esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Electrodialysis (ED)  
Electrodialysis (ED) is an electrochemical desalination process. This technology uses a combination of semipermeable membranes and the generation of an electric field to remove the dissolved ions from the solution [53]. The working principles are shown in Figure 24[23; 24].  
The electric field is generated by two electrodes, supplied in direct current voltage. Each ion has an electric charge (positive or negative). Due to the electric field, each ion is affected by an electric force directly proportional to the amplitude of the electric field and the value of the ion charge.  
The cations (positive ions, as Na+, Ca2+) are attracted by the anode, while the anions (negative ions, as Cl-, HCO3-, CO3-2-) are attracted by the cathode [53].  
Anionic and cationic semipermeable membranes are alternatively installed in the region between the two electrodes. The first one allows the flow to the anions, the latter to the cations [23]. In this way, the migration of ions generated by the electric field is selectively stopped by the semipermeable membranes. As an example, during its motion to the anode (on the left in the picture) a positive ion can cross the cationic membrane but  
Figure 23: Filtration technologies by required gradient pressure and porous size. Reproduced with Elsevier License permission n. 4965821233043 [36].  
Figure 24: The working principles of an Electrodialysis desalination unit.  
not the anionic one. Thus, the electric field causes the motion of positive charges to the right side, while the anionic membrane stops the motion to the electrode. In the same way the negative ions migrate to the cathode but are stopped by the cationic membranes. As a result, the ions are confined inside the brine channels, removing ions from the freshwater channels (see Figure 24) [24].  
The first ED units were commercially introduced in the early 1970s. As a solution to avoid the deposition of salts on membranes, at regular intervals the polarity of electrodes is inverted for a few minutes, changing the motion of the ions inside the unit. The feedwater channels work temporarily as brine channels and vice versa [23]. This technology is currently used to produce freshwater from brackish water (salinity up to 2000 ppm) [10].
###### Abstract  
Isolation piles are widely used to control the influence of shield tunnelling on adjacent buildings as an effective protective measure. However, the restraint effect of isolation pile on surface settlement trough is rarely explored from the internal mechanism. Firstly, the restraint mechanism of isolation piles is investigated from the pile-soil-tunnel interaction mechanism. Secondly, based on the Melan solution and the Loganathan formula, the analytical solution of surface settlement trough under the influence of adjacent isolation piles is derived. Thirdly, in order to satisfy the engineering analysis scale and reflect the friction characteristics between isolation pile and soil particles simultaneously, the FDM-DEM coupling technique is introduced to establish a numerical model including discrete medium and continuum medium. Finally, the applicability and reliability of the analytical solution and the FDM-DEM coupling numerical solution are verified by comparing field measured data. The results indicate that the surface settlement trough under the influence of isolation piles will have an asymmetric distribution. Surface settlement tends to develop more to the opposite side of the tunnel when isolation piles are pre-installed on one side. The findings of the study have substantial theoretical significance and engineering reference value.  
shield tunnel; isolation pile; surface settlement trough; analytical solution; FDM-DEM coupling
## 2 Materials and Methods
### NTP Device and Its Characteristics  
A needle-ring electrode configuration was employed to generate a point-to-ring discharge, as studied and described previously [24; 25]. The ring electrode, constructed from brass, featured a conical design. The diameter of the upper circle section of the electrode was approximately 11 mm, and the tip of the needle electrode was placed in its center. The ring electrode was connected to the positive terminal of a high-voltage power supply, while the needle electrode was connected to the negative terminal. The arrangement of the electrodes is illustrated in Figure 1a. Near the tip, a negative corona discharge occurs, while a positive discharge ignites at the edge of the ring. Consequently, a bipolar corona discharge is produced, with reactive species generated in the plasma being transported by ions accelerated in the electric field between the electrodes. Although the ring electrode attracts most of the charged particles from the negative corona, neutral reactive particles are forced through the ring electrode to the surface where they are treated. The device operated at a voltage of 6.5 kV and a current of 200 \(\mu\)A. For sample exposure, a set of 30 seeds was placed in a plastic Petri dish under the electrode system at a distance of 20 mm and treated for the appropriate time.  
