rz}^{4}\), \(F_{zz}^{4}\) and \(F_{\theta\theta}^{4}\) are nonzero, but \(F_{\theta z}^{4}\), \(F_{r\theta}^{4}\) and \(F_{z\theta}^{4}\) are zero. Considering that the nonzero \(F_{df}^{4}\) terms are proportional to \(1/\omega^{2}\), Equation (68) can be rewritten for multi-frequency waves as  
\[u_{df}(\omega_{1},\cdots,\omega_{n})=\sum_{i=1}^{n}W_{i}u_{df}(\omega_{i}), \tag{69}\]  
\[W_{i}=\frac{\omega_{i}^{2}}{\sum_{i}^{n}\omega_{i}^{2}}. \tag{70}\]  
Equation (70) obtains contributions with equal weights at the configured frequencies. Therefore, the observed weight of the \(i\) component \(\big{(}w_{\rm emp,\beta}\big{)}\) is compensated to Equation (70). The final formula of the AE wave is  
\[u_{df}(\omega_{1},\cdots,\omega_{n})=\sum_{i=1}^{n}w_{\rm emp,\beta}\frac{ \omega_{i}^{2}}{\sum_{i}^{n}\omega_{i}^{2}}u_{df}(\omega_{i}), \tag{71}\]  
where \(u_{rz},\ u_{zz}\) and \(u_{\theta\theta}\) are active and the others are inactive. Among these active displacements, \(u_{zz}\) is dominant (hereafter, referred to as \(u_{z}\)).
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 5 Discussion  
In the first scenario, employing the Amazon Kindle book dataset, our Bi-LSTM model demonstrated favorable performance metrics. Specifically, it achieved an Area Under the Curve value of 92%, indicating a high level of predictive accuracy. Additionally, the model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CF Technique** & **Sentiment Analysis Approach** & \(\alpha\)**Value** & **MAE** & **RMSE** \\ \hline \multirow{8}{*}{User-based} & & \(\alpha=0.3\) & 1.77 & 2 \\  & TF-IDF+SVM & \(\alpha=0.5\) & 1.89 & 2.08 \\  & & \(\alpha=0.7\) & 2.10 & 2.39 \\  & & \(\alpha=0.3\) & 1.16 & 1.34 \\  & Glove+BI-LSTM & \(\alpha=0.5\) & 1.13 & 1.31 \\  & & \(\alpha=0.7\) & 1.12 & 1.30 \\ \hline \multirow{8}{*}{Item-based} & & \(\alpha=0.3\) & 1.77 & 1.99 \\  & TF-IDF+SVM & \(\alpha=0.5\) & 1.96 & 2.16 \\  & & \(\alpha=0.7\) & 1.57 & 2.54 \\ \cline{1-1}  & & \(\alpha=0.3\) & 1.18 & 1.35 \\ \cline{1-1}  & Glove+BI-LSTM & \(\alpha=0.5\) & 1.25 & 1.45 \\ \cline{1-1}  & & \(\alpha=0.7\) & 1.54 & 1.80 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sentiment analysis techniques combined with CF using Kindle book reviews.  
Figure 13: Evaluation metrics values obtained for item-based CF technique using Amazon digital music dataset.  
achieved an accuracy rate of 93%, implying that it correctly classified 93% of the instances in the dataset. The F1-score, which measures the model's overall precision and recall, reached an impressive 94%. Moving on to the second scenario, where we utilized the Amazon digital music dataset, our Bi-LSTM model yielded slightly different performance results. Although the AUC value decreased to 78%, indicating a comparatively lower predictive accuracy compared to the first scenario, the model still demonstrated a satisfactory level of performance. The accuracy rate remained high at 94%, meaning that 94% of the instances were correctly classified by the model. Moreover, the F1-score reached an outstanding 96%, highlighting the model's exceptional precision and recall. Overall, these findings underscore the effectiveness of our Bi-LSTM model in both scenarios, albeit with some variations in performance metrics.  
The incorporation of model sentiment analysis into collaborative filtering has demonstrated notable enhancements in evaluation metrics when compared to conventional collaborative filtering methods devoid of sentiment analysis across various algorithms and all \(\alpha\) values examined. In the initial experiment, which employed a user-based approach, optimal performance was achieved by setting \(\alpha\) to 0.7 for both the Amazon Kindle book dataset and the Amazon digital music dataset. Conversely, in the subsequent experiment utilizing an item-based approach, the most favorable outcomes were observed when \(\alpha\) was assigned a value of 0.3 for both the Amazon Kindle book dataset and the Amazon digital music dataset.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
###### Abstract
In addition, [PERSON] [20; 21; 22] proposed to install helical static blades for the inducer, and he observed that the composite structure, called the axial-vortex stage, has a higher head radio and a better cavitation quality than the separate inducer. The helical static blade is a spiral cascade fixed on casing, with an opposite thread rotation direction to the inducer. More vortex flows appear at the periphery, which increases the complexity of fluid flow. [PERSON] [23] compared the cavitation performance of an inducer with and without helical static blades. It was found that the helical static blade channel helps to delay the time in which bubbles block the inducer channel. [PERSON] [24] analyzed the unsteady flow with the CFD method, and the results showed that when helical static blades are equipped, the pressure pulsation of blade passing frequency in the inducer outlet becomes lower. [PERSON] [25] studied helical static blades with different axial lengths as well, and he pointed out that the pressure coefficient increases almost linearly with the increase in the axial length. He also found low-frequency oscillations provoked by vortex disturbances at inlet, and he proposed to optimize the position and number of vanes of helical static blades. At present, there is little research on the influence of the radial height of helical static blades on the cavitation performance of inducers. In order to improve the cavitation quality of an inducer, further optimization of helical static blades is necessary. In this study, helical static blades with different radial heights are employed in the cavitation study of inducers, and the analysis of the results provides a theoretical reference for the design of helical static blades.