esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Mechanisms of ZnO-Based Antimicrobial Nanomaterials
#### 4.2.2 Chemical Effect of ZnO-Based Nanomaterials on Antibacterial  
In the process of exploring the antibacterial mechanism of ZnO-based nanomaterials, three main chemical antibacterial mechanisms were obtained: generation of reactive oxygenated species (ROS) [158], release of Zn\({}^{2+}\) ions [31], and photoinduced production of H\({}_{2}\)O\({}_{2}\)[159]. The ROS mechanism is basically the same as the photocatalytic mechanism mentioned in Section 4.2.1.  
Similar exhaustive studies carried out by [PERSON] et al. [31] and [PERSON] et al. [160] demonstrated that the toxicity of Zn\({}^{2+}\) ions to cells is one of the mechanisms for the sterilization of ZnO-based nanomaterials. The concentration of ZnO NPs in ultrapure water in the toxicity test was 5 mg/L. To compare the cytotoxicity, a Zn\({}^{2+}\) ion solution also prepared in ultrapure water was used (concentration below 0.1 mg/L). TEM images of the treated G\(-\) strains of _E. coli_ are shown in Figure 10. It is evident that the morphology of _E. coli_ was deformed after modification with ZnO-based nanomaterials or Zn\({}^{2+}\) ion solution. Figure 10b,c shows intracellular fluid leakage due to Zn\({}^{2+}\) ions and osmotic stress. The experimental results showed that the cytotoxic effects of ZnO NPs and Zn\({}^{2+}\) ion-treated solutions on _E. coli_ were comparable. This fully proves that the release of Zn\({}^{2+}\) has a positive effect on antibacterial, and it also shows that it is one of the antibacterial mechanisms of ZnO-based nanomaterials (Figure 11).  
Figure 10: TEM images of **(a)** untreated _E. coli_ cells, **(b)** treated with ZnO nanoparticles, and **(c)** treated with Zn\({}^{2+}\) ions solution in ultrapure water. Reprinted/adapted with permission from Ref. [31]. Copyright © 2022, American Chemical Society.  
Figure 9: Basic mechanism of ZnO photocatalysis. Reprinted/adapted with permission from Ref. [27]. Copyright © 2022, Elsevier B.V.  
Besides the light-induced reactive oxygen species produced by ZnO NPs, many pieces of literature consider H\({}_{2}\)O\({}_{2}\) as the main substance for antibacterial activity against dermo bacteria [28,161]. Negatively charged ROS cannot penetrate bacterial cell walls, but H\({}_{2}\)O\({}_{2}\) can also easily penetrate bacterial cell walls. [PERSON] et al. [162] believed that the H\({}_{2}\)O\({}_{2}\) produced by ZnO slurry was the main reason for the biocidal mechanism. It can also be hypothesized that after H\({}_{2}\)O\({}_{2}\) or HO disrupts the membrane, ROS can penetrate the cell wall and enter the intracellular space, thereby enhancing the biocidal effect (Figure 12).
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs
### CV-QKD Implementation Technique
#### 4.2.3 Modulation and Modulator  
Gaussian modulation is the most commonly used scheme for CV-QKD as it enables a high secret key rate. However, it requires high-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) and complex reconciliation algorithms, posing a significant challenge. To address these limitations, discrete modulation techniques such as phase shift keying (PSK) and quadrature amplitude modulation (QAM) have been proposed [46; 47; 48]. Lower modulation levels allow for low-resolution DACs and ADCs, reducing complexity and making the system more cost-effective for OANs. To increase the secret key rate, higher order modulation levels or probabilistic constellation shaping (PCS) can be adopted [47]. In PCS, the probability mass function of modulation levels follows a Gaussian distribution, but the number of discrete levels is limited, enabling a compromise between Gaussian and discrete modulation schemes.  
An IQ modulator can be used to generate the CV-QKD signals. When implementing the GG02 protocol, independent Gaussian-distributed electrical signals are applied to the I- and Q-axes. IQ modulators offer improved tolerance to overshooting or ringing in the driving signals [51]. An alternative approach involves cascading an amplitude modulator (AM) and a phase modulator (PM) in series. For the GG02 protocol, a Rayleigh-distributed electrical signal is applied to the AM, and a uniformly distributed signal is applied to the PM. This approach is more cost-effective and introduces lower loss than an IQ modulator. However, when detecting either IQ quadrature, the resolution of the PM signal may vary depending on the AM modulation levels, which is a drawback of this approach.
