esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Types of Adsorbent
#### 6.6.7 Clay  
The negative charge on clay is due to the morphology of fine silicate which results in better adsorption values of clay. This anionic charge is balanced by positively charged species on the surface of clay and clay was able to adsorb cations, such as toxic metals. The expansive surface area of clay (up to 800 m\({}^{2}\)/g) increases the extraction capacity of clay [18]. Clay is found in three fundamental classes namely, kaolinite, micas (for example, illite), and semectites (for instance montmorillonite). Of the three species, montmorillonite clay has the smallest size of the crystal, more surface area, and maximum cation exchangeability. In this way, montmorillonite clay must possess a prominent adsorption capacity. [PERSON] et al. found that the elimination of mercury by montmorillonite was more prominent as compared to kaolinite [150]. [PERSON] et al., utilised clay wollastonite during his batch experiments. According to Langmuir isotherm, the maximal adsorption value shown by Wollastonite was 6.52 mg/g for nickel [151].  
[PERSON] et al. examined the sorption of chromium onto a 1:1 mixture of fly ash and wollastonite [152]. Oxides in the fly ash and wollastonite were responsible for the mixture's sorption potential. Chromium was sorbed as an anion of chromium (HCrO\({}_{2}\)\({}^{-4}\)) and for that, a cation was needed to attract the metal ions. Above pH 2.5, alumina, SiO\({}_{2}\) and CaO are negatively charge and maximum sorption took place at this pH. The extraction of negatively charged ions is more successful in strongly acidic conditions. Therefore, clay might be improved to enhance its adsorption capacity. [PERSON] et al. utilised bentonite, modified by substituting the natural exchangeable cations tetramethyl ammonium ion (TMA) to extract lead, chromium and the tetramethyl ammonium ions attached to resin has enhanced the clay sorption ability. It was found that 1 g of natural bentonite has an adsorption capacity of 6 mg for Pb\({}^{2+}\) ions and 55 mg for Cr\({}^{6+}\) ions, while tailored bentonite was adsorbed approximately 58 mg of lead ions and 57 mg of chromium ions [18].  
Figure 7: The mechanism of Cd (II) and Pb (II) ions adsorption in montmorillonite and zeolites by ion exchange method. Adapted with permission from Reference [149].  
[PERSON] et al. analyzed that acid treatment of modified natural bentonite decreases the sorption capacity of Cd and Zn, while high temperature treatment basically increased the sorption value whether bentonite was present in modified for or in the natural state [153].
## 3 Preliminaries: Network Model and Problem Definition  
### Cloud-Edge Collaboration Model for MEC Network  
As shown in Figure 1, the cloud-edge collaboration model can be divided into user layer, edge layer and cloud layer. The user layer is composed of m mobile devices used by all users in the service area, and it can be denoted by \(\mathit{UD}=\{ud_{1},ud_{2},\cdots ud_{m}\}\). User equipment may move locations and generate computing tasks at any time. The edge layer includes \(n\) edge nodes, which are represented as \(EN=\{en_{1},en_{2},\cdots en_{n}\}\). Each node consists of a wireless base station and an edge server.
# Volatile Profile of Nuts, Key Odorants and Analytical Methods for Quantification
## 5 Volatile Profile of Nuts after Thermal Treatments
### Novel Roasting Treatments  
Although hot air roasting is the most popular heat treatment of nuts, this operation is time and energetically expensive. As a consequence, new processing methods have been applied to reduce these drawbacks.  
In this context, microwave energy offers the advantages of speed of operation, energy savings, precise process control, and faster start up and shut-down times, compared with conventional heat processes. Microwaves have been used in the roasting of almonds [61], and pistachios [66]. Regarding almonds, microwave heating enhanced the production of volatiles compared to frying and air-hot heating, and the flavor was the most preferred by panelists. Although the main VOCs were similar to those obtained by hot air roasting, two additional compounds were reported. On the one hand, the Strecker aldehyde methionalcontributes vegetable and creamy aromas. On the other hand, the undecane and 1,4-butyrolactone were obtained from the lipid oxidation of the nut--the lactone contributes to milky and creamy aromas. With respect to pistachios, [PERSON] et al. [66] found that limonene, nonanal, and \(\alpha\)-pinene were the main components in microwave-roasted pistachios. In this sense, the treatment variables, i.e., time and microwave power, affected the volatile concentrations of the nut. In particular, the total concentration of volatiles increased with the time and power of microwave roasting.  
