esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: MaxIndependentSet
#### 4.2.2 Full QAOA Mapping  
Mixing operators.Let \(H_{\text{CX}}=\sum_{i=1}^{n}H_{\text{CX},v_{i}}\). We defined two distinct types of mixers:  
* The _simultaneous_ controlled-\(X\) mixer, \(U_{\text{sim-CX}}(\beta)=e^{-i\beta H_{\text{CX}}}\), and;
* A class of _partitioned_ controlled-\(X(\beta)\) mixers, \(U_{\mathcal{P}\text{-CX}}(\beta)=\prod_{i=1}^{|\mathcal{P}|}\prod_{v\in P_{i}} U_{\text{CX},v}\),  
where \(\mathcal{P}\) is an ordered partition of the partial mixers, in which each part contains mutually commuting partial mixers. Since the partial mixers often do not commute, different ordered partitions often result in different mixers. By design, both the simultaneous and partitioned mixers restrict evolution to the feasible subspace. With respect to the second design criterion, there is nonzero transition amplitude from the \(|0\rangle^{\otimes n}\) state corresponding to the empty set to all other independent sets; for \(0<\beta<\pi/2\), we got terms corresponding to products of the individual control-bit-flip operators for all subsets of vertices, including those corresponding to independent sets. (For those subsets \(S\) not corresponding to independent sets, the product will result in a independent set \(V^{\prime}\in S\) that does not include vertices in \(S\) which have neighbors whose controlled-bit-flip preceded them in the partition order; thus, different ordered partition affects the amount of nonzero amplitude in the states corresponding to independent sets.) Two applications of any such partitioned mixer result in nonzero amplitude between any two feasible states. An interesting question is how different ordered partitions affect the ease with which good parameters can be found and the quality of the solutions obtained.  
Partitioned mixers are generally easier to compile than the simultaneous mixer, since the partitioned mixer is a product of multiqubit-controlled-not operators (generalized Toffoli gates) on at most \(D_{G}+1\) qubits. Altogether, this construction uses \(n\) partial mixers, which can then be compiled into single- and two-qubit gates. For many graphs, partitions in which each set contains multiple commuting partial mixers exist, reducing the depth.  
Phase-separation operator.The objective function is the size of the independent set, or \(f(\mathbf{x})=\sum_{i=1}^{n}x_{i}\), which we could translate into a phase-separating Hamiltonian via substitution of \((I-Z)/2\) for each binary variable. Instead, we used affine transformation of the objective function \(g(\mathbf{x})=n-2f(\mathbf{x})\), which, when translated, yields a phase separation operator of a simpler form:  
\[U_{\mathrm{P}}(\gamma)=e^{-i\gamma H_{g}}=\prod_{i=1}^{n}e^{-i\gamma Z_{i}}, \tag{25}\]  
which is simply a depth-1 circuit of \(n\) single-qubit Z-rotations.  
Initial state.A reasonable initial state is the trivial state \(\ket{s}=\ket{0}^{\otimes n}\) corresponding to the empty set.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 2 Preliminary
### Original BP Decoding Algorithm  
The BP algorithm for polar codes is based on a DFG. A polar code with code length \(N\) is represented by an \(n\)-stage DFG. We use \((i,j)\) to indicate the nodes of the DFG, where \(i\) indicates node index and \(j\) indicates column index. The leftmost nodes in the DFG mean \(j=0\), such as the blue and black nodes in the Figure 1. Similarly, the rightmost nodes in the DFG mean \(j=n\), as shown in the grey node column.  
