esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 2 Properties and Potential of Plant Growth-Promoting Rhizobacteria (PGPR)
### Auxins, Cytokins, Gibberellins  
The phytohormone IAA is the most common auxin among plant-associated bacteria and play a central role in plant-bacterial interactions [65]. The main beneficial effect is root augmentation, especially root hairs and secondary roots, which consequently leads to an increase in root exudates; therefore, the production of IAA is certainly one of the most important mechanisms that favor plant growth [66]. The greater the root surface, the better the absorption of minerals from the soil. It also increases the valuable surface for the colonization of bacteria attracted by plant metabolites.  
Cytokinins play a central role in developing the vascular system, in embryogenesis, in the formation of nodules, and in response to environmental changes [67]. Gibberellins are involved in the transport of metabolites in the formation of chloroplasts, leaf senescence, cell division, and stem morphogenesis [68]. While the biosynthetic pathways of gibberellins in plants and fungi have been thoroughly investigated and elucidated, little has been discovered about the biosynthesis of these enzymes in bacteria. Recent studies [69] hypothesize that bacteria have developed an independent biosynthetic pathway for the production of gibberellins.
## 3 Validation of Numerical Models
### Numerical Simulation of SCB  
According to [PERSON] and [PERSON] et al. [56; 57; 58; 59], the SCB sample comprises a narrow beam at the bottom, an ice layer, and a wide beam at the top. Figure 6 displays the sample dimensions and numerical model. The bottom, somewhat longer, narrow beam was subjected to a central loading auger velocity of 0.02 mm/s, while the top wide beam was fixed in an appropriate location on the test apparatus. The cohesive element is embedded between the bottom narrow beam and the ice layer. In contrast, the top wide beam and the ice layer are considered to adhere perfectly without failure, which is modeled this way because, according to the experimental results, the debonding of the ice from the aluminum plate occurs between the bottom narrow beam and the ice layer around the negative fracture phase angle. The ice material characteristics are the same as in Table 1, and the cohesive element parameters are displayed in Table 2. Ice and aluminum are modeled using linear elastic materials and C3D8R. The SCB debonding process begins when the peak stress between the interfaces exceeds a threshold value and continues until debonding is completed.  
Figure 5: Stress cloud for time: (**a**) 6.27 s, (**b**) 12.49 s, (**c**) 12.56 s.  
Figure 6: Numerical model of SCB.  
In this experiment, the SCB samples are modeled using four element sizes: 2.5 mm, 3 mm, 3.5 mm, and 4 mm. Figure 7 compares the SCB experiments' load-displacement relationship to the numerical results. The results reveal that the numerical load-displacement curves are primarily compatible with the actual results, and element size has little effect on simulation results. When the element size is 4 mm, the simulated peak load is 18 N, with a displacement of 0.0288 mm and a relative error of 8.9%. Figure 8 shows the SCB ice-aluminum debonding process. Figure 8 shows that the cohesive element begins to suffer damage, and the debonding between the ice and the aluminum plate begins. Figure 8 shows that the cohesive element has been destroyed, and the ice and aluminum plate have been wholly debonded. Comparing the experimental phenomena [56; 57; 58; 59], it is clear that the simulation fits with the experimental results, confirming the model's reliability. Validated model parameters will be used for wing electric-impulse de-icing simulations.
## Materials and Methods
### Simplified FEM Calculations  
The initial FEM analysis of the machine is represented by simplified FEM calculations aimed at determining the size of the axial component of the magnetic flux density along a line that extends between both rotor discs through the center of gravity of PMs. The simplification is that the calculations were performed without considering the windings; only the air between the opposite rotor disks with surface-mounted PM was considered, since the permeability of air and copper is almost the same.  
Figure 1a shows the initial AFPMSM model consisting of two rotor disks, 20 permanent magnets and a 100 mm virtual air gap (distance between PMs on opposite rotor disks), which consists of 20 layers. For each layer of the virtual air gap, a simplified calculation with FEM was performed, analyzing the axial component of the magnetic flux density across the virtual line passing through both the rotor disks, the virtual air gap, and the PMs on opposite rotor disks (through the center of gravity). The position of the line is shown in Figure 1b.  