Radio frequency is a novel thermal processing technology with a frequency range of 3 kHz-300 MHz. Heat is generated inside the food products by molecular friction, owing to ionic conduction and dipole rotation during heating. Compared with microwave heating, radio frequency has a deeper penetration depth and a relatively uniform electromagnetic field distribution, which allows the bulk and thick materials to be heated more efficiently [67]. Recently, a roasting processing method of almonds has been reported by using the radio frequency technique for 15 min at 120-130 \({}^{\circ}\)C. As a result, similar major volatile compounds were obtained. Moreover, radio frequency roasting produced almonds with desired roasted flavor compounds at a lower roasting temperature.
## 6 Security Analysis
### Informal Security Analysis
With the obtained \(MD_{i}\) and \(SC_{i}\), it is also not possible for \(\mathcal{A}\) to spoof \(S_{m}\) with \(u_{m,i}=B_{m,i}\oplus A_{i,m}\), as \(\mathcal{A}\) does not know the \(ID_{i}\) for the check \(u_{m,i}\stackrel{{?}}{{=}}h(ID_{i}||prk_{m})\). If \(\mathcal{A}\) uses \(V_{i}\) obtained from \(SC_{i}\) for the verification, \(\mathcal{A}\) can also not compute a valid \(C_{i,m21}\) for the login request without \(ID_{i}\). Hence, my proposed protocol can fully prevent lost smart card attacks. _Resistance to impersonation attacks_: Suppose \(\mathcal{A}\) has obtained the identity \(ID_{i}\) and then uses it to compute a candidate login request for the purpose of impersonating \(P_{i}\). Due to the stated resistance to online and offline password guessing attacks, \(PW_{i}\) will not be revealed to \(\mathcal{A}\). Moreover, \(B_{i}\) is completely protected and possessed by \(P_{i}\) only; \(MD_{i}\) and \(SC_{i}\) are also carefully preserved to prevent them from being retrieved \(u_{m,i}\). Therefore, upon the obtained \(ID_{i}\), it is still not possible for \(\mathcal{A}\) to compute a correct login message \(\{C_{i,m1},C_{i,m2},T_{i,m}\}\). Thus, the proposed protocol can withstand impersonation attacks. _Resistance to insider attacks_: Each server \(S_{m}\) is accepted as trustworthy during the registration procedure because \(P_{i}\) registers their secret information to gain services from \(S_{m}\). No sensitive values are stored in \(S_{m}\)'s database after registration. Moreover, my protocol is also designed without unmasked biometrics database or plaintext password table required. Hence, the protocol can resist insider attacks. _Resistance to desynchronization attacks_: During the communication process, two acknowledgements \(u_{m,i}\) and \(\beta_{i,m}\) are generated for the verifications, which prevent user impersonation and server impersonation. These values will be deleted after the communication sessions are completed. \(P_{i}\) and \(S_{m}\) do not further store any redundant values after each authentication procedure finishes. Therefore, the proposed work completely withstands desynchronization attacks.
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 1 Introduction
Owing to the wide range of uses with numerous advantages of the laser assisted welding process, it is essential to update, understand, and model before implementing industrial applications. As a result, the major goal of this research is to construct a finite element based three dimensional model for laser (CW) dissimilar welding of AISI 316L and Ti6 Al4V with 2.5 mm thickness in butt weld configuration by using COMSOL Multiphysics codes. A three dimensional conical shape laser heat source model was considered. Weld geometry, mesh formation, transient temperature distribution prediction, development of temperature contours across the joint were accomplished. Individual specific and comparative temperature-distribution-based graphical representations of the two dissimilar welds (AISI 316L and Ti6 Al4V) were plotted and further analyzed for efficacious comprehension and a better understanding of the thermal mechanisms involved in the welding process.
## 3 Results and Discussion
### Effect of Immobilization and Freeze-drying on Cell Viability  
Table 1 shows the effect of freeze-drying on levels of free and immobilized _L. plantarum_ B282, _P. acidilactici_ SK, and _L. plantarum_ F4 cells on zea flakes, pistachios, and raisins along with cell survival rates. In all cases, freeze-dried cell loads were higher than the minimum recommended concentration (>7 logcfu/g) for conferring a health effect, according to IPA Europe [33].  