The classic BP DFG is depicted in Figure 1. Each stage consists of \(N/2\) processing elements (PE), where a fundamental PE is shown in Figure 2. Figure 1 consists of three stages and each stage has four PEs. One PE has four nodes and each node is associated with two types of messages, a right-to-left message \(L_{j,i}\) and a left-to-right message \(R_{j,i}\). \(L_{j,i}\) and \(R_{j,i}\) are in the form of LLR. The message propagation rules are as follows:  
\[\left\{\begin{array}{l}L_{j,i}=g(L_{j+1,i},R_{j,i+2^{j}}+L_{j+1,i+2^{j}}),\\ L_{j,i+2^{j}}=g(L_{j+1,i},R_{j,i})+L_{j+1,i+2^{j}},\\ R_{j+1,i}=g(R_{j,i},L_{j,i+2^{j}}+R_{j+1,i+2^{j}}),\\ R_{j+1,i+2^{j}}=g(R_{j,i},L_{j+1,i})+R_{j,i+2^{j}}.\end{array}\right. \tag{5}\]  
where  
\[g(x,y)=\alpha\cdot\text{sign}(x)\cdot\text{sign}(y)\cdot\min(|x|,|y|), \tag{6}\]  
where \(\alpha=0.9375\) follows from the scaling factor used in [21]. \(L_{j,i}\) and \(R_{j,i}\) need to be initialized as follows  
\[L_{j,i}=\left\{\begin{array}{ll}0,&\quad i\
eq n+1,\\ \mathcal{L}_{n}^{(j)},&\quad i=n+1.\end{array}\right. \tag{7}\]  
\[R_{j,i}=\left\{\begin{array}{ll}0,&\quad j\in\mathcal{A},\\ +\infty,&\quad i=1,\;\;j\in\mathcal{A}^{C},\end{array}\right. \tag{8}\]  
where \(\mathcal{L}_{n}^{(j)}\) denotes the LLR of the \(j\)-th received bit. The \(+\infty\) in the first column of \(R_{j,1}\) indicates the prior knowledge carried by the frozen bits.  
Figure 1: Decoding factor graph with \(N=8\).  
The maximum number of iterations \(I_{\max}\) is preset, and the decoding is terminated when the number of iterations is equal to \(I_{\max}\) or when the CRC check is satisfied. The hard decisions of \(\hat{u}_{i}\) and \(\hat{x}_{i}\) are estimated as  
\[\hat{u}_{i}=\left\{\begin{array}{ll}1,&\quad L_{j,1}+R_{j,1}<0,\\ 0,&\quad L_{j,1}+R_{j,1}\geq 0.\end{array}\right. \tag{9}\]  
\[\hat{x}_{i}=\left\{\begin{array}{ll}1,&\quad L_{j,n+1}+R_{j,n+1}<0,\\ 0,&\quad L_{j,n+1}+R_{j,n+1}\geq 0.\end{array}\right. \tag{10}\]
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
###### Abstract  
Modern real-valued optimization problems are complex and high-dimensional, and they are known as \"large-scale global optimization (LSGO)\" problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC'13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.  
large-scale global optimization; cooperative coevolution; evolutionary algorithms; computational intelligence; evolutionary computation  
1  
[PERSON], [PERSON] Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +  
Footnote †: This paper is an extended version of our paper published in IOP Conference Series: Materials Science and Engineering, Volume 1047, III International Conference MIST: Aerospace 2020: Advanced Technologies in Aerospace, Mechanical and Automation Engineering (Aerospace 2020), Krasnoyarsk, Russia, 20-21 November 2020.
# Adsorption of Heavy Metals from Low Concentration Solutions onto Dried _Chlamydomonas reinhardtii_
## 2 Materials and Methods
### Biosorption Procedure  
#### 2.3.1. Dried Cells  
First, biosorption from single-ion solutions were performed. Stock metal solutions of 1 mg/mL stored at room temperature (20 \(\pm\) 1 \({}^{\circ}\)C) were used to reach the desired concentration of metal in the contacting aqueous solution. After adjusting the pH of the metal solutions to pH 6 and pH 7 (Hanna Instrument HI 221 pH Meter, Woonsocket, RI, USA) by means of NaOH (1 M) and HCl (1 M), the biosorption of Pb(II), Cd(II), and Cr(III) ions, respectively, 0.5 mg/L, 0.2 mg/L, 0.2 mg/L (data coming from BATs reference documents of different productive sectors: ceramic manufacturing, ferrous metals processing, and surface treatment of metals and plastics [46; 47; 48]), was carried out by contacting the solution with the microalgal biomass (0.3 g/L corresponding to 5 \(\times\) 10\({}^{9}\)\(\pm\) 1 \(\times\) 10\({}^{9}\) cells/L). Throughout all the experiments, the pH remained constant at the fixed initial pH, with a maximum variation of \(\pm\)0.5. After the reaction time of 2 days, samples of 10 mL of solution were taken and filtered by syringe filters of 0.22 \(\upmu\)m pore diameter (Rotilabo(r), Karlsruhe, Germany) to remove all the cells. The filtrate was analysed with ICP-OES (Spectro Amtek, Spectro Analytical Instruments GmbH, Kleve, Germany), to determine the residual metal concentration. The remaining solution was centrifuged, to recover the biomass, or, when necessary, it was kept continuing the experiment. Centrifugation was performed at 4000 rpm for 5 min, followed by washing with MilliQ 3 to 5 times. Then, the recovered algae were frozen at \(-\)20 \({}^{\circ}\)C for a few hours and then freeze-dried at \(-\)50 \({}^{\circ}\)C for 48 h to obtain metal-loaded algal biomass (Figure 2). Also, multi-ion experiments were carried out, maintaining the same initial concentrations and operative conditions as the single-ion adsorption experiments: 0.5 mg/L of Pb(II), plus 0.2 mg/L of Cr(III), plus 0.2 mg/L of Cd(II), for a total heavy metals amount of 0.9 mg/L.  
