esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 5 6G RF Spectrum Technologies  
One of the key features of next-generation mobile communications is the transmitting frequency spectrum. Mobile networks have demonstrated remarkable growth over the years, particularly in terms of spectrum resources (for every generation), given the rising demands for higher speed or data transfer rates. As the development of 6G is expected to offer significantly higher bit rates (up to Tbps), operation at higher frequencies for the available spectrum and bandwidth is expected. Both VLC and THz are spectra with highly promising prospects to realize the vision of 6G [49].  
Figure 3 presents the two main 6G spectrum technologies, while Table 2 summarizes the different technologies of spectrum communication for 5G and 6G, including mmWaves, THz, and VLC.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Traffic Offloading by Multi-Receiver  
Sometimes, several nodes will subscribe to same content. There would be many subscriptions for a common content in CSS with different deadlines. Let _subscriber ratio_ be the percent of the subscribers of a common content. While a content is routed to the subscriber, some receivers might be subscribers to this content. Once a node receives the subscribed content, it repeals its subscription for this content. Let _offloading efficiency_ represent the ratio of the traffic offloaded from cellular networks to the total traffic includes offloaded traffic and cellular network traffic:  
\[offloading\ efficiency=\frac{traffic\ offloaded\ from\ cellular\ networks}{total\ traffic} \tag{2}\]  
For example, node_2 to node_7 subscribed to video_1 as Figure 4 shows. Node_1 gets video_1 through a cellular network. Node_1 contacts CSS and knows that node_7 subscribed to video_1 many days ago. After comparing \(d_{max}\) and \(d_{ps}\), node_1 decides to send video_1 to node_7. When node_1 encounters node_2 and node_3, it sends video_1 through a direct WiFi connection to node_2 and node_3 that also have subscribed to video_1 on CSS. After these two nodes receive video_1, they repeat their subscriptions to video_1 on CSS. However, as they are not the destination of the content delivery, they continue to transmit it to other nodes, and node_2 to node_7 receive video_1 before the deadlines of their subscriptions. In this case, only when node_1 downloaded video_1 through the cellular network, 6/7 cellular network traffic is offloaded onto an opportunistic network and the offloading efficiency is 85.7%.  
Figure 3: An example of subscribing content and opportunistic publishing content in DOPS.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 4 Algorithm Design and Description
### Algorithm Description  
According to the basic steps of the FQGA-SR algorithm, taking the substrate network \(G_{sn}\), migration requirements \(R\), chromosome qubit coding length \(K\), and population size \(M\) as the output, the FQGA algorithm can be used to obtain the optimal migration strategy \(X_{b}\), where \(X_{b}=\{x_{1},x_{2},\ldots,x_{i},\ldots x_{K}\}\) represents the VNF state of the node after service migration and \(x_{i}\) is where the VNF \(f\in F\) department is deployed on the substrate node \(n_{i}\in N_{sn}\).  
For ease of understanding, the pseudo code of the main steps of FQGA-SR algorithm is shown in Table 2. The algorithm divides the service migration problem into two parts: the migration node selection stage and the shortest migration path selection stage. Among them, line 3 is used to obtain the shortest path matrix \(\mathbb{S}\), line 4 is used to initialize the model parameters of FQGA, lines 5-9 are the population evolution process, and lines 10-13 are the mutation process of FQGA.  
In addition, combined with the computing power of the cloud computing center and edge nodes of the ground management control center, the migration strategy optimization algorithm based on FQGA runs on the control plane of the STIN to collect the whole network state information in real time through the GEO satellite controller. It can quickly and effectively obtain the optimal migration decision, realize VNF migration, and reallocate network resources for the migrating nodes and related links, to complete the service migration and refactoring steps.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Models
#### 2.4.2 Extreme Gradient Boosting (XGBoost)  
Extreme Gradient Boosting is built on the gradient boosting framework, where models are trained sequentially [130]. Each new model corrects the errors made by the previous models. This is achieved by optimizing a loss function using gradient descent. The regressor combines the predictions of multiple weak learners, typically decision trees, to create a strong predictive model. Each tree is built to reduce the residual errors of the previous trees [131]. It employs multiple additive functions to generate the following prediction [132].  
\[p_{i}=p_{i}^{0}+n\sum_{a=1}^{k}f_{a}(X_{i}) \tag{2}\]  
where \(p_{i}\) represents the predicted value for the \(i\)th sample with the feature vector \(X_{i}\), \(k\) is the number of estimators, and each estimator \(f_{a}\) (with \(a\) from \(1\) to \(k\)) corresponds to an independent tree structure. The initial guess \(p_{i}^{0}\), is the mean of the observed values in the training set. The learning rate \(\eta\) (also known as the shrinkage parameter) adds a new tree while preventing overfitting.  