No cryoprotectants were used in this study, which are usually used during the freeze-drying of microbial cultures [34], in order to examine the possible positive effect of immobilization on the extension of the lifetime of the functional cultures.  
Freeze-drying resulted in a significant decrease (\(p\) < 0.05) of the cell levels of all strains, in free form. Similar results were also reported in the literature for _L. plantarum_ TISTR 2075 [35] and for _L. helveticus_ LH-B02 [36].  
The effect of freeze-drying on the levels of immobilized _L. plantarum_ B282, _P. acidilactici_ SK, and _L. plantarum_ F4 cells on the natural food carriers was also studied, which ranged >7 logcfu/g, in all cases. The maximum concentration (8.5 logcfu/g) was recorded in immobilized _P. acidilactici_ SK cells on pistachios. Immobilization of _L. plantarum_ B282 cultures on pistachios and _L. plantarum_ F4 on both zea flakes and pistachios resulted in similar (\(p\) > 0.05) cell loads compared to wet cells, and also higher survival rates (\(p\) < 0.05), compared to free cells. In contrast, survival rate of free and immobilized _P. acidilactici_ SK cells on zea flakes and pistachios ranged at similar levels (\(p\) > 0.05).  
Immobilization of cells on zea flakes or pistachios may protect cells from the stress to which they are subjected during the freeze-drying process and enhances cell survival, in a manner that depends on the food ingredient and on the microbial strain [37]. Similar results were obtained after immobilization of keir culture on grape skins that led to increased viability of lactobacilli compared to free cells, even when no cryoprotectant was applied during freeze-drying [32].  
In contrast, cell immobilization on raisins resulted in decreased (_p_ < 0.05) survival rates of _P. acidilactici_ SK and _L. plantarum_ F4 cells; however, no significant differences (_p_ > 0.05) were observed between free and immobilized _L. plantarum_ B282 cells. The reduced survival rates of immobilized cells on raisins may be due to the antimicrobial activity of specific fruit ingredients, mainly polyphenols [38].
## 5 Experiment and Analysis
### Computation and Results Analysis
#### 5.2.2 The k-Nearest Neighbor and Artificial Neural Network with Back Propagation
According to Figure 13, the testing accuracy is 96.44%, lower than the diagnostic accuracy of the CNN, showing the importance of the convolutional operation of CNN in feature extraction. Compared with Figure 10, it can be seen that the iterative process of the designed CNN model is more stable than that of the ANN-BP. To study the influence of the number of hidden layers and the number of nodes per layer on the performance of ANN-BP, we attempt to fine-tune the structure of the network in order to study its prediction performance. The three hidden layers of ANN-BP are denoted as _HL1_, _HL2_, and _HL3_, respectively. Firstly, the number of hidden layers is changed, including _HL1_, _HL2_, _HL3_, _HL1 HL2_, _HL2 HL3_, and _HL1 HL3_, and the prediction accuracy results are shown in Figure 14. Then, the number of nodes in each layer is changed, (i.e., _HL1_ is varied from 180 to 220, _HL2_ from 44 to 84, and _HL3_ from 12 to 52), and the prediction accuracy results are shown in Figure 15.  
Through analysis, we find that ANN-BP is sensitive to the number of network layers and the number of nodes in each layer, and the prediction accuracy does not reach 100%, meaning that the prediction accuracy of ANN-BP is less than that of the CNN proposed in this paper. To visualize the prediction results, we display the confusion matrix of ANN-BP in Figure 16. According to Figure 16, the precision of NM, DS, twisted rope (TR), BS-LR, BS-DR, and D-BR is 1, which is the highest  
Figure 13: The training and testing process of the artificial neural network with back propagation (ANN-BP) with the structure 784–200–64–32–9. The precision of the left broken rope (L-BR) is 0.77, which is the lowest value attained. The prediction results show that the main prediction errors occur among similar fault types. For example, among the 134 L-BR faults, 19 are predicted as the R-BR type and 12 are predicted as the D-BR type; among the 121 R-BR fault types, 6 are predicted as the D-BR type. The results show that ANN-BP and KNN have some deficiencies in distinguishing similar fault types, which is consistent with the hypothesis analysis in Section 5.1. Figure 16: The confusion matrices of ANN-BP. (**a**) Without normalization; (**b**) normalized. Figure 14: The performance of ANN-BP with varied of hidden layers (_HL_s). Figure 15: The performance of ANN-BP with varied of node numbers in the hidden layers.
