esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 1 Introduction
It was further noted that ambient temperature (23 \(\pm\) 1 \({}^{\circ}\)C) curing does not achieve a final setting for 100% low calcium fly ash based geopolymer concrete, with a number of researchers studying the activation of low calcium (class F) fly ash by alkalis utilizing heat curing in the range 60-80 \({}^{\circ}\)C [18; 19; 20; 21]. [PERSON] et al. [22] reported that micro silica-fly ash blended geopolymer concrete prepared using a hybrid solution of sodium silicate and sodium hydroxide cured at 80 \({}^{\circ}\)C for 24 h and 7 days at ambient temperature (23 \(\pm\) 1 \({}^{\circ}\)C) is thermally stable up to 800 \({}^{\circ}\)C and that the compressive strength obtained satisfied the requirement of building material with high-temperature resistance in modern construction and the ceramic industries. [PERSON] et al. [23] examined the technical feasibility of geopolymer synthesis from the coal fly ash with high iron oxide and calcium oxide contents, and concluded that it is possible to improve the economics of geopolymer production by varying the material use while not impairing the performance of geopolymer. The curing temperature and curing time for geopolymer concrete have been reported to vary significantly based on the source of the fly ash. [PERSON] et al. [24] used heat curing of 80 \({}^{\circ}\)C temperature for 24 h to produce geopolymer concrete using low calcium, Class F fly ash obtained from four different power stations in Australia. [PERSON] al. 2013 [25] produced geopolymer concrete using high calcium, Class C fluidized bed combustion fly ash, cured at 60 \({}^{\circ}\)C for 24 h. [PERSON] et al. [26] showed that the 28-day compressive strength of 15-35 MPa can be obtained using alkali-activated high calcium fly ash concrete cured at ambient temperature (23 \(\pm\) 1 \({}^{\circ}\)C). Further, [PERSON] et al. [27] used three high calcium, Class C fly ashes obtained from power plants around the United States in order to produce geopolymer concrete while it cured for 3 days at 60 \({}^{\circ}\)C. [PERSON] et al. [28] produced geopolymer concrete using law calcium Class F fly ashes cured at 65 \({}^{\circ}\)C and 85 \({}^{\circ}\)C for 24 h, and showed the importance of heat curing, where a significant increase in strength was observed at 85 \({}^{\circ}\)C as compared to 65 \({}^{\circ}\)C. Moreover, [PERSON] [17] developed geopolymer concrete using two Australian low calcium Class F fly ashes cured at 100 \({}^{\circ}\)C for 24 h. In the case of brown coal fly ash,Dirgantara et al. [15] observed the alkali activation of brown coal fly ash obtained from La Trobe Valley, Victoria, Australia required heat curing of 120 \({}^{\circ}\)C for 24 h to optimize the strength development and that the application of lower curing temperatures gave significantly lower compressive strengths.
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment
### Data Layer Connectivity (On Top of Network Layer)  
Once the device-to-device connectivity has been appropriately configured for all the available interfaces (Section 3.2), the goal is now to transmit data link layer traffic over the network layer (i.e., Ethernet traffic over an IP network). Overlay networks are used for this purpose.  
An overlay network is a virtual network of logically linked nodes built on top of one or more underlying networks. The overlay network nodes are connected by virtual links to implement network services that are not available in the underlying network(s). The reason for using overlay networks in this scenario is because they provide isolated domains, i.e., the traffic transmitted over the overlay is isolated from any other traffic going over the network, allowing for control and data traffic differentiation (e.g., VPN, OLSR, network monitoring app). Furthermore, creating an overlay requires no additional configuration of the intermediate nodes.  
In our case, we use Virtual Extensible LAN (VxLAN) [27], an encapsulation protocol designed for this purpose. In this way, traffic entering a VxLAN tunnel exits at the tunnel's other end(s) as if both devices were on the same Local Area Network (LAN). The establishment of virtual point-to-point links (VxLANs) through a layer-3 VPN has been validated in the context of the H2020 European project 5G-ZORRO, where authors of the paper are involved.