Figure 2 shows an example of the axial component of the magnetic flux density (\(B_{x}\)) along the centerline (shown in Figure 3) with a 55-mm virtual air gap thickness.  
The values of the axial component of the magnetic flux density, \(B_{x,\mathrm{max}}\) and \(B_{x,\mathrm{min}}\), are shown, which represent the value of \(B_{x}\) near the PM (\(B_{x,\mathrm{max}}\)) and in the middle of the virtual air gap (\(B_{x,\mathrm{min}}\)). Values in the middle of the virtual air gap represent the initial set of input data for development of the mathematical model in explicit form for the calculation of the value of axial component of magnetic flux density in the middle of the virtual air gap and consequently in the middle of the stator.  
Figure 1: Simplified finite element method (FEM) model: (**a**) 3D model of the simplified axial flux permanent magnet synchronous machine (AFPMSM), and (**b**) model of rotor disk with surface-mounted permanent magnets (PMs) with their dimensions and the position of the centerline on which the axial component of magnetic flux density is analyzed.  
Figure 3: Pascal triangle for function with three unknowns.  
Figure 2: Axial component of magnetic flux density along the centerline (an example is shown at 55 mm virtual air gap thickness, 7 mm rotor disk thickness and 5 mm PM thickness. Values of \(B_{x}\) near the PM (\(B_{x,\mathrm{max}}\)) and at the middle of the virtual air gap (\(B_{x,\mathrm{min}}\)) are shown).  
Simplified FEM calculations were performed with limitations, such as the condition that stator (virtual air gap) thickness is not larger the 4 times the PM thickness (\(d_{\rm s}\leq 4h_{\rm TM}\)). Initial simplified FEM calculations were performed for the 3-phase AFPMSM with the data shown in Table 1 [41] and again repeated for the AFPMSM with the data shown in Table 2.
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
###### Abstract  
The motion of an underwater vehicle is prone to be affected by time-varying model parameters and the actuator limitation in control practice. Adaptive control is an effective method to deal with the general system dynamic uncertainties and disturbances. However, the effect of disturbances control on transient dynamics is not prominent. In this paper, we redesign the \(L_{1}\) adaptive control architecture (L1 AC) with anti-windup (AW) compensator to guarantee robust and fast adaption of the underwater vehicle with input saturation and coupling disturbances. To reduce the fluctuation of vehicle states, the Riccati-based AW compensator is utilized to compensate the output signal from L1 AC controller via taking proper modification. The proposed method is applied to the pitch channel of REMUS vehicle's six Degrees Of Freedom (DOF) model with strong nonlinearities and compared with L1 AC baseline controller. Simulations show the effectiveness of the proposed control strategy compared to the original L1 AC. Besides, the fluctuation in roll channel coupled with pitch channel is suppressed according to the performances of control tests.  
Autonomous Underwater Vehicle (AUV); \(L_{1}\) adaptive control; anti-windup (AW) compensator; uncertainty; coupling disturbances +
Footnote †: journal: Applied Sciences
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 4 Experimental Result
### Cse-Cicidds2018 Scenario 1 Results  
Upon studying the CSE-CICIDS2018 dataset, the initial scenario presents a captivating classification challenge containing \"Benign\", \"FTP-BruteForce\", and \"SSH-Bruteforce\". This research analyzes the efficacy of a model trained in the characteristics of these unique traffic types, as illustrated in Tables 5 and 6.  
The results in Table 5 present an overview of the model's performance, providing a comprehensive understanding of its effectiveness. With an accuracy rate of 99.937%,matched by precision, recall, and F1-score, all at the same value, the model's consistency and reliability in classifying diverse network traffic types are affirmed.