## 5 Conclusions  
This paper combined DInSAR and MAI observations to reconstruct the 3D coseismic deformation associated with the 2017 Iraq Mw7.3 earthquake. From the 3D coseismic deformation filed, it can be found that the maximum coseismic displacements in U-D, N-S, and E-W direction are 100 cm, 50 cm, and 100 cm, respectively. The U-D components are the most violent, and the westward motion plays a major role in the horizontal motion. With the 3D displacements derived from the DInSAR and MAI measurements, it can be concluded that the Iraq earthquake predominantly resulted in reverse slip with small dextral slip component. Furthermore, fault inversions were performed based on the 3D and LOS coseismic deformation data sets, respectively. After comparison, we find that the 3D coseismic deformation data sets improved the inversions' accuracy by 30%. Our results reveal a deep dislocation on an NNW trending fault (the strike is 352.63\({}^{\circ}\)) extending about 60 km, along the fault dips 14.76\({}^{\circ}\) to the ENE. The estimated seismic moment is \(8.44\times 10^{19}\) Nm (Mw7.3), which is close to the solution provided by USGS (United States Geological Survey). The slip distributed at the depth  
Figure 9: The residual distribution histograms. (**a,b**) correspond the residual in Figure 7c,f, respectively. (**c,d**) correspond the residual in Figure 7i,l, respectively.  
between 12 and 18 km, and the peak slip of 6.53 appears at the depth of 14.5 km left near the epicenter. Further comparative analysis indicated that the inferred geodetic results derived from this study can be considered as the optimized results due the use of 3D displacements.  
Considering the geological structure in the earthquake region, locations of aftershocks and fault source-parameters, it comes to a preliminary conclusion that the ZMFF (the Zagros Mountain Front fault) is responsible for the earthquake. In total, this paper demonstrates the superiority of using the 3 Doseismic deformation fields on fault inversion.  
[PERSON] completed the algorithm implementation, validation and prepared the manuscript. [PERSON] proposed the basic idea and carefully revised the manuscript. [PERSON] provided constructive suggestions on modifying the manuscript.  
This work was jointly supported by the National Key Research and Development Program of China (No. 2017 YFB0502700), the National Natural Science Foundation of China (No. 41474003, No. 41601503), and the Fundamental Research Funds for the Central Universities (No. 2682016 CX087, No. 2682016 CY19).  
The authors declare no conflict of interest.
## 6 Discussion
### Limitation and Future Works  
This is an initial study that observes the differences in product information perceived by users according to a different information provision method in a VR environment, compared to traditional e-commerce. However, this study has several limitations.  
First, we designed and conducted an experiment with 28 participants to quantitatively compare the two environments. This allowed us to confirm the differences perceived by users in each environment, but it may not have been enough for an objective factual evaluation. Therefore, in future studies, it will be necessary to conduct experiments with more participants and have diverse demographic distributions.  
Second, there is the issue of dizziness that users may experience regarding the way product information is provided in VR shopping. In this study's experiments, there were cases where some participants complained of slight dizziness in the VR shopping environment. This is still a challenge for sustainable VR shopping regardless of the richness of product information perceived by users in VR shopping and the shopping experience. In addition to the hardware development of VR, we need to study the effect of VR shopping and its product information perception on users in future studies by using a method such as a VR room other than an HMD and methods that involve rotating products rather than rotating them according to the user's gaze in the VR environment.  
Resolving these issues is possible with advancements in VR device technology and commercialization. Furthermore, future research should not only focus on sneakers but also consider a variety of shoe products. It seems pertinent to explore how an expanded platform environment, which includes single product information along with \"photos of models wearing the products\" and \"price information\", affects consumers.