###### Abstract  
Polymer membranes often face challenges of oil fouling and rapid water flux decline during the separation of oil-in-water emulsions, making them a focal point of ongoing research and development efforts. Coating PVDF membranes with a hydrogel layer equips the developed membranes with robust potential to mitigate oil fouling. However, developing a controllable thickness of a stable hydrogel layer to prevent the blocking of membrane pores remains a critical issue. In this work, atmospheric pressure low-temperature plasma was used to prepare the surface of a PVDF membrane to improve its wettability and adhesion properties for coating with a thin hydrophilic film of an AM-NaA copolymer hydrogel. The AM-NaA/PVDF membrane exhibited superhydrophilic and underwater superoleophobic properties, along with exceptional anti-crude oil-fouling characteristics and a self-cleaning function. The AM-NaA/PVDF membrane achieved high separation efficiency, exceeding 99% for various oil-in-water emulsions, with residual oil content in the permeate of less than 10 mg/L after a single-step separation. Additionally, it showed a high-water flux of 5874 L/m\({}^{2}\)-h for crude oil-in-water emulsions. The AM-NaA/PVDF membrane showed good stability and easy cleaning by water washing over multiple crude oil-in-water emulsion separation and regeneration cycles. Adding CaCl\({}_{2}\) destabilized emulsions by promoting oil droplet coalescence, further boosting flux. This strategy provides a practical pathway for the development of highly reusable and oil-fouling-resistant membranes for the efficient separation of emulsified oily water.  
surface modification; PVDF membrane; plasma treatment; copolymer hydrogel; antifouling; demulsification Article
## 2 Methods
### Proposed De-Noising Method for Complex Noisy PD Signal
Figure 5: Discrete Fourier transformation (DFT) power spectrum of different components in noisy pulse. As shown in Figure 6c,d, the de-noising PD signal extracted by the proposed method combined adaptive VMD and adaptive wavelet packet decomposition maintains a high degree of similarity with the original signal in amplitude and phase. In order to analyze the results quantitatively, evaluation parameters such as NCC, SNR, and variational trend parameter (VTP) [22] is calculated in Table 3. VTP represents the similarity of the two waveforms' changing trends, and measures the oscillation of the waveforms to a certain extent. It makes up for the deficiency of NCC in microscopic description of waveform. The closer VTP is to 1, the more similar the oscillations of the two waveforms are. Figure 6: De-noising results of the proposed method. (**a**) Result of adaptive variational mode decomposition (VMD). (**b**) Further result of adaptive wavelet packet de-noising. (**c**) Comparison of de-noising and real PD signal of pulse 1. (**d**) Comparison of de-noising and real PD signal of pulse 2. From the results shown in Table 3, the extracted PD signals maintain a high degree of similarity with the real signal, the similarity is as high as 0.9977. The waveform detail variational trends also maintains a high degree of consistency, which is up to 1.0485. The SNR after noise suppression reaches the highest level of 14.1 dB, compared with \(-\)21.4 dB before de-noising. All these results indicate that adaptive VMD combined with adaptive wavelet packet decomposition method can effectively suppress periodic narrowband and white noise interference. The specific steps of the proposed de-noising method are given as follows, and the overall flow of our method is shown in Figure 7.  
Step 1: Use adaptive VMD algorithm to decompose periodic narrowband signal, white noise signal and partial discharge signal into different intrinsic modal components. Step 2: Remove the component with the larger power according to the DFT power spectrum, and synthesize the remaining components. Step 3: The signal obtained in Step 2 is further decomposed by adaptive wavelet decomposition to decompose the white noise into high, medium, and low frequencies. Step 4: According to the wavelet threshold, remove white noise, synthesize the remaining components as the extracted PD signal.
## 2 Theoretical Background
### Complex Dielectric Permittivity
On the other hand, the second term, characteristic of materials containing free charges, represents the losses due to conductivity [22] and it is dependent on frequency (\(f\)) and direct-current (or \"zero frequency\") electrical conductivity (\(\sigma_{dc}\)). Here, \(\varepsilon_{v}\) is the vacuum permittivity. In the common frequency range of soil sensors (1-1000 MHz), the real part \(\varepsilon_{r}\)' is not frequency-dependent while the imaginary component is. However, as they operate at a lower frequency than the relaxation frequency of water, \(\varepsilon_{r}\)\" is negligible [23]. Debye relation does not consider conductive losses but only the dielectric mechanism; so, at low frequencies, the imaginary component of dielectric permittivity can be underestimated if conductive losses are not considered (see Equation (3)). According to the literature, conductive losses can be neglected at radar frequencies (10 MHz--2 GHz), because the interfacial and electrochemical mechanisms, such as the surface effects associated with the soil/water interface, are of lower importance [24]. Instead, in the lower MHz range of frequency (<10 MHz) conductive losses are not negligible, so the complex dielectric permittivity depends on particle shape and mineralogy, electrolyte type and concentration, particle orientation, and soil electrolyte interaction [25]. Most soil sensors cannot distinguish between the real and the imaginary component, but they measure a parameter that contains both, called apparent dielectric permittivity (\(\varepsilon_{a}\)) [26]:  
\[\varepsilon_{a}\;=\;\frac{\Big{\{}1\;+\;\Big{[}1\;+\;tan^{2}(\delta)\Big{]}^{ 0.5}\Big{\}}\varepsilon_{r}\text{'}}{2} \tag{6}\]  
It is evident from Equations (5) and (6) that high values of \(\varepsilon_{r}\)\" or \(\sigma_{dc}\) amplify the \(\varepsilon_{a}\), potentially causing errors in the estimation of soil moisture content. When the \(\varepsilon_{r}\)\" values are of the same or lower order of magnitude as \(\varepsilon_{r}\)', the \(\varepsilon_{a}\) is only minimally affected by the imaginary part. Generally, to solve this problem, soil sensors operate at mid-high frequencies to lower the imaginary component contribute; others measure separately the electrical conductivity at low frequencies to estimate it. Summarizing this, while the real part is mostly related to moisture content and porosity, the imaginary component is prone to the variability of salinity and temperature, and it depends on interfacial electrochemical mechanisms.