Unlike traditional gradient boosting, XGBoost includes a novel regularization technique to prevent overfitting and enhance model robustness. This technique adds an extra term to the loss function, \(\mathcal{L}(\varphi)\), allowing for quicker and more efficient model tuning [118].  
\[\mathcal{L}(\varphi)=\sum_{a}L(r,p_{i})+\sum_{b}f(c) \tag{3}\]  
where \(L(p_{r},p_{i})\) represents the actual loss between the real values \(r\) and the predicted values \(p_{i}\) with \(f(c)\) being a regularization function that defines the complexity of the model. XGBoost has also been widely used in the prediction of conventional concrete properties like compressive strength [133; 134], slump [135] and deflection [136]. It has also been used in concrete printing [118]; however, with an increased dataset, XGBoost models' performance improves in three keyways. First, it mitigates variance by providing more information, making the model less susceptible to randomness in the training data. Second, the broader range of patterns encountered during training with a larger dataset enhances the model's generalizability to unseen data. Finally, the abundance of data facilitates XGBoost ensemble of decision trees in capturing more complex relationships between the features, leading to improved performance. The tuned hyperparameters for XGBoost are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**Tuning Range**} & \multicolumn{3}{c}{**Tuned Value**} \\ \cline{3-4}  & & **BR** & **DGAN** \\ \hline \multirow{3}{*}{hidden\_layer\_sizes} & [(50, 50), (100, 100), \\  & (100, 100, 100), \\  & (200, 200, 200)] & (200, 200, 200) \\ \cline{1-1} alpha & [0.0001, 0.001, 0.01] & 0.0001 \\ max\_iter & [400, 600, 800, 1000] & 400 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters for ANN models.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 2 SmartBAN Specifications
### Medium Access Control Layer  
The SmartBAN superframe, called the interbeacon interval (IBI), was divided into three channel access periods, as shown in Figure 2: the scheduled access period (SAP), the control and management (C/M) period and the inactive period. SmartBAN provides three different mechanisms. The SAP was used only for data frame transmission and utilizes time division multiple access (TDMA). On the other hand, the C/M period was basically introduced for control and management information between the nodes and the hub, and it utilized slotted ALOHA channel access. The C/M period was available for all the nodes that were ready to transmit the data frame. A node started as a slotted ALOHA channel access session by transmitting in the C/M period with a contention probability (CP) determined by the user priorities defined in Table 2. The range of CPs for different user priority levels is listed in Table 3. At the beginning of each time slot in the C/M period, a node chose its CP as follows:  
1. The user priority of its traffic was determined according to Table 2;
2. The range of its CP based on the user priority of its traffic was obtained according to Table 3;
3. If the node had successfully completed or had not begun a slotted ALOHA channel access session previously, its CP was set to \(CP_{\text{max}}\);
4. If the node did not successfully complete a slotted ALOHA channel access session in the last attempt: * If it had failed \(m\) times consecutively, where \(m\) is an odd number, it kept its unchanged CP;
* In the event that it had failed \(n\) times consecutively, where \(n\) is an even number, it halved its CP if the CP was greater than or equal to 2 \(\times\)\(CP_{\text{min}}\) or kept its CP unchanged otherwise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol Rate \(R_{s}\)** & & & **Data Rate** \\
**(Mega-Symbol/sec, Msps)** & **Coding Rate** & \(N_{R}\) & **(Mbit/sec)** \\ \hline
1.0 & \(1\) & \(1\) & \(1.0\) \\
1.0 & \(1\) & \(2\) & \(0.5\) \\
1.0 & \(1\) & \(4\) & \(0.25\) \\
1.0 & \(113/127\) & \(1\) & \(0.89\) \\
1.0 & \(113/127\) & \(2\) & \(0.44\) \\
1.0 & \(113/127\) & \(4\) & \(0.22\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: PHY throughput.  
Figure 2: Access periods in the data channel (DCH).  
\(T_{\text{s}}\) and \(T_{\text{D}}\) are the durations of the time slot and the interbeacon interval, respectively. \(N_{\text{s}}\) and \(N_{\text{CM}}\) represent the number of available time slots in the SAP and the C/M period, respectively. For the busy channel case, the SmartBAN defines a back-off mechanism for a transmission reattempt. Interframe spaces (IFSs) were provided to separate the data and acknowledgment (ACK) frames.