# Experimental Quantum Message Authentication with Single Qubit Unitary Operation
## 3 Security Analysis
### Origin Authentication of Quantum Message  
To clarify the origin of the quantum message, the proposed quantum message authentication operates by using not only the secret key pre-shared by [PERSON] and [PERSON] but also [PERSON]'s private key. In general, message authentication guarantees the origin of message authentication by using a secret key previously shared by [PERSON] and [PERSON]. At this time, as the user who can create a message authentication code (MAC) pair can be [PERSON] or [PERSON], the origin of the message may become unclear. On the other hand, in the proposed protocol, [PERSON] generates a QMAC pair \(\left|\,M\,\right\rangle_{u}\left|\,S\,\right\rangle_{d}\) of Equation (4) by using a private key sequence \(S=(s_{1},s_{2},\ s_{3},\ \ldots,\ s_{N})\) known only to her; thus, the possibility of such a dispute is very low.
## 3 Results
### Tribological Behavior
#### 3.5.1 Scratch Tests  
A surface scratch test on the coating can be considered as a single point abrasive wear test [38]. It essentially involves a diamond stylus scratching the coating at a progressively increasing load to determine the \"critical loads\" at which fracture within the coating occurs or the coating is detached from the substrate to signify its failure. In the present study, as the coating thickness and the diameter of the stylus tip were of comparable magnitude, detachment of coating from the substrate was not possible. However, the extent and nature of damage in the coating with increasing load were tracked, with an AE sensor also continuously picking up the number of signals from the indenter to aid in the assessment of cohesive strength between successive splats. The analysis of the test also involved a qualitative comparison of the scratch track width (observed through an optical microscope) with increasing load, which can be correlated with the cohesive strength of the coatings.  
Figure 8 shows the OM images of scratches at intermediate normal loads. With an increase in normal load, the edges of the scratch track clearly show increasing damage in the form of larger pull-outs from the coating. One plausible reason for such pull-outs can be initiation and subsequent propagations of cracks in the coating due to an increase in tensile stresses induced by the motion of the scratch tip with increasing load [39]. This crack propagation could cause the linking of pores which could lead to detachment of the material from the coating. This microscopic observation is also supported by the increasing intensity of acoustic emission signals with an increasing normal load on the coating, as shown in Figure 9.  
A closer examination of the scratch and the acoustic emission signal reveals that the critical load corresponding to the onset of noticeable damage in the coating is around 75-80 N for these Al\({}_{2}\)O\({}_{3}\)-YSZ coatings. It may also be noted that, at loads beyond the above-mentioned critical load, local regions with more pull-outs in the vicinity of the scratch are observed. Such an instantaneous increase in scratch width can be caused not only by local defects such as pores but are also attributable to the accumulation of stresses and their relief upon cracking. This is also apparent from the sudden rise and fall in the intensity of acoustic emission signals (Figure 9). The effect of internal stress and cracking on acoustic emission signals has been previously reported [40]. The observed instantaneous change  
Figure 8: Optical microscope images of the residual scratch track on the SPS Al\({}_{2}\)O\({}_{3}\)-YSZ coating. The normal load in Newton (N) is indicated and the arrow depicts the scratch direction.  
in intensity of signals, together with the sizes of the fractured areas, reveals that the cohesion of the coating is high. If cohesion was low, the damages would be much smaller, correlated to the splat sizes, and located very close to the track.
## 2 Results and Discussion
### Basic Composition of Samples Collected during RO-EP Treatment of Wine  
RO-EP treatment of wines D and E achieved decreases in alcohol content of 2.6 and 2.4%, respectively (Table 3). Retentate fractions generated by RO yielded a higher ethanol concentration than the initial wine in both cases, for reasons given above. That is, although both ethanol and water permeate the RO membrane, water permeates at a higher rate due to its considerably lower molecular weight. The removal of alcohol due to EP can be clearly seen by the significant decrease in ethanol content for permeate fractions before and after EP (i.e., >10% abv difference), with the ethanol subsequently being transferred to the strip water (which contained ~8-9% abv). Small but significant differences in density were observed, in particular for permeate and strip water fractions. Glycerol concentrations increased in retentate due to RO fractionation, with the lower levels observed in permeate being substantially affected by EP, despite glycerol not being detected in strip water. The levels of glycerol in wine out samples were slightly higher than in untreated wine, which may impact the perception of viscosity [33].  
The pH of wine, retentate and permeate remained relatively consistent, despite the increased TA of retentate and decreased TA of permeate following RO as a consequence of the retention of organic acids, including succinic and lactic acids (Table 3). Notably, EP had no effect on TA which may be due to the levels of organic acids pre-EP being quite low. VA was not affected by RO-EP treatment.  