## 4 Discussion
### Application of AI for Planning OGS  
OGS are mainly considered with an intent to correct the deformed facial structures involving the jaw bones and to restore them to symmetry and sagittal balance with esthetic corrections. A well-designed treatment plan is the key for a successful OGS. The conventional way of treatment planning is mainly dependent on the surgeons' experience supplemented by the cephalometric analysis and study models [5]. Currently, deep learning-based automated model have been applied for OGS planning [39, 40]. [PERSON] et al. has reported using a deep learning-based model to estimate the normal 3D bone shape models in patients with facial deformities requiring orthognathic corrections. The authors compared this model with the existing sparse representation method [58]. The deep learning model demonstrated superior estimation performance in comparison with the sparse representation method [39, 40]. However, in the training process due to the lack of ground truth reference bones, this model was trained on simulated pairs of deformed-normal bones which are unlikely to cover all possible types of deformities. Hence, this model may demonstrate suboptimal performance when applied on real data with orthognathic deformities [39].
## 9 Conclusions  
This study proposes a data-driven and model-driven integration approach for optimizing equipment safety investment in digital twin coal mining enterprises. By applying the equipment safety investment optimization model to a specific coal mining enterprise, the optimal investment proportion for the enterprise was determined. The validity of the proposed approach was then verified using an accident loss prediction model. The main conclusions are as follows:  
1. This study integrates digital twin technology with equipment safety investment, establishing a digital twin model for the coal mining equipment safety investment and introducing a digital twin-based optimization principle for coal mining equipment safety investment. The model and principle enable precise allocation and dynamic adjustment of equipment safety investments, effectively enhancing the safety of coal mining equipment and reducing accident losses.
2. For optimizing safety investment, this study introduced a buffer operator to adjust investment data, addressing the impact of safety investment characteristics on the optimization results. Furthermore, a C-D production function was used to construct an optimization model for coal mining equipment safety investment, to minimize accident losses. The model was applied to a specific enterprise, resulting in the determination of the optimal investment proportion for equipment safety in that enterprise.
3. To validate the effectiveness of the optimization scheme, an accident loss prediction model based on particle swarm optimization (PSO) and Backpropagation (BP) neural network was established. This model predicts the potential accident loss values resulting from the implementation of the optimized scheme, thereby verifying the effectiveness of the proposed solution. Error metrics indicated that the prediction model exhibits high accuracy and precision, making it suitable for accident loss prediction. This provides a feasible method for validating the effectiveness of the equipment safety investment optimization scheme.  
The digital twin-based equipment safety investment optimization method proposed in this study has achieved precise allocation of safety investments, addressing issues such as irrational investment allocation, poor safety economic benefits, and resource wastage in coal mining enterprises. This method has effectively improved equipment safety, reduced accident losses, and ensured safe production in coal mining enterprises.  
Conceptualization, L.W.; data curation, L.W.; formal analysis, R.L.; investigation, H.W.; methodology, H.W.; software, W.L.; supervision, Y.W. and W.L.; validation, R.L.; visualization, L.W.; writing--original draft, L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article. For detailed data presentation and analysis, please refer to the corresponding sections of the article.  