###### Abstract  
Using industrial by-products to replace cement is an important way to reduce carbon emissions in the cement industry. The purpose of this article is to understand the effect of quartz powder on the properties of cement-based materials. Experimental studies were conducted on the macroscopic and microscopic properties of cement-based materials mixed with quartz powder to evaluate their feasibility as a replacement for cement. The substitution rates of quartz powder were 0% (Qu0), 7.5% (Qu7.5), and 15% (Qu15). The test time was from 1 day to 28 days, and the main results are as follows: In the early stage of the hydration reaction, as the amount of quartz powder substitution increases, the cumulative hydration heat increases. This is mainly because the nucleation effect of quartz powder accelerates the hydration reaction of cement. In the later stage of the hydration reaction, as the amount of quartz powder substitution increases, the cumulative heat of hydration decreases. This is mainly due to the diluting effect of quartz powder. For Qu0, Qu7.5, and Qu15, the decrease in compressive strength after 1 day is not obvious. The decrease in compressive strength at 28 days is more obvious. Overall, there are exponential relationships between the UPV measurement or surface resistivity results and the compressive strength measurement results at 1, 3, 7, and 28 days. The XRD test results show that the main products of the reaction are AFt, CH, Hc, and Mc. From Day 1 to Day 28, the content of Mc becomes evident. The test results for TG showed that, as the amount of quartz powder substitution increases, the mass loss decreases. For different specimens of Qu0, Qu7.5, and Qu15 at different test times (3 and 28 days), there is an exponential function relationship between chemically bound water and strength. A numerical hydration model is proposed for cement-quartz binary blends. The parameters of the hydration model are determined based on the hydration heat normalized by the cement mass. Moreover, the hydration heat at 28 days is calculated using the proposed model. The strength development of all specimens and all test ages can be expressed as an exponential function of hydration heat.  
Keywords:quartz powder; cement; heat of hydration; compressive strength; microstructure +
Footnote †: journal: Applied
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Drawbacks of phantom cell approach  
* Signaling overload at CN at the time of inter-MC domain handoff.
* Smooth (no disruption) service availability is still an issue at the time of handoff among MC domains [27].
* Considerations for smart AP/RAT choice  
The smart RAT choice mechanism fulfills the dependability criterion based on parameter DL4 as mentioned in Table 2. Similarly, the adaptability parameters AL5 and AL4 can fulfill the criteria for adaptability. Next, the EL4 parameter can also fulfill the criteria of extensibility. Finally, PL1 and PL4 parameters of power-saving can be satisfied for power-saving criteria.
## 4 Discussion  
### Major Restoration Activities in 2001-2015  
Similar to the findings of a previous study in Southwest China during the period of 2001-2015, our results showed that afforestation was the dominant land cover change [30]. The large area of afforestation was attributed to GGP and KRDRP. These ERPs were launched to re-establish forests, shrublands and grasslands and counter severe rocky desertification in Southwest China since 2000/2001 [18; 39]. Forests have high economic value and easily survive in the study area with abundant precipitation and warm temperature. Therefore, planting trees was the preferred method for improving vegetation cover [18; 49] and the forest area substantially increased during this period [18]. Our results also showed that more than 48% of the entire afforestation area was converted from agricultural land (Figure 10). Consistent with the results of previous studies, this finding indicated that ERPs were successfully implemented in the study area [50]. Existing studies also found that afforestation considerably relieved land degradation due to the large conversion of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multicolumn{3}{c}{**BESS GPP Trend (g C m\({}^{-2}\) d\({}^{-1}\))**} & \multicolumn{3}{c}{**MODIS GPP Trend (g C m\({}^{-2}\) d\({}^{-1}\))**} \\ \cline{2-7}  & **ERP-Driven** & **Actual** & **CI (\%)** & **ERP-Driven** & **Actual** & **CI (\%)** \\ \hline Afforestation & 25.35 & 169.69 & 14.94 & 33.30 & 136.11 & 24.47 \\ Grass planting & 0.05 & \(-\)0.03 & 38.46 & 0.55 & \(-\)0.94 & 26.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ERP-driven (afforestation and grass planting) and actual GPP trends from 2001 to 2015.  
Figure 8: Contributions of afforestation and grass planting to the actual GPP increase in afforestation and grass planting areas during the period of 2001–2015.  
Figure 9: Annual GPP changes caused by (**A**) afforestation and (**B**) grass planting during the period of 2001–2015.  
desertification areas into forests [30; 51]. Therefore, afforestation has become the dominant restoration activity that remarkably enhances vegetation cover in Southwest China.