# Novel Polymer Sorbents with Imprinted Task-Specific Ionic Liquids for Metal Removal
## 3 Results and Discussion
### Sorption Studies
#### 3.2.3 Effect of Metals Ions Concentration  
The influence of the initial metal ion concentration on their removal with the sorbent series VBC and VBBr was also investigated. The experiments were conducted for 3 h of shaking at a pH of 5; at T = 298.15 K; and using the aqueous feed solution containing from 50 to 200 mg/L of Cu(II), Cd(II), and Zn(II). From the results presented ion Figure 7, it can be seen that the removal of metals ions is strongly dependent on their initial concentration in the water phase and on the structure of the pyridinium group located on the sorbents surface.  
In the case of the Cu(II) sorption and almost all sorbent series VBC, the metal ions removal increases with the increase in the initial Cu(II) concentration but without saturation achieving. Only for VBC-Ox4.10, the increase is insignificant (Q\({}_{\rm e}\) increases from 31 to 34 mg/g using the aqueous feed solutions at 50 and 200 mg/L, respectively), which suggests that Q\({}_{\rm e}\) = 34 mg/g is the maximum loading. An insignificant increase in the Q\({}_{\rm e}\) value is also observed using VBC-K3.10 as the sorbent and the aqueous feed solutions  
Figure 7: Effect of initial concentration of Cu(II), Cd(II), and Zn(II) on their sorption with VBC and VBBr series sorbents (M(II) = 50–200 mg/L, pH = 5, time: 180 min, sorbent dosage—0.1 g, and V—100 mL).  
containing 150 and 200 mg/L of Cu(II). In this case, the results indicate that the value 47 mg/g is slightly off the saturation level. The sorbent series VBBr shows much worse sorption properties towards copper(II). Additionally, the use of aqueous solutions with increasing metal ion concentrations indicates low saturation. The studiedorbents behave differently in contact with the water phase containing cadmium ions. The VBBr series s  
From the presented results, it can be concluded that the sorption of Cu(II) does not change remarkably with the change in temperature. A similar effect is observed for the Zn(II) sorption with the sorbents obtained by the modification with Ox4.10. In contrast, in the case of the Cd(II) sorption and other Zn(II)-VBC and Zn(II)-VBBr systems, the increase in temperature makes the sorbents more efficient. The effect is significant when using the VBC and VBBr series materials as sorbents of Cd(II). For instance, the sorption of Cd(II) with VBC-K3.10 increases by almost 100% if the temperature is changed from 25 to 45 \({}^{\circ}\)C. In the case of the zinc(II) sorption, the effect is particularly evident using the VBBr-K3.10 and VBBr-Ox3.10 resins, which were found as the least efficient sorbents of Zn(II). The increase in the sorption may result from the increased diffusion rate of the adsorbate ions through the surface boundary layer, especially if the hydrophobic nature of the surface was found. This increase may also be due to changes in the availability of the active sites capable of coordinating with the metal ions. Here, the temperature can cause isomerization changes in the oxime C=N or conformational changes in the decile chain.
## 4 Conclusions  
The comparative performance of FeSO\({}_{4}\) and Fe(0)-catalytic Fenton oxidations were assessed in terms of the optimum dose requirement, removal efficiency of the micropollutants, toxic effects, sludge produced after the treatment and the statistical significance for its robustness. In all mentioned categories, the Fe(0) catalytic Fenton process showed better performance. The major conclusions of this study are highlighted below:  
* Fe(0) required less dosage of H\({}_{2}\)O\({}_{2}\); the optimised molar ratio of H\({}_{2}\)O\({}_{2}\):Fe(0) for zero valent iron was 0.7:1 whilst for H\({}_{2}\)O\({}_{2}\):FeSO\({}_{4}\) it was 2.7:1.
* Both treatments completely degraded the chosen MPs (gabapentin, sulfamethoxazole, diuron, terbutryn and terbuthylazine). However, Fe(0) could remove 70% DOC when FeSO\({}_{4}\) removed only 45% for the given reaction conditions.
* The sample toxicity after the Fe(0)-catalytic Fenton treatment was completely removed, while the FeSO\({}_{4}\) Fenton treatment showed a slight toxicity.
* The heavy sludge was produced in the effluent after the FeSO\({}_{4}\) catalytic Fenton process, whereas, the Fe(0)-catalytic Fenton process produced much less sludge after the treatment.
