esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### MMC System  
#### 2.3.1 Configuration and Operation of MMC  
The configuration of the three-phase MMC system is depicted in Figure 6. The MMC in this study is a 7 (seven)-level modular converter. The MMC consists of three-phase legs (Leg A, Leg B, and Leg C). The legs have two similar arms, i.e., upper arm (p) and  
Figure 4: One-lumped mass model of wind turbine generator.  
Figure 5: The pitch blade controller model.  
lower arm (n). Each arm consists of six identical sub-modules (SM), an arm inductor (\(L_{arm}\)), and an arm resistance (\(R_{arm}\)), arranged in series. The arm inductors are used to limit circulating arm current between the three-phase units and the valve short circuit current, and to contribute to interface between the AC grid system and the MMC. The sub-module has a two-level half-bridge configuration composed of two IGBTs with anti-parallel diodes and a capacitor (\(C_{SM}\)). Three-phase AC terminal voltage is connected to each phase leg on the common point connection between the upper and lower arms through a phase reactor consisting of an inductor (\(L_{f}\)) and a resistor (\(R_{f}\)). The reactor is used as a filter for AC voltage and current [24].  
In the operation of the MMC, the DC link voltage (\(V_{dc}\)) charges the capacitor (\(C_{SM}\)) in the entire sub-module, in which the sub-modules are switched into an inserted state or a bypassed state. In the inserted state, the sub-module capacitor can be charging or discharging, depending on the voltage reference polarity. The switching state of the sub-module conditions is given in Table 1. Figure 7 shows the direction of sub-module arm current (\(I_{arm}\)) according to S1 and S2 switching state. The positive polarity of the arm current direction is indicated in red, and the negative polarity of the arm current direction is indicated in blue.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Switching State** & \multicolumn{2}{c}{**Terminal Voltage of**} & \multicolumn{1}{c}{**Arm Current Polarity**} & \multicolumn{1}{c}{**Status of Capacitor**} \\ \hline
**S1** & **S2** & **SM (\(V_{SM}\))** & & & \\ \hline OFF & ON & 0 & + & \multicolumn{1}{c}{Bypass} \\ ON & OFF & \(V_{Gsm}\) & + & \multicolumn{1}{c}{Charging} \\ OFF & ON & 0 & \(-\) & \multicolumn{1}{c}{Bypass} \\ ON & OFF & \(V_{Gsm}\) & \(-\) & \multicolumn{1}{c}{Discharging} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Switching state of the Sub-Module conditions.  
Figure 6: Configuration of MMC.
## 2 Construction of a Water Resource Optimization Model  
### Objective Function  
The fundamental goal of water resource optimization is to achieve efficient utilization of regional water resources and ensure sustainable regional development. Based on this premise, the model in this paper aims to maximize economic benefits, minimize the total regional water shortage, and reduce pollutant emissions as its objective functions.  
(1) Economic Objective: The objective function is to maximize the water supply benefits.  
\[f_{1}(x)=max\sum_{k=1}^{K}\left[\sum_{j=1}^{J}\sum_{i=1}^{I}(b_{j}^{k}-c_{j}^{k })x_{ij}^{k}a_{i}\lambda_{j}\right] \tag{1}\]  
In the formula, \(k\) represents the water supply sub-region. \(i\) denotes water supply sources, categorized into surface water, groundwater, transferred water, and reclaimed water. \(j\) represents water users, classified as domestic, production (primary, secondary, and tertiary industries), and ecological; \(b_{j}^{k}\) and \(c_{j}^{k}\) are the water supply benefit coefficient and cost coefficient for water user \(j\) in supply sub-region \(k\), Unit: CNY; \(a_{i}\) is the water supply priority coefficient for water source \(i\); \(\lambda_{j}\) is the water use equity coefficient for water user \(j\);\(x_{ij}^{k}\) is the water allocation volume from water source \(i\) to water user \(j\) in supply sub-region \(k\), Unit: \(10^{4}\) m\({}^{3}\). (2) Social objective. The objective function is to minimize the total water shortage of water users. \[f_{2}(x)=min\sum_{k=1}^{K}\left[\sum_{j=1}^{I}\sum_{i=1}^{I}(D_{j}^{k}-x_{ij}^{ k})\right]\] (2) In the formula, \(D_{j}^{k}\) represents the total water demand of water user \(j\) in supply sub-region \(k\), Unit: \(10^{4}\) m\({}^{3}\). (3) Ecological and Environmental Objective: The objective function is to minimize the pollutant emissions. \[f_{3}(x)=min[\sum_{k=1}^{K}\sum_{j=1}^{J}\alpha_{j}^{k}\theta_{j}^{k}(\sum_{ i=1}^{I}x_{ij}^{k})]\] (3) In the formula, \(\alpha_{j}^{k}\) is the Sewage Discharge Coefficient for water user \(j\) in supply sub-region \(k\); and \(\beta_{j}^{k}\) represents the pollutant discharge concentration for water user \(j\) in supply sub-region \(k\), Unit: mg/L.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 4 Future Issues
How the algorithm selects a new CH in the case of failure and how to perform re-clustering while minimizing the message overheads must also be investigated.