The authors declare no conflicts of interest.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 4 Machine-Learning Algorithm
### Parameter Tuning  
Table 6 summarizes the algorithm parameters used in the training. These parameter selections were made to optimize the efficacy of the model in predicting the prices of cloud services. The high number of iterations enables the model to discover complicated data relationships, while the low learning rate ensures a delayed but more accurate convergence. The depth of 16 enables the model to capture more complex interactions between features, but it also increases the likelihood of overfitting. The bagged temperature of 0.2 regulates the regularization technique's strength and prevents overfitting. The iterative outlier-detection strategy and the waiting period contribute to an early termination if the evaluation metric remains unchanged.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline _iterations_ & up to 15,000 \\ _loss\_function_ & RMSE \\ _learning\_rate_ & 0.001 \\ _depth_ & 16 \\ _custom\_metric_ & RMSE \\ _random\_sed_ & 42 \\ _bagging\_temperature_ & 0.2 \\ _ad\_type_ & Iter \\ _metric\_period_ & 75 \\ _task\_type_ & GPU \\ _ad\_wait_ & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CatBoost algorithm parameters.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Location** & **Total Offers** & \
\begin{tabular}{c} **Offers in All Years** \\ **(2016–2022)** \\ \end{tabular} &
\begin{tabular}{c} **Offers in All** \\ **Instance Families** \\ \end{tabular} \\ \hline _AsiaPacific_ & 170,419 & Yes & Yes \\ _Europe_ & 152,439 & Yes & Yes \\ _US_ & 132,339 & Yes & Yes \\ _AWSGovCloud_ & 33,972 & No & Yes \\ _SouthAmerica_ & 19,694 & No & Yes \\ _Canada_ & 19,568 & No & No \\ _MiddleEast_ & 16,352 & No & No \\ _Africa_ & 12,544 & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of offers in AWS EC2 historical data (2016–2022) in each location.  
By utilizing the GPU for training, the model can process data faster and more effectively manage larger datasets. Experimentation and domain knowledge were used to select these parameter settings for optimal performance in predicting cloud service prices.
## 3 Results and Analysis
### Simulation Experiment and Result Analysis
#### 3.3.1 Parameter Setting  
The relevant parameter settings with which to avoid the influence of different parameters on the algorithm are shown in Table 4. All experiments in this paper are based on the PyCharm platform and were run on the Window10 (64 bit) operating system with the processor configured as Inter (R) Core (TM) i5-5200U CPU @ 2.20 GHz 2.20 GHz with a memory of 12 GB.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 6 Test Setup
### Results--Generalization  
To test the generalization and robustness of new classes, an RF model was trained on three interference signal types, but tested on all four types, as in Exp-B. According to the confusion matrix presented in Figure 19, the misclassified instances of \"Signal 4\" are mainly assigned to the \"Signal 2\" category, with approximately 20% being classified as \"Signal 3\". Only about 5% of the signals of \"Signal 4\" are assigned wrongly to the class \"none\".  
These results demonstrate that the RF model can correctly classify unknown interference signals, particularly those that share similarities with other known interference signals.  
In summary, detection in unknown scenarios with NLOS components is significantly more challenging. However, the ML can successfully detect the interference signal in most cases.
## 2 Case Study
### Modelling Parameters Properties  
A number of constitutive models that can represent the behaviour of geomaterials were observed in numerical solutions. In this study, two constitutive modelling schemes have been adopted in order to ensure that the stress-strain behaviour of the soil can be effectively represented. The filler material of the embankments and sand cushions was modelled using the Mohr-Coulomb (MC) model, while the soft ground was modelled by the soft soil (SS) model. In recent years, both schemes of this model have been widely adopted for numerical analysis by researchers investigating embankments with PVDs [10; 22; 23]. The MC model requires five parameters: cohesion (\(c\)), friction angle (\(\phi\)), Poisson ratio (\(\
u\)), dilatancy angle (\(\psi\)) and elastic modulus (\(E\)). In contrast, the SS model is defined by two parameters, i.e., the modified swelling index (\(\kappa^{*}\)) and the modified compression index (\(\lambda^{*}\)). The modelling parameters employed in the numerical analysis are summarised in Table 5.
## 4 Numerical Results
However, since 3Z manages three zones (two zones in 2Z) within one RA, the registration cost is always less than 1Z. On the other hand, as shown in Figure 14, it could be seen that the paging cost to connect the UE increased as \(\mu\) increased. These results imply that the Network_DB does not know the exact zone of the UE, after which extra paging costs are incurred. Figure 14 also showed that as \(\mu\) increased, the paging cost at 2Z and 3Z gradually increased due to the two-step paging in 2Z and three-step paging in 3Z, which was slightly reduced compared to the cost of one-step paging. Figure 11: Total cost and reduction ratio versus CMR. Figure 12: Total cost versus \(\theta\). Based on the numerical results, we obtained the following major observations. 1. The performance of 3Z improved more as the CMR became smaller; that is, when the UE traveled more in other zones. 2. For most CMR values, the 3Z outperformed the 1Z and the 2Z. The smaller the CMR, the better the performance of 3Z. 3. The greater the probability of returning to the previous zone, the more the performance of 2Z and 3Z could be linearly improved. 4. As the number of times entering another zone increased, that is, as the zone residence time decreased, the registration costs of 2Z and 3Z were smaller compared to the registration cost of 1Z by managing three or two zones with one RA. Figure 14: Paging cost versus \(\mu\). Figure 13: Registration cost versus \(\mu\). * In 1Z, the paging cost was determined regardless of the UE's zone residence time. However, in 2Z or 3Z, the paging cost gradually increased as the zone residence time decreased.