Figure 1: The arrangement of the electrodes (**a**); the emission spectra of the generated plasma (**b**); and the hand-held point-to-ring plasma discharge device setup (**c**). The Petri dish with the seeds in it is kept in the circular base on the table, and the device is mounted on the top.  
The emission spectrum of the generated plasma was recorded in the spectral range 190-1100 nm with a resolution of 1 nm by a Silver-Nova TEC Spectrometer (StellarNet Inc., Tampa, FL, USA) and is illustrated in Figure 1b. The predominant features in the emission spectrum are the second positive system of nitrogen molecules and the first positive system of nitrogen molecules, both exhibiting significantly lower intensity due to the relatively low electric power. Intensities of approximately 1% were observed in the spectral lines corresponding to the resonance transitions of oxygen atoms. The emission of hydroxyl radicals and spectral lines of hydrogen, typically present in air plasma, was not detected. Furthermore, no atomic nitrogen spectral lines were observed. The estimated vibrational temperature of the nitrogen molecules was approximately T \(\approx\) 3000 K.  
The long-lived species generated in the discharge atmosphere were measured using a Serinus 40H NOx Analyzer (ACOEM Ecotech, Melbourne, Australia) for the detection of nitrogen oxides and a UV-100 Ozone Analyzer (Eco Sensors, Santa Fe, NM, USA) for the detection of ozone. The concentration of ozone was (380 \(\pm\) 30) ppm, the concentration of NO\({}_{2}\) was (1.2 \(\pm\) 0.4) ppm, and the concentration of NO was below the detection limit of 0.01 ppm. The concentrations of NO\({}_{x}\) were an order of magnitude higher compared to the concentration of ozone.
## 6 Pharmacological Activities of _Astragalus_ Polysaccharides
### Immunoregulatory Effects  
The _Astragalus_ polysaccharides not only enhance the function of immune organs and cells but also stimulate the release of cytokines, affect the nervous-endocrine-immune system network, and promote the expression of related genes. APS can enhance the immune function by increasing the weight of immune organs. It has been reported that APS administration (220 mg/kg) in feed could significantly increase the relative weight of immune organs, as well as the number of lactobacilli and _Bacillus cereus_ in the intestinal microbiota of chicks [60]. Furthermore, APS could increase the weights of the thymus and spleen, as well as the number of peritoneal macrophages in mice [40], and enhance the function of macrophages. It has been reported that APS at a concentration of 300 \(\upmu\)g/mL could significantly increase the nitric oxide (NO), interleukin (IL)-18, IL-6, and tumor necrosis factor (TNF)-\(\upalpha\) levels in the human monocyte/macrophage strain TPH-1, indicating that APS can activate macrophages [61]. _Astragalus_ polysaccharides showed an impact on the functional status and phenotype of T cells during polymicrobial sepsis. Treatment of mice with APS at a dose of 400 mg/kg on day one after cecal ligation and puncture could increase the T helper (Th) cell population and also the percentage of Th17 cells in the blood. Consequently, APS could attenuate immunosuppression in polymicrobial sepsis [62]. In tilapia fish, it has been shown that 1500 mg/kg APS supplementation could upregulate the phagocytic activity, as well as the superoxide dismutase, glutathione peroxidase, and amylase activities. However, APS had no effect on the serum NO and malondialdehydedehyde levels [63]. In addition, APS can be used as an immunomodulator of vaccines. At a dose of 5, 10, and 20 mg/kg, APS could markedly increase the titer of foot-and-mouth disease virus (FMDV)-specific antibodies in a dose-dependent manner and upregulate the mRNA expression of interferon (IFN)-\(\upgamma\) and IL-6, indicating that APS can protect against FMDV [64]. In summary, APS can be used as an immunopotinator, affecting the non-specific and specific immune systems.