The intensity of wine color increased significantly due to dealcoholization (Table 3), but with minimal impact on wine hue, similar to that of wines A, B and C in Trial 1 (Table 1). CIELab measurements suggested the color increase was reflective of wines becoming darker, with significantly lower \(L^{*}\) and higher \(a^{*}\) values obtained. The color properties of retentate fractions were augmented (relative to wine samples before or after treatment) due to the concentration of anthocyanins and derived pigments during RO. This was further reflected by the complete absence of color in permeate and strip water fractions. For wine D, the \(\Delta E^{*}\) following RO-EP treatment was only 2.0, which is not expected to be evident to the naked eye, but for wine E, \(\Delta E^{*}\) was 3.7, and so the change in color would likely be perceptible. The impact of RO treatment on wine color was more obvious due to the intensification of color in retentate from both wines; with \(\Delta E^{*}\) being >10 (Table 3), this color change would also be expected to be readily observed.
## Results and Discussion
### Constitutive Model of Hot Deformation Behavior  
The dependence of stress on hot deformation parameters, such as deformation temperature and strain rate, may be described by the Zener-Hollomon parameter [27]:  
\[Z=\dot{\varepsilon}e^{\frac{Q}{RT}} \tag{1}\]  
where \(\dot{\varepsilon}\) is strain rate (s\({}^{-1}\)), \(T\) is temperature (K), and \(Q\) is apparent activation energy (J/mol). The relation between the Zener-Hollomon parameter and the stress is commonly expressed by three types of empirical equations, such as power law, the exponential, and the hyperbolic sine dependences.  
\[Z=\mathrm{A}_{1}\alpha^{\mathrm{n}_{1}} \tag{2}\]  
\[Z=\mathrm{A}_{2}\mathrm{e}^{\beta\sigma} \tag{3}\]  
\[Z=\mathrm{A}_{3}[\sinh(\alpha\sigma)]^{\mathrm{n}_{2}} \tag{4}\]  
where A\({}_{1}\), A\({}_{2}\), A\({}_{3}\), n\({}_{1}\), n\({}_{2}\), \(\beta\), and \(\alpha\) are constants of the material.  
Equation (4) is a universal function for a wide range of stresses. Nevertheless, the determination of the value adjustable constant \(\alpha\) is needed to find the constants of Equations (2) and (3):  
\[\alpha\approx\frac{\beta}{\mathrm{n}_{1}} \tag{5}\]  
The values of A\({}_{1}\), A\({}_{2}\), A\({}_{3}\), n\({}_{1}\), n\({}_{2}\), \(\beta\), \(\alpha\), and Q constants at different strain values were calculated using the multiple linear regression of Equations (2)-(4) in linearized form. As one can see in Figure 5, the values of A\({}_{3}\), n\({}_{2}\), \(\alpha\), and Q\({}_{3}\) constants are significantly dependent on true strain due to changes in the  
Figure 4: Microstructure of 13 CrMoNbV steel after hot deformation at different temperatures and strain rates.  
microstructure during deformation. To include the strain (\(\varepsilon\)) influence too, the constitutive Equation (3) was modified by using fourth-degree polynomial dependence of the material constants [28,29]:  
\[\ln(\text{A}_{3})=34.48+12.98\varepsilon-59.7\varepsilon^{2}+100.6\varepsilon^{3 }\ -55.1\varepsilon^{4} \tag{6}\]  
\[\alpha=0.0188-0.0307\varepsilon+0.12\varepsilon^{2}-0.17\varepsilon^{3}+0.08 \varepsilon^{4} \tag{7}\]  
\[\text{Q}_{3}=422.18+154.57\varepsilon-690.3\varepsilon^{2}+1156.9\varepsilon^{ 3}-632.7\varepsilon^{4} \tag{8}\]  
\[\text{n}_{2}=3.14+7.11\varepsilon-32.4\varepsilon^{2}+52.3\varepsilon^{3}-27.9 \varepsilon^{4} \tag{9}\]  
As one can see in Figure 6a, the average difference between calculated and experimental flow stress values is 3.3%. In addition, the accuracy of the constitutive model was checked by the modeling of the samples' compression at a strain rate of 1 s\({}^{-1}\) using the FEM method. The comparison between measured and simulated force vs. displacement dependences for 1100 and 1200 \({}^{\circ}\)C temperatures (Figure 6b) shows the satisfactory predictability of the model.  