Figure 1: Dried microalga preparation scheme.  
Figure 2: Biosorption procedure and samples (powder) preparation.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 6 Joint Network-Channel Coding over Cooperative Link  
In this section, the performance of joint network-channel coded link is analyzed and simulated. A simple channel coding scheme is used along with network coding to improve the performance of overall system. The existing literature on JNC coding in wireless networks spans multiple access networks [35; 36] with 4-5 nodes and two-way relay networks [37; 38] using different data combining techniques like superpositioning [39] and XOR-ing [40].  
Considering the scenario of WBAN, the number of nodes and power consumption are critical while designing a system and minimum node scenario is a priority, so the said system just considers three nodes and the data is XOR-ed at the relay node. The basic idea behind choosing this scenario is the reduced node deployment and less power consumption for XOR operation. The scheme in which superposition is done on the relay node demands greater energy. The power level is increased at the relay node by superimposing two signals. To bring it back to normal, the power level is scaled at the relay, which, in turn, reduces the power received at receiving nodes. The overall system, hence, loses the performance as the signal received at both of the nodes is less useful.  
A similar three nodes scenario is used, where the nodes \(S\) and \(D\) want to exchange their data via two-way relay cooperation using relay node \(R\). The data to be transmitted among \(S\) and \(D\) is indicated by \(n_{S}\) and \(n_{D}\), respectively.
## 1 Introduction  
Digital media consumption worldwide is exploding alongside wired and wireless Internet access speeds and bandwidth since the COVID-19 pandemic started. Consumers now have access to media content they want, anytime and anywhere, at cheap prices. YouTube, Netflix, Hulu, Amazon Prime, and Roku are now threatening the existence of traditional media markets [1]. These new over-the-top (OTT) services are defined as \"video contents provided through paths based on the internet or internet protocol (IP)\" [2]. OTT services are spreading rapidly because consumers can select personalized content and platforms based on their own schedules. The global OTT market is expected to reach USD 1039.03B by 2027 with an average annual growth rate of 29.4% from 2020 to 2027 [3]. In the US (the largest OTT market), there were 182-million OTT subscribers as of 2019 [4], and YouTube (the most popular digital broadcasting platform), was watched by 84.2% of the US digital video viewers; Netflix was watched by 67.6% [5]. As the OTT market has grown, various global service providers and telecommunication carriers have competed intensely.  
As some indoor and outdoor activities were restricted owing to COVID-19, demand for video streaming skyrocked as movie heaters shut down. Subscription video on demand, the most typical OTT method, is a monthly subscription model that allows users to view all platform content for a gateway fee. Subscriptions have increased by approximately 10% since COVID-19 started [6]. Similar phenomena have led to a significant increase in consumer traffic consumption. As the OTT market grows rapidly, an analysis methodology specialized for the OTT market is required. Focusing on the OTT market, we intend to propose a market analysis methodology that will be required by players participating in the OTT market.