# Accurate RANS Simulation of Wind Turbine Stall by Turbulence Coefficient Calibration
## 4 Simulations with Turbulence Coefficient Calibration  
### Closure Coefficients of the SST Turbulence Model  
Turbulence is a key determinant of flow separation and stall. The SST turbulence model used in the present study is recognized as one of the best linear eddy viscosity turbulence models providing a relatively good prediction of turbulence for both attached and separated flows. There are two transport equations for the turbulence kinetic energy \(k\) and the specific dissipation rate \(\omega\) in the SST model. More than a dozen closure coefficients are involved in the equations. Their default values are  
\[\begin{array}{c}\sigma_{k,1}=1.176,\;\sigma_{\omega,1}=2.0,\;\sigma_{k,2}=1. 0,\;\sigma_{\omega,2}=1.168,\;\alpha_{1}=0.31,\;\beta_{i,1}=0.075,\;\beta_{i,2 }=0.0828,\;\alpha_{\infty}^{*}=1.0,\\ \alpha_{0}=1/9,\;\beta_{\infty}^{*}=0.09,\;R_{\beta}=8.0,\;R_{k}=6,\;R_{\omega }=2.95,\;\xi^{*}=1.5\end{array}\]  
Most of these values were determined by the observed turbulence properties in certain conditions. That limits the universality of the turbulence model. The turbulence simulation may be more suitable in some cases but less applicable in other cases. Calibrating the values of these coefficients is a way to achieve a better result for a given case. After a lot of computational tests, the coefficient \(\beta_{\infty}^{*}\) is adopted to be calibrated in the present study. It is a key coefficient responsible for the dissipation of \(k\) and  
Figure 11: The magnitude of the simulation error at different blade sections, with a comparison to the corresponding curves of the S809 airfoil.  
Figure 10: The \(\text{C}_{t}{}^{\prime}\) versus AoA at different blade sections, with a comparison to the corresponding curves of the S809 airfoil.  
the production of \(\omega\) in the transport equations. The variation of the value of \(\beta_{\infty}^{*}\) eventually leads to a change of the turbulent viscosity in RANS simulations. Therefore, the calibration of \(\beta_{\infty}^{*}\) can be considered as an appropriate adjustment of turbulent viscosity.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.5 Mathematical Model Autoencoder  
By identifying intricate patterns in data, Autoencoder algorithms--neural network topologies intended for unsupervised learning--are useful for predicting energy consumption. They are composed of a decoder that reconstructs the original input and an encoder that compresses input data into a lower-dimensional representation. The mathematical framework by which Autoencoders learn from past energy usage data and pertinent characteristics such as temperature and time of day to recognize typical consumption patterns and spot irregularities is presented in Equations (33)-(36). By filtering out noise and comparing incoming inputs to learnt representations, this capacity improves future demand estimates, increasing their accuracy and dependability. Table 6 displays all the Autoencoder's equations [43].
###### Abstract  
Analyses of efficiency are vital for planning and monitoring the duration and costs of construction works, as well as the entire construction project. This paper introduces a combined quantitative (probabilistic) and qualitative (machine learning-based) approach to the problem. The proposed approach covers probabilistic analysis based on fitting a triangular distribution to empirical data, followed by the application of support vector machines (SVM). Following the theoretical assumptions, the paper also presents an application of the proposed approach for formwork assembly as an exemplary construction work. This is based on real-life data, including conditions, characteristics, and features of formwork assembly work recorded on a construction site. As a result of the study, triangular distributions were fitted to data representing efficiencies of formwork assembly for three different types of structural members made of reinforced concrete. The parameters (\(a\)--minimum, \(m\)--peak and \(b\)--maximum values of efficiency measured as square meters of an assembled formwork per hour) of the fitted distributions for the particular real-life data were as follows: for columns \(a=0.100\), \(m=1.450\), \(b=1.900\), for walls \(a=0.700\), \(m=1.995\), \(b=3.300\) and for slabs \(a=0.200\), \(m=2.125\), \(b=3.200\). The obtained distributions allow us to assess the probability of achieving efficiency not less than a certain assumed critical value. The study also developed two SVM models--the first based on so-called \(C\)-classification and the second based on \(v\)-classification--capable of recognising with satisfactory accuracy whether the efficiency of formworks assembly works for certain conditions, characteristics, and features of works are above or below median values computed based on previously fitted distributions. The performance of both developed models in terms of proper classification, either for training or testing, was above 80%.  
a Article  
Work Efficiency Analysis--Application of Probabilistic Approach and Machine Learning for Formworks Assembly.  