* The Taguchi method/analysis could be used to select and obtain the optimal operating conditions for the Fenton reactions.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/11/2181/s1](http://www.mdpi.com/2076-3417/9/11/2181/s1)]([http://www.mdpi.com/2076-3417/9/11/2181/s1](http://www.mdpi.com/2076-3417/9/11/2181/s1)). Figure S1. Micro-pollutants (MPs) degradation at optimal experimental conditions. Figure S2. Regression plot of actual vs predicted SN ratio obtained after the Taguchi analysis for the (a) FeSO\({}_{4}\) treatment (R2 = 0.956) and (b) Fe treatment (R2 = 0.996). Table S1. Taguchi array L16 experimental layout.  
**Author Contributions:** Conceptualization, J-Q.J. and A.G.; methodology, J-Q.J. and A.G.; software, A.G; validation, J-Q.J. and A.G; experiments, A.G; writing--draft preparation, A.G; writing--manuscript's review, re-structure, correction and editing J-Q.J.; visualisation, J-Q.J. and A.G; supervision, J-Q.J; funding acquisition, J-Q.J.  
**Acknowledgments:** The authors are grateful to Glasgow Caledonian University (GCU) who sponsored a research studentship to [PERSON] for the PhD study. We also appreciate technicians' support and [PERSON] for LC/MS analysis.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Methodology
### Dynamic Resource Scheduling and Task Offloading Based on Value Iteration
#### 4.1.1 System State and Decision Variables  
Let the system state at time \(t\) be denoted by \(s_{t}\in\mathbb{S}\), where \(\mathbb{S}\) represents the state space. The system state encompasses each task's computational demand, each node's resource status, and link delays.The decision variables for resource allocation are represented by \(a_{t}\in\mathbb{A}\), where \(\mathbb{A}\) is the set of all possible resource allocation actions. The task offloading decision aims to minimize maximum delay and maximize resource utilization efficiency. Therefore, we define an appropriate objective function and constraints for optimization.  
Specifically, the decision variable \(a_{t}\) includes the task \(j\) assignment to node \(i\) represented by the offloading matrix \(X_{ij}\):  
\[X_{ij}(t)=\begin{cases}1&\text{if task $j$ is offloaded to node $i$ at time $t$},\\ 0&\text{otherwise}.\end{cases} \tag{13}\]
## 4 Application
### Non-Clinical Application  
In nonclinical sectors such as home health care, fitness monitoring, sleep monitoring, polygraph, living skin detection, stress monitoring, driver monitoring, security, war zone, natural calamity, and animal research, the iPPG may play an important future role by monitoring vital signs.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development  
For bare soil and vegetation, the random surface scattering model and first-order radiative transfer (RT) model are employed to calculate geophysical parameters [23; 24]. On 5 July 2021, the Chinese meteorological satellite FY-3E was successfully launched. It carries 11 payloads, one of which is the GNSS occultation and GNSS-R combined payload. In conjunction with the FY-3E GNSS-R mission, our land surface GNSS-reflection simulator (LAGRS) is also employed to obtain the polarization characteristics of surface objects [25]. However, it should be noted that owing to the characteristics of GNSS-R, the scattering models suitable for conventional microwave remote sensing must be improved. This section introduces methods for such improvements, together with details of the developed LAGRS.
## 2 Materials and Methods
### Methods
#### 2.3.5 Accuracy Assessment  
The proposed spatiotemporal change detection method as well as the single-date image classification method were evaluated for the five field sites. For the spatiotemporal change detection, all of the 203 wilded pine trees can be used for validation, while for the single-date classification, 60% of the samples at Sites C and D were already used for training so the rest wilted pine trees were selected as validation data. To match the feature points recorded in field survey with the \(BB\)s generated by detection algorithms, referring to [PERSON] et al. (2017), we defined true positive, omission and commission errors according to the following rules: true positive is defined as wilted pine trees falling within the \(BB\)s; when an actual wilted pine tree does not fall in any \(BB\), it is recorded as an omission error; when there is no actual wilted pine in a \(BB\), it is defined as a commission error (Figure 5c). The producer's accuracy was defined as the proportion of the number of wilted pine trees correctly detected to the number of actual wilted pine trees, and the user accuracy was defined as the proportion of the number of \(BB\)s containing actual wilted pine trees to the total number of \(BB\)s in the detection results.  
Figure 5: Schematics showing (**a**) the wilted pine tree pixels masked by the threshold \(\alpha\), (**b**) the candidate bounding boxes (\(BB\)s) converted from the pixel-based result, and (**c**) the omission and commission errors defined based on the locations of wilted pine trees and \(BB\)s.
## 5 Applications in Control Theory
### Control of Chaos  
In this section, we consider three aspects of the control of chaos: stabilization, adaptive and predictive control methods, and the identification of chaotic systems.