Figure 5: The fits of the dependence of \(\ln(\text{A}_{3})\), \(\alpha\), \(\text{Q}_{3}\), and \(\text{n}_{2}\) on true strain by a fourth-order polynomial.
# Online Distributed User Association for Heterogeneous Radio Access Network
### Load Balancing Problem  
The definition of \"load\" in a cellular network changes according to the environment. The authors in Refs. [14; 17; 19] consider the load of a BS as the number of UEs attached to it. If the maximum number of UEs supported by a BS varies and the average time frequency resource used by UEs is constant, then the traffic flow experienced by the BS is directly proportional to the attached number of UEs. This definition of load is simple to consider and easy to implement. If a BS has many UEs attached to it, then the total resources will be divided among the attached UEs. Hence, the per-user data rate gets reduced, which is not desirable from the UEs perspective. Authors in Ref. [20] consider the application-aware target rate and instantaneous physical rate to calculate the load of a BS. The UE's satisfaction index is calculated by taking the inverse of the load. If a UE is associated with a highly loaded BS, then it will be less satisfied. Traditional user association schemes in a HetNet environment with small cells result in lower UE experience, as well as load imbalance in the network; hence, a novel user association scheme should be adopted. Thus, our objective is to design a novel user association and load-balancing model in the new HetNet environment.  
In this paper, we propose low-complexity online distributed algorithms for cell selection in a HetNet environment, while considering two types of load definitions. The BSs and UEs are treated as bidders and objects, respectively, such as that in online combinatorial auctions. We present an analytical model to show the near-optimal performance of our proposed solutions.  
The remainder of this paper is organized as follows. Section 2 presents some related works. In Section 3, the system model is presented and the problem is formulated. Distributed cell-selection algorithms are presented in Section 4. Performance analysis is given in Section 5. The performance evaluation, discussion, and conclusion are given in Sections 6-8, respectively.
## 1 Introduction
Therefore, the traditional biological methods are not as effective as would be desirable. Physical-chemical methods such as nanofiltration, ultrafiltration, ultrasound, hydrothermal carbonization, and different advances oxidation processes have been reported for the treatment of OMW reducing its chemical oxygen demand (COD) and phenol content [17; 18]. However, these techniques have several drawbacks, such as the addition of chemicals, fouling of the membrane, and high pressure and temperature conditions [5]. Therefore, the search for alternative methods that allow the treatment of OMW and AL in an economic and eco-friendly way is crucial. The use of fungi has been described as a promising alternative over the use of bacteria for OMW treatment due to its ability to grow under adverse conditions and to produce a great variety of extracellular enzymes that make possible the degradation of recalcitrant compounds [19]. In this way, fungi can break down the complex recalcitrant compounds making them more assimilable to be used by themselves or by the bacteria in a subsequent treatment [20; 21]. The OMW treatment by fungi has mainly focused of removing COD, phenolic content, and colour, as well as obtaining by-products with biotechnological interest, such as fungal enzymes [22; 23]. White-rot fungi can degrade the lignin present in lignocellulosic wastes due to the release of enzymes, mainly lignin peroxidase and manganese peroxidase [24]. These fungi have been investigated for the treatment of recalcitrant compounds and colour degradation of OMW, obtaining good results. For example, [PERSON] et al. [25], who studied the capacity of several strains of _Pleurotus_ and _Ganderna_ fungi to treat OMW, reported significant removals of COD, TOC, and phenolic compounds, as well as a reduction of the toxicity of the effluent. COD degradations around 50% have been achieved by the fungus _Plunerochaete chrysosporium_ immobilized on loofah [26]. Great removals of colour, phenolic compounds and COD have also been obtained when OMW was treated with fungi from genus _Aspergillus_[22; 27]. As far as we know, the bioremediation with fungi has been mainly applied to treat OMW or pomace coming from the three-extraction system. However, its application to AL waste, obtained from the two-phase extraction system, has been hardly studied. Therefore, the main objective of this study was to investigate the capability of the white-rot fungus _Planerochaete chrysosporium_ to treat AL waste, to reduce its COD, colour, and phenolic compounds.
# Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking
## 4 Simulation
### Scenario 1
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **MOTP** & **Miss Rate** & **False Positive Rate** & **Mismatches** & **MOTA** \\ \hline \multirow{3}{*}{\(\beta=1\) s} & LMB & 0.6484 & 7.64\% & 7.05\% & 53 & 72.19\% \\  & HGM-LMB & 0.5029 & 4.74\% & 3.83\% & 33.22 & 83.2\% \\  & JPHGM-LMB & **0.4119** & **3.51**\% & **2.98**\% & **27.04** & **86.82**\% \\ \hline \multirow{3}{*}{\(\beta=2\) s} & LMB & 0.6102 & 7.83\% & 6.46\% & 50.54 & 73.21\% \\  & HGM-LMB & 0.4741 & 6.21\% & 4.14\% & 32.02 & 81.72\% \\  & JPHGM-LMB & **0.3893** & **4.66**\% & **3.13**\% & **25.34** & **85.94**\% \\ \hline \multirow{3}{*}{\(\beta=3\) s} & LMB & 0.5618 & 8.9\% & 5.39\% & 47.3 & 74.01\% \\  & HGM-LMB & 0.4497 & 8.28\% & 3.52\% & 31.16 & 80.49\% \\  & JPHGM-LMB & **0.3802** & **7.34**\% & **3.48**\% & **24.38** & **83.15**\% \\ \hline \multirow{3}{*}{\(\beta=4\) s} & LMB & 0.5472 & 10.71\% & 4.73\% & 43.66 & 73.76\% \\  & HGM-LMB & 0.433 & 11.14\% & 3.14\% & 29.42 & 78.44\% \\ \cline{1-1}  & JPHGM-LMB & **0.3814** & **10.31**\% & **3.01**\% & **22.96** & **81.03**\% \\ \hline \hline \end{tabular}  
* MOTP is the multiple object tracking precision and MOTA is the multiple object tracking accuracy.  
\end{table} 
Table 4: Clear mot metrics of filters with different occlusion duration. Figure 11: The OSPA of three filters with different occlusion duration: (**a**) \(\beta=1\) s, (**b**) \(\beta=2\) s, (**c**) \(\beta=3\) s and (**d**) \(\beta=4\) s.  
When occlusion happened, there are two aspects. Firstly, the number of targets will be underestimated, filters take several time steps to obtain the true cardinality estimation. There is no measurement available to update prediction state of occluded target, the estimation error of occluded target can not be avoided because of the model mismatch and measurement noise, the OSPA distance increases as shown in Figure 11. However, the superiority of JPHGM-LMB can be found clearly in Table 4, JPHGM-LMB always achieves larger MOTA than other filters as occlusion duration increases. Secondly, the structure of targets is broken to some extent when occlusion happened, the structure can be recovered if occlusion duration is short, then the structure information can also enhance the tracking performance, see Figure 11a,b. However, if occlusion duration is long enough, then the structure of targets may be destroyed permanent, the performance of HGM-LMB and JPHGM-LMB will degrade, see Figure 11c,d.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 3 Applications and Use Cases of FANETs  
There are various applications of FANETs including environmental monitoring and emergency communications, and UAV services as shown in Figure 5. In this section, we highlight four common real-life 5G/6G business use cases and the applications of FANET.  
Figure 5: Applications and use cases of FANET.
## 2 Introduction to Basic Theory
### Hilbert-Huang Transform  
The Hilbert-Huang transform (HHT) is used to draw the envelope spectrum of the test vibration signal, which can more easily view the fault characteristics of each component.  
The main content of Hilbert-Huang transformation consists of two parts. The first part is empirical mode decomposition (EMD), which was proposed by [PERSON]; the second part is Hilbert spectrum analysis (HSA). The HHT of the signal \(x(t)\) is as follows:  
\[\hat{x}=\frac{1}{\pi}\int\limits_{-\infty}^{+\infty}\frac{x(\tau)}{t-\tau}d \tau=\frac{1}{\pi}\int\limits_{-\infty}^{+\infty}\frac{x(t-\tau)}{\tau}d\tau= x(t)\times\frac{1}{\pi t} \tag{4}\]  
The HHT is equivalent to an interesting filter, in which the amplitude of the spectral components remains unchanged, but their phases are shifted by one [32,33]. Its unit impulse response is \(1/\pi t\).  
The inverse of the HHT is as follows:  
\[x(t)=-\hat{x}(t)\times\frac{1}{\pi t} \tag{5}\]  
The HHT has the following properties:  
1. The signal \(x(t)\) and its HHT energy spectrum and amplitude spectrum are the same, but the phase spectrum is different.
2. Signal \(x(t)\) and \(\hat{x}(t)\) are orthogonal to each other, namely: \[\int\limits_{-\infty}^{+\infty}x(t)\hat{x}(t)dt=0\] (6)
3. If you perform \(x(t)\) twice using HHT, you will get -\(x(t)\), namely \(\hat{x}(t)=x(t)\).