Appl. Sci.2023135780[[https://doi.org/10.3390/app13095780](https://doi.org/10.3390/app13095780)]([https://doi.org/10.3390/app13095780](https://doi.org/10.3390/app13095780))
## 6 Federated Learning for Early Health Prediction
### Federated Learning Methodology
Techniques such as secure multi-party computation (SMPC) or homomorphic encryption ensure that local model updates remain confidential. These methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy. SMPC allows edge devices to perform computations on their local models while maintaining privacy. The aggregation process can be represented mathematically as follows:  
\[W=\text{SMPCAggregation}(w_{1},w_{2},\ldots,w_{N})\]  
Here:  
* SMPCAggregation is the secure multi-party computation aggregation function. In SMPC, each edge device \(i\) contributes its local model \(w_{i}\) without revealing the parameters of the model or the data used for training. Secure protocols such as homomorphic encryption or secret sharing facilitate the computation of aggregate statistics, including gradients, on encrypted data. These encrypted statistics are then combined to update the global model \(W\). SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements [84].
## 2 Materials and Methods
### UPLC Analysis of Phenolic Compound  
The phenolic compound content has been determined using an Acquity H class UPLC system equipped with a Waters Acquity PDA detector (Waters, Milford, MA, USA). An Acquity UPLC(r) HSS T3 C18 column (150 mm \(\times\) 2.1 mm, particle size 1.8 \(\upmu\)m) was used for the chromatographic separation (Waters, Dublin, Ireland). The gradient elements were performed using the following mobile phase composition: A, 0.1% aqueous formic acid; B, acetonitrile 0.1% formic acid (pH = 2). The gradient program looked like this: flow 0.4 mL/min 5% B (2 min), 5-16% B (5 min), 16% B (3 min), 16-20% B (7 min), 20-28% B (1 min) flow 0.45 mL/min, 28% (1 min). A 28-60% B (3 min) flow 5.0 mL/min, 60-95% B (1 min), 65% B (1 min), 95-5% B (0.1 min) flow 0.4 mL/min, 5% B (1.9 min).  
The concentration levels of phenolic compounds were identified using an external standard at wavelengths \(\lambda\) = 320 (caffeic acid, chlorogenic acid, salicylic acid, sinapic acid, vitexin, p-cumaric acid, ferulic acid, rutin, apigenin, kaempfer, quercetin, luteolin), \(\lambda\) = 280 (gallic acid, syringic acid, vanillic acid, 4-hydroxybenzoic acid, vanillin, t-cinamic acid, protocatechuric acid naringenin) and \(\lambda\) = 230 nm (benzoic acid, katechin). The concentration is given in mg/1 g of extract. The compounds were detected by comparing the retention times of the investigated peak to those of the standard, as well as by adding a specific amount of the standard to the tested sample and repeating the analyses. The following were the retention times for phenolic compounds, including flavonoids gallic acid \(-\)2.39, protocatechuric acid \(-\)5.09, 2,5-dihydroxybenzoic acid \(-\)7.96, 4-hydroxybenzoic acid \(-\)8.05. katechin \(-\)9.18, chlorogenic acid \(-\)9.19, vanillin \(-\)9.53, caffeic acid \(-\)9.79, syringe acid \(-\)10.53. vanillic acid \(-\)12.01, p-cumaric acid \(-\)13.09, ferulic acid \(-\)15.62,salicylic acid \(-15.94\), benzoic acid \(-16.22\), sinapic acid \(-16.62\), vitexin \(-18.32\), rutin \(-19.13\), t-cinnamic acid \(-26.93\), quercetin \(-29.68\), luteolin \(-29.98\), naringenin \(-31.53\), apigenin \(-31.79\), kaempfer \(-31.89\). The detection level has been set at 1 \(\upmu\)g/g [17; 18; 19].
## 2 Security and Privacy Challenges in IoE
### Fundamental Security Risks in IoE
#### 2.1.3 Comparison of Centralized and Decentralized Security Models  
System architectures used in IoE security are generally divided into two main categories: centralized and decentralized security approaches.  
As shown in Figure 1, the Single Point of Failure (SPOF) risk inherent in centralized structures is compared with the fault-tolerant architecture provided by decentralized security models. In the centralized security model, the security of all IoE devices is managed through a single server or authority. Although this structure offers management simplicity, it poses a risk to the entire network's security in case of a system failure.  