## 4 Industrial Background
### Industrial Case Study
Main phases of the order fulfillment process from order incoming up to delivering include planning, machining, washing, assembly, testing, packaging, and delivering, as presented in Figure 5. A customer order (CO) is turned into a production order (PO) during planning, which also determines the confirmed delivery date (CDD). Planning also decides about the lot (batch) sizing and sequencing rules to be applied to the actual PO under production. Release date (RD) is when actual production (its first step, machining) is started, and the lead time (LT) of the order spans until the end of the last operation given by the completion time (CD). All timing data relate to a complete lot. Before employing the new decision support workflow, the average lead time of orders was 2.5-3 days in the mini-factory. The main task for production planning and manufacturing is to respect the CDD for our customers, while filing in the system with as many orders as possible. All in all, of utmost importance is the temporal relation of CD and CDD. While the decisions on RD are out of their authority, the production planners need to make appropriate sequencing and lot  
Figure 4: Layout and operations of the mini-factory investigated. Figure 5: Phases of the order fulfillment process from order incoming to delivery. sizing decisions to satisfy our commitment to the CDD. This requirement is best expressed by a combined performance measure which focuses on the lead time of production orders and the makespan of the orders on a daily level.
# Comprehensive Numerical Analysis of a Porous Piezoelectric Ceramic for Axial Load Energy Harvesting
## 6 Conclusions  
In this study, an axial-type porous piezoelectric energy generator is designed and investigated. The experimental and numerical simulations are carried out for different operating conditions. The errors of 2.83% and 9.2% are obtained at the first eigen-frequency and deflection amplitude, respectively, for the experimental and numerical simulations. In the case of increasing the porosity, output voltage increases in bimorph patches and decreases in cylinders. The output voltages measured are equal to 2.25 V and 1.75 V for 0% and 80% porosities, respectively. The measured values of output current are equal to 2.53 \(\upmu\)A and 1.74 \(\upmu\)A in the cases of 0% and 80% porosity, respectively. The current intensity decreases with increasing the porosity However, the total cumulative voltage and power decrease. The maximum and minimum powers of 5.1 \(\upmu\)W and 3.0 \(\upmu\)W, respectively, are obtained from the device. From 1 K\(\Omega\) to 300 K\(\Omega\) resistance, the output power increases rapidly but above 700 K\(\Omega\) resistance, the output power increases slowly and tends to constant value. When applied vibration acceleration increases, the output voltage of the bimorphs and cylinders increases, too. The total cumulative voltage and power increase, when the applied acceleration increases. The maximum voltage and power are equal to 4.5 V and 21 \(\upmu\)W, respectively, at 10 \(\upmu\)s\({}^{-2}\) acceleration. The inner structure of the porous polymer does not change, and the mechanical strength of the porous piezoceramic is relatively low. Porosity increases the d\({}_{31}\) sensitivity of the piezoelectric patches.  
R.K.H.: software, original draft preparation, review and editing, setting up experimental work, methodology. A.V.C.: review and editing, setting up experimental work, production of calculations and conclusions, validation of research methods. L.A.P.: supervision, validation of research methods funding acquisition, resources, formal analysis. V.E.Y.: production of calculations and conclusions, formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant from the Ministry of Science and Higher Education of Russia supported by Southern Federal University, grant No. VnGr-07/2020-04-IM.  
Not applicable.  
The authors declare no conflict of interest.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 2 Tchebichef Moments  
Moments are scalar quantities used to characterize a function and to capture its significant features. Moments have been widely used for description of the shape of a probability density function and in classic rigid-body mechanics to measure the mass distribution of a body [32]. Similarly, an image can be considered as any piece-wise continuous real function \(f(x,y)\) of two variables defined on a compact support \(D\subset R\times R\) and having a finite nonzero integral. From the mathematical point of view, image moments are equivalent to the projection of an image onto a polynomial basis. Therefore, the general moment \(G_{pq}\) of an image is defined as:  
\[G_{pq}=\iint\limits_{D}\psi_{pq}(x,y)f(x,y)dxdy \tag{1}\]  
where \(p\), \(q\) are non-negative integers; \(p+q\) is called the order of the moment, and \(\psi_{pq}(x,y)\) is a polynomial basis function defined on \(D\).  