## 1 Introduction  
The textile industries effluents contain dyes, provoking several effects on ecosystems and effects on human health [1; 2; 3; 4; 5; 6; 7]. Most industries use synthetic azo dyes with one or more azoic groups (-N=N-) that may form aromatic amines causing mutagenic and carcinogenic effects on aquatic organisms [8; 9; 10; 11; 12; 13; 14].  
Many research papers have focused on the removal methods of azo dyes in order to decrease their effect on environment and human health. These techniques could be classified in biological biodegradation, biosorption, bioaccumulation, applying bacteria, or algae) [15; 16; 17; 18], chemical (ozonation, advanced oxidation processes (AOPs), electrochemical degradation, photocatalysts) [19; 20; 21; 22] and physical methods (adsorption, ion exchange) [23; 24; 25], depending on the type of process. Adsorption is considered to be quite attractive in terms of effectiveness and ultimate removal, while catalytic oxidation in terms of cost and of a high degree of dye elimination [26; 27; 28].  
Recently, significant research efforts have been focused on the environmental application of manganese oxides MnO\({}_{x}\) (x = 1, 2, 4/3), because of their low cost, abundance, environmental friendliness, and high adsorption capacity [29; 30]. In particular, manganese oxide/nanomaterials demonstrated oxidative and catalytic activity for the degradation of dyes and organic compounds from aqueous waste [31; 32]. Furthermore, manganese oxides are powerful oxidizing/catalytic agents [33].  
Carbonaceous materials like activated carbon and graphene oxide have been used for dye removal due to the advantage of their selective adsorption capacity through a strong \(\pi\)-\(\pi\) interaction of the benzene rings [34; 35; 36]. Recently, activated carbon and graphene oxide were used as support for manganese oxides for the synthesis of nanocomposites for a dye degradation [37; 38] while sodium hydroxide modified graphene oxide was used as support of manganese oxides for the preparation of novel nanocatalysts applied to the catalytic degradation of bisphenol A [39; 40]. The novel nanocomposites presented a sufficient performance of bisphenol A degradation attributed to a synergetic effect of adsorption onto the carbonaceous support and the oxidation onto the manganese oxides' surface.  
In the current study, sodium hydroxide modified graphene oxide was used as support of manganese oxides (MnO\({}_{2}\) and Mn\({}_{3}\)O\({}_{4}\)) and were tested for the degradation of the azo dye Reactive Black 5. The nanocomposites showed to possess high activity for the degradation/oxidation of the dye at ambient conditions, without light irradiation, which was higher than that of the precursors' manganese oxides. The increased degradation can be attributed to the synergistic effect of the adsorption onto modified graphene oxide and oxidation onto manganese oxides. The presence of radicals further increased the dye degradation.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 6 Discussion
### Pore Effect  
In the frame of the relationship between rock porosity and IRT, it was demonstrated that the presence of macroscopic voids on a framed rock causes surface temperature variations [45]. In particular, macropores occurring within an oven-heated rock sample are labeled by a higher surface temperature than the rock matrix, with the greatest values recurring at the most persistent voids. This suggests that the IRT output of a rock surface is controlled by the presence of voids (Figure 5b). Such an effect, from now on referred to as the \"pore effect\", was detected herein; for example, at the carbonate rock group, where the massive or microporous dolostone, limestone and calcislitte keep a lower emissivity than the macroporous travertime and calcarenite samples (Figure 4a). It must be underlined that this discussion point only involves the presence of macroscopic porosity, whose surface temperature can be detected by an infrared camera, regardless of the porosity percentage of the rock. A similar outcome can be highlighted for basalts, in their massive (\(\varepsilon=0.94\)) and vesicular (\(\varepsilon=0.965\)) varieties (Figure 5c,d). Even in this case, samples affected by macropores returned a higher emissivity due to the higher surface temperature of vesicles. Once it is further proved that the presence of voids affects the surface temperature  
Figure 5: (**a**) Explanatory sketch for the slight emissivity differences occurring within the rocks: a specimen of mismatite characterized by a characteristic emissivity ranging between 0.90 and 0.91; (**b**) pore effect on the surface temperature variation of a travertime sample; (**c**) example of vesicular basalt with voids highlighted by the highest surface temperatures; (**d**) example of massive basalt where no voids affect the specimen surface. In this case, phenocrysts are highlighted by temperature contrast with the groundmass.  