Figure 1: Comparison of centralized and decentralized security models in IoE.  
The SPOF is the primary vulnerability of this model [11]. In contrast, decentralized security models are based on technologies such as blockchain, edge computing, and SSI [47]. Security is ensured in a distributed manner among devices without relying on a central authority. As a result, the system becomes more resilient, and there is no single attack target. As shown in the figure, in the centralized model, all devices connect to a single server, whereas in the decentralized model, security is ensured at the edge level, and secure connections are established between the devices.  
However, this structure may increase computational costs and management complexity due to cryptographic processes. The advantages and disadvantages of centralized and decentralized security models vary depending on system preferences. These differences are summarized in Table 1:
## 3 Results
### Edge Detection and Enhancement
#### 3.2.3 Fault Likelihood (FL) Attribute, the Probability Volume  
Fault likelihood is three-dimensional scanning of structure-oriented semblance volume. Mathematically, fault likelihood is similar to the semblance. However, it not only scans for  
Figure 5: Result of conventional attributes. Time slices at 1550 ms (**a**) Difficult to trace faults on input seismic data. (**b**) Variance attribute reveals the major faults in the study area. Pointers compare the fault presence on all images in this figure. (**c**) Curvature and (**d**) Chaos attributes are not helpful. (**e**) Edge Enhancement attribute is able to detect only major faults, (**f**) Edge Evidence detects major faults but it is too noisy. (**g**) Dip Steering Median Filter (DSMF) picks up few fault traces as shown by the pointers. (**h**) Ant-tracking showed comparatively better results and detected several major and minor discontinuities.  
semblance in multiple fault orientations but also measures it at a variety of dips and azimuths to enhance the fault likelihood. The lowest semblance will occur along the fault plane and that will represent the maximum likelihood. The scanning concludes with the images of maximum fault likelihood and its corresponding fault strikes and dips [37]. Fault likelihood attributes represent the probability of faults at each sample location. Figure 6a shows the Fault Likelihood (FL) image.  
With estimates of reflective paths, structure-based semblance could be defined as:  
\[Semblance=\frac{(Image)_{s}^{2}}{(Image^{2})_{s}}\]  
Which shows the semblance value, (.), as structure-oriented. Similarly, computed ratios vary greatly where the numerators and denominators are small [38]. This is because usually, before computing emulsion ratios, we perform a supplementary smoothing of numerators and denominators. Therefore, the equation after applying the modifications is as under:  
\[Semblance=\frac{((image)_{s}^{2})_{f}}{((image^{2})_{s})_{f}}\]  
Figure 6: Time slices at 1550 ms. (**a**) Fault likelihood (FL) or the fault probability volume, which has been computed using fault-oriented semblance (**b**) Thinned Fault Likelihood (TFL) image showing razor sharp discontinuities across the study area. Red and green pointers in both images indicate major faults.  
Therefore, the f is the additional smoothing to the images, which follows the faults. Finally, the vertical smoothing to the data is accounted for, to a vertical extent, the low semblance appearance:  
\[f=1-Semblance^{8}\]
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 4 The Proposed System  
A network consisting of low-cost devices is assumed to play the role of the IoT system. Each device is a wireless node of the considered network and certain specifications regarding node operation need to be taken into consideration in order to implement the previously described TiMAC policy. The particular information, along with various implementation issues (e.g., TDMA synchronization), is presented next. More specifically, the elements comprising the low-cost devices (nodes) are given in Section 4.1, and the types of messages being exchanged among the nodes are described in Section 4.2, TDMA parameters are presented in Section 4.3, and synchronization issues are given in Section 4.4.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 4 SMTrust Design and Analysis
### Attacks Detection
#### 4.5.1 Rank Attack Detection  
In SMTrust Rank attack detection, when a node receives a DIO message from a neighbouring node, it checks whether the rank changes without DIO_seq. If the new DIO_seq is less than or equal to the current DIO_seq, it means that it is a suspicious or fake DIO [66]. Therefore, a Rank attack is identified. Moreover, checking the inconsistency of the rank of the potential parent against the neighbouring nodes indicates a fake DIO and Rank value [25; 66]. Algorithm 1 demonstrates the detection of Rank attack in SMTrust.