For a digital image, the integral in Equation (1) is usually approximated by discrete summation. On an \(N\times N\) image coordinate space, the moment integral can be expressed in the following discrete domain:  
\[G_{pq}=\sum\limits_{x=0}^{N-1}\sum\limits_{y=0}^{N-1}\psi_{pq}(x,y)f(x,y) \tag{2}\]  
where \(0\leq p,q,x,y\leq N-1\).  
Image moments are region-based descriptors. Depending on the polynomial basis used, we can recognize various image moments. In the same way, Tchebichef moments of order \(p+q\) on an image of size \(N\times N\) pixels are defined using the following polynomial basis function:  
\[\psi_{pq}(x,y)=t_{p}(x)t_{q}(y) \tag{3}\]  
where \(t_{p}(x)\), \(t_{q}(y)\) are the discrete orthogonal Tchebichef polynomials.  
The discrete orthogonal Tchebichef polynomials \(t_{p}(x)\) of order \(p\) and length \(N\) are defined as in Reference [33]:  
\[t_{p}(x)=p!\sum_{k=0}^{p}\left(-1\right)^{p-k}\left(\begin{array}{c}N-1-k\\ p-k\end{array}\right)\left(\begin{array}{c}p+k\\ p\end{array}\right)\left(\begin{array}{c}x\\ k\end{array}\right) \tag{4}\]  
It can be observed that the value of \(t_{p}(x)\) given by Equation (4) grows as \(N^{p}\), which is not suitable for defining moments [34]. To ensure the numerical stability of higher order functions, a scaled version of Tchebichef polynomials is proposed as follows [35]:  
\[\widetilde{t}_{p}(x)=\frac{t_{p}(x)}{\sqrt{\rho(p,N)}} \tag{5}\]  
where the squared-norm \(\rho(p,N)\) is given by:  
\[\rho(p,N)=(2p)!\left(\begin{array}{c}N+p\\ 2p+1\end{array}\right) \tag{6}\]  
Above all, for an \(N\times N\) image \(f(x,y)\), the Tchebichef moments \(T_{pq}\) of order \(p+q\) are defined as:  
\[T_{pq}=\sum_{x=0}^{N-1}\sum_{y=0}^{N-1}\widetilde{t}_{p}(x)\widetilde{t}_{q}( y)f(x,y) \tag{7}\]
# Techno-Functional Properties and Recent Advances in the Manufacturing of Whey Beverages: A Review
## 3 Impact of Wastewater from Cheese Production on the Environment  
Sweet whey, previously deemed a waste byproduct, has been transformed into a valuable raw resource with considerable market potential owing to technological breakthroughs and research. This advancement has enabled the creation of value-added components, thus accelerating market growth for these goods relative to other dairy constituents [5]. Cheese production is environmentally demanding, mainly due to the high amount of whey, which is often considered waste. The whey is mixed with wash water from the plant and ends up in the wastewater treatment plants. This wastewater can vary according to the final product, the type of system, and the operating methods used. Generally, it consists of various concentrations of milk or processed dairy products, in addition to wash water that contains both alkaline and acidic chemicals used during the cleaning of bottles, tanks, and processing equipment, including tools and pumps [11; 13].  
Characteristic indicators of wastewater include Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) values [32]. The Chemical Oxygen Demand (COD) of cheese whey can vary significantly, typically ranging between 50.000 and 80.000 mg/L, while its Biological Oxygen Demand (BOD) generally falls within 40.000 to 60.000 mg/L. Whey represents a waste load that is 100-175 times greater than the equivalent volume of domestic wastewater [28]. The rapid depletion of oxygen in soil, caused by the breakdown of proteins and sugars in whey, presents a substantial disposal issue, especially given the high volumes involved [32; 33]. Wastewater from cheese and casein production is notable for its high levels of these indicators, as dairy wastewater contains a high concentration of organic substances, such as serum proteins, milk sugar (lactose), mineral salts (calcium, magnesium, phosphorus, and sodium), and milk fat [11].  
[PERSON] [34] describes three methods of handling acid whey: direct use or disposal (e.g., as animal feed or a food ingredient), direct stabilisation (physical or chemical treatments to improve stability against microbial degradation), and conversion processes (the transformation of lactose into other compounds through biotransformation or chemical reactions).