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 4 Results
### DL-Based Method and CM-Based Method Comparison  
The separation results between the DL-based method and CM-based method applied to the reconstructed dynamic dual-tracer noisy MLEM images were compared. For \(\Delta t\) = 0, 5, 10 and 5 min, Figures 8 and 9 show that in terms of visual assessment the CM-based method produces much nosier separated images than the DL-based method. This observation is consistent with better average NRMSE values of the DL-based separation results as illustrated in Figure 10. When using only 1 training pair, the DL-based method achieved high average NRMSE values across all \(\Delta t\) (\(\sim\)25% for \({}^{[18}\)F]FDG and \(\sim\)22% for \({}^{[11}\)C]MET). However, it nevertheless produces better average NRMSE values for \(\Delta t\) = 0, 5, 10 and 15 min when compared to the separation results using the CM-based method. In fact, there are 1282 = 16,384 TACs contained in 1 training pair, one for each voxel; hence, in this instance, the proposed network was trained using 16,384 TACs. Note that the DL-based method utilises both spatial and temporal information of the TACs from all voxels, but the CM-based method was implemented voxel by voxel, meaning only temporal information of one TAC was considered at each time. Therefore, for \(\Delta t\) = 0, 5, 10 and 15 min, the DL-based method with 1 training pair yields better separation performance. When \(\Delta t\) = 2 days, the CM-based method, in contrast to the DL-based method with 80 training pairs, achievedlower average NRMSE values for both \([^{18}\)F]FDG and \([^{11}\)C]MET. We believe this is the result of not enough training data which includes the case of \(\Delta t=2\) days.
## 3 Background
### Principles of the Classic RRT Algorithm  
The principle of the classic RRT algorithm is to use an initial point as the root node \(\text{q}_{\text{init}}\). In each subsequent expansion, a random node \(\text{q}_{\text{rand}}\) is randomly generated in the map. The nearest function selects a node \(\text{q}_{\text{nearest}}\) to \(\text{q}_{\text{rand}}\) based on the Euclidean distance on the existing random tree and expands this node in the direction of \(\text{q}_{\text{rand}}\) with a step distance through the extend function to obtain a new node \(\text{q}_{\text{new}}\). Evaluating whether \(\text{q}_{\text{new}}\) collides with an obstacle proceeds as follows. If it collides, growth is abandoned, \(\text{q}_{\text{new}}\) is removed, and a random node is regenerated; if there is no collision, \(\text{q}_{\text{new}}\) is added to the random tree, and \(\text{q}_{\text{new}}\)'s parent node is assigned to \(\text{q}_{\text{nearest}}\), as shown in Figure 2. When the distance between the node of the random tree and the target point is less than a specific value \(\text{m}\), the program is terminated, as a collision-free path from the starting point to the target point has been obtained. The above steps are followed until reaching the target point, as shown in Figure 3.  
Figure 1: Diagram illustrating the principle of the APF method.  
Figure 2: Classic RRT mind map.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 3 Solution Method
### Differential Evolution Algorithm
The differential evolution algorithm uses discrete crossover factors, including binomial crossover and exponential crossover. The crossover operator discretely crosses the mutation vector \(V_{i}\) generated by the mutation operator with the parent individual vector \(X_{i}\) to obtain the trial vector \(U_{i}\). The method of the crossover operation is as follows:  
\[U_{i,j}(g+1)=\left\{ \begin{array}{l}V_{i,j}(g+1)\ if\ rand(0,1)\leq CR\\ x_{i,j}(g)\ otherwise\end{array} \right.. \tag{21}\]  
Here, \(CR\) is called the crossover probability, and it has a range of \([0,1]\). It is used to generate new individuals with a certain probability. (4) Selection  
The selection step in the differential evolution algorithm uses a greedy strategy, where the fitter individuals are chosen as new individuals after the offspring population is generated through the mutation and crossover operators. This is done using a binary tournament selection operator, where each offspring competes with its corresponding parent, and the one that is more fit is selected to be included in the next generation's population. For minimization problems, the selection operator can be described as follows:  
\[X_{i}(g+1)=\left\{ \begin{array}{l}U_{i}(g+1)\ if\ f(U_{i}(g+1))\leq f(X_{i }(g))\\ X_{i}(g)\ otherwise\end{array} \right.. \tag{22}\]  
Because the differential evolution algorithm uses one-to-one tournament selection, it is a steady-state evolution algorithm that preserves elite individuals. Once a new population is formed, the differential evolution algorithm continues to evolve the population through mutation, crossover, and selection operators until the termination condition is met, and the program is exited. The algorithm of DE for solving the calibration of the link performance function is summarized as follows (Algorithm 3).