distribution on the rock surface and considering that voids can be represented both by pores and by cracks, a reasonable hint for future studies could be represented by the tentative correlation between the emissivity dispersion and the presence of cracks/damage zones within the rock itself.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible Array Antennas for a Sub-6 GHz 5G Band
#### 4.2.1 Slot/Slit Based Flexible Antenna

Table 7:Figure 13: _Cont._  
The array has a gain of 12 dB and achieved the isolation of more than \(-30\) dB for a two-port configuration. The array achieves an efficiency of 80% within the band of operation. The SAR analysis of the proposed array antenna is conducted with a body model that consists of different layers exhibiting a different conductivity as well as relative permittivity values and is 0.0989 W/kg for 10 g of tissue which is well below the international standards. The array antenna has a directive radiation pattern, as shown in Figure 13c,d. Another slot-based array is proposed in [64], where the antenna array consists of a slot incorporated in the radiating patch and a parasitic patch towards the center that provides proper isolation. The array consists of graphene and semi-flexible epoxy fiber as the radiating patch and substrate, respectively. A partial ground plane is made to achieve the bandwidth enhancement. The antenna operates for a bandwidth of 2.21-5.13 GHz. A measured gain value of 2.73 and 3.744 dBi for 2.40 and 4.0 GHz is achieved. The antenna has an efficiency of 78%. The array antenna shows a directional radiation pattern at 2.45 and 4.0 GHz. The bending analysis of the antenna is performed along different angles and it is seen that there is a slight fluctuation in the reflection coefficient at a bending angle of 14\({}^{\circ}\) in the E-plane and 18\({}^{\circ}\) in the H-plane, respectively.
## 4 Applications in Optimization
### Non-Evolutionary Chaotic Optimization Algorithms
#### 4.5.1 Chaotic Optimization Algorithms
In [95], the tested values of step size in COLM were described as follows:  
* COLM(1): the step size \(\lambda\) equals 0.01
* COLM(2): the step size \(\lambda\) equals 0.05
* COLM(3): the step size \(\lambda\) equals 0.10
* COLM(4): the step size \(\lambda\) linearly decreases from 0.10 to 0.01
* COLM(5): the step size \(\lambda\) linearly increases from 0.01 to 0.10  
In each case study, fifty independent runs were carried out for each of the COLM methods involving fifty different initial trial conditions \(y1(0),y(0)\). From the case studies and comparison of the results through the five tested COLM approaches, it has been shown that the step size parameter \(\lambda\) is essential to obtaining a good convergence profile. In this context, the \(\lambda\) parameter regulates the trade-off between the global and local exploration abilities of the chaotic local search. Shayeghi et al. [96; 97], using the COA introduced by Coelho, proposed a multi-objective design for multi-machine Power System Stabilizers (PSSs). The PSS parameter tuning problem was converted to an optimization problem which was solved by COLM using the COLM(4) strategy. The authors suggested that COLM, which has the features of easy implementation, short execution time, and robust mechanisms of escaping from the local optimum, is a promising tool for engineering applications. The chaotic sequences increase its convergence rate and improve the precision of the results. The effectiveness of the proposed method was tested on a multi-machine power system for a wide range of loading conditions and disturbances. Eigenvalues analysis provided the satisfactory damping level for the system modes, especially the low frequency modes, for systems with the proposed COA-based tuned PSSs, especially according to the time domain-based multi-objective function. Time domain simulations showed that the oscillations of the synchronous machines can be quickly and effectively damped for power systems over a wide range of loading conditions using the proposed PSSs. The same authors used this algorithm for an output feedback damping controller design of a static synchronous compensator (STATCOM) [98]. The same algorithm was used by Jalilvand et al. [99] to design a Unified Power Flow Controller (UPFC) based on damping controllers using COA. Their eigenvalues analysis and simulation results showed that it has good performance for damping low-frequency oscillations and can improve transient stability under different operating conditions. The COLM algorithm was later improved by Hamaizia and Lozi [100] by locally averaging the global search, then performing a few steps of chaotic local search around every point obtained by the chaotic series. In another study, Bououden and Abdelouahab [101] used transformations to modify the density of the Lozi map and combined Lozi and logistic maps during global and local search [102].