## 4 Constitutive Model and Finite Element Implementations
### Main Equations  
1. We postulated that total strain \(\varepsilon\) was partitioned into elastic part \(\varepsilon^{\varepsilon}\) and plastic part \(\varepsilon^{p}\): \[\varepsilon=\varepsilon^{\varepsilon}+\varepsilon^{p}\] (4)
2. Elastic strain followed the generalized [PERSON]'s law: \[\sigma=C:(\varepsilon-\varepsilon^{p})\] (5) where \(\sigma\) was the stress tensor, \(C\) was the fourth-order elasticity tensor.
3. The material obeyed the [PERSON] yield criterion and the yield function could be expressed as  
Figure 8: Three-dimensional ratcheting response surface.  
\[F=\sqrt{\frac{3}{2}(s-\alpha):(s-\alpha)}-\sigma_{y} \tag{6}\]  
where \(s\) was the bias stress tensor, \(\alpha\) was the back stress tensor, and \(\sigma_{y}\) was the yield stress.
4. Plastic flow rate was written as the following: \[de^{p}=\sqrt{\frac{3}{2}}\lambda\frac{s-\alpha}{\|s-\alpha\|}\] (7) where \(\lambda\) was the plastic multiplier, which could usually be replaced by the cumulative plastic strain rate \(dp\). \[\lambda=dp=\sqrt{\frac{2}{3}}de^{p}:de^{p}\] (8)
5. [PERSON] [44] combined the A-F model and the OW-II model and derived the formula as follows: \[\begin{array}{l}\alpha=\sum\limits_{i=1}^{M}\alpha_{i}\\ d\alpha_{i}=\gamma_{i}\bigg{[}\frac{2}{3}r_{i}de^{p}-\mu_{i}\alpha_{i}dp- \bigg{(}\frac{\overline{\alpha_{i}}}{r_{i}}\bigg{)}^{m_{i}}\bigg{<}de^{p}: \frac{\alpha_{i}}{\overline{\alpha_{i}}}-\mu_{i}dp\bigg{>}\alpha_{i}\bigg{]} \end{array}\] (9)  
where \(\alpha_{i}\) was the back stress component, \(\overline{\alpha_{i}}\) was the modulus of the back stress component, and \(\overline{\alpha_{i}}=\sqrt{\frac{3}{2}\overline{\alpha}_{i}:\alpha_{i}}, \gamma_{i},r_{i},m_{i}\) and \(\mu_{i}\) were the material parameter.
6. The isotropic hardening evolution was described in a classical way in the following: \[\sigma_{y}=\sigma_{0}+R\] (10) \[dR=b(Q-R)dp,R(0)=0\] (11)  
where \(\alpha_{0}\) was the initial yield limit, \(R\) was the increment of isotropic hardening, i.e., the increment of the radius of the yield surface. \(b\) and \(Q\) were material parameters. \(Q\) was the saturation value of the isotropic resistances, and \(b\) was related to the rate at which \(R\) reached the saturation value, which described the cyclic hardening behavior and ratcheting behavior of SAF2507 stainless steel when \(Q\geq\sigma_{0}\).
## 2 Grape Composition
### Grape-Derived Aroma Compounds
#### 2.1.1 Terpenoids  
Terpenoids, among the various classes of grape-derived aromas, are the most studied volatile compounds. Terpenoids are grouped according to their carbon numbers into hemiterpenes (C\({}_{5}\)), monoterpenes (C\({}_{10}\)), sesquiterpenenes (C\({}_{15}\)), diterpenes (C\({}_{20}\)), and tetraterpenes (C\({}_{40}\)), with monoterpenes (C\({}_{10}\)) as the dominant class [4; 29; 30]. Grapes are categorized into Muscat, non-Muscat aromatic, and neutral varieties based on their monoterpene concentration levels [24]. Monoterpenes are synthesized through the mexulonic acid (MVA) pathway and the methylerythnitol phosphate (MEP) pathway from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Thereafter, through the activity of terpene synthases (TPS), monoterpenes are formed from 2-(E)-geranyl diphosphate (GPP) [31]. However, among the two biosynthetic routes, the MEP pathway is said to be the prime route for the formation of terpenoids in grapes [32]. Terpenoids are stored as free and bound volatiles, mainly in the grape skin, with trace concentrations in the pulp [4; 30]. Climate, management practices such as grape shading, elicitation, and many other factors (e.g., pruning, irrigation, fertilization) influence the concentrations of terpen, as reported in the literature [33; 34; 35]. For instance, concentrations of monoterpenes in Sauvignon blanc grapes decreased with high canopy density [36], while the concentration of these molecules increased when Sauvignon blanc grapes were exposed directly to the sun after leaf removal [37].