## 2 FM-CSK and QCPSK Communication Systems
### Vilnius Chaos Oscillator  
The chaos oscillator selected for the study of the two systems is the Vilnius chaos oscillator [21]. This oscillator was chosen due to the circuit's simplicity and low power consumption. The circuit diagram of the Vilnius chaos oscillator is given in Figure 3.  
Figure 1: Block diagram of QCPSK data transmission system.  
Figure 2: Block diagram of FM-CSK data transmission system.  
The mathematical model of the Vilnius chaos oscillator is represented with a set of differential equations:  
\[\left\{\begin{array}{l}C_{1}\frac{dv_{C_{1}}}{dt}=i_{L_{1}},\\ L_{1}\frac{dt_{L_{1}}}{dt}=(k-1)R_{1}i_{L_{1}}-v_{C_{1}}-v_{C_{2}},\\ C_{2}\frac{dv_{C_{2}}}{dt}=i_{0}+i_{L_{1}}-i_{D},\end{array}\right. \tag{1}\]  
where \(v_{C_{1}}\) and \(v_{C_{2}}\) are voltages across the capacitors \(C_{1}\) and \(C_{2}\); \(i_{L_{1}}\) is the current of the inductor \(L_{1}\); \(i_{D}\) is the current of the diode \(D_{1}\); \(i_{0}\) is the current of the resistor \(R_{4}\); \(k\) is the gain of operational amplifier \(OA\). The given circuit has the following nominals: \(C_{1}=1\) nF; \(C_{2}=150\) pF; \(L_{1}=1\) mH; \(R_{1}=1\) k\(\Omega\); \(R_{2}=R_{3}=10\) k\(\Omega\); \(R_{4}=20\) k\(\Omega\); \(V_{1}=3\) V; \(k=2\). The nonlinear current-voltage characteristic of the diode is given in (2).  
\[i_{D}=i_{S}\cdot\left(\exp(\frac{ev_{D}}{k_{B}T})-1\right), \tag{2}\]  
where \(i_{s}\) is the saturation current; \(v_{D}\) is the saturation voltage; \(e\) is the electron charge, \(k_{B}\) is the Boltzmann constant and \(T\) is the temperature [21].  
The differential equations show that the state variables of the oscillator are the voltages of the capacitors \(C_{1}\), \(C_{2}\) and the current of the inductor \(L_{1}\). The QCPSK and FM-CSK use \(v_{C_{1}}\) and \(v_{C_{2}}\) state variables of the oscillator as \(X_{1}\) and \(X_{2}\) (as well as \(Y_{1}\) and \(Y_{2}\)). In the case of \(i_{L_{1}}\), the voltage of the \(R_{1}\) is taken as \(X_{3}\), knowing that \(i_{L_{1}}=i_{R_{3}}\). The state variables form a chaotic attractor, and the projections of Vilnius chaos oscillator's attractor are shown in Figure 4a-c.  
Figure 4: Vilnius chaos oscillator’s attractor projections.  
Figure 3: Circuit diagram of Vilnius chaos oscillator.
## 6 Evaluation of Perceptual Transparency with a Listening Test  
We performed a listening test to validate the full-band representation of musical instrument sounds with eaQHM. The aim of the test was to evaluate whether full-band modeling with eaQHM resulted in resynthesized musical instrument sounds that are perceptually indistinguishable from the original recordings. The 21 sounds **in bold** in Table 1 were selected for the listening test, which presented pairs _original_ and _resynthesis_. The participants were instructed to listen to each pair as many times as necessary and to answer the question \"Can you tell the difference between the two sounds in each pair?\" Full-band (FB) resynthesis with eaQHM (using a harmonic template with \(K=K_{\text{max}}\) sinusoids) was used for all 21 musical instrument sounds. For nine of these sounds, half-band (HB) resynthesis with eaQHM (using a harmonic template with \(K=K_{\text{max}}/2\) sinusoids) was also included as control group to test the aptitude of the listeners and compare against the FB version. All HB versions were placed at random positions among the FB, so the test presented 30 pairs overall. The listening test can be accessed at [59].  
In total, 20 people aged between 26 and 40 took the test. The participants declared themselves as experienced with listening tests and familiar with signal processing techniques. Figure 7 shows the result of the listening test as the percentage of the people who answered \"no\" to the question, indicating that they cannot tell the difference between the original recording and the resynthesis. In general, the result of the listening test shows that full-band modeling with eaQHM results in perceptually indistinguishable resynthesis for most musical instrument sounds tested. The figure indicates that 10 out of the 21 FB sounds tested were rated perceptually identical to the original by 100% of the listeners. As expected, most HB sounds fall under 30% (except _Tenor Tombone_) and most FB sounds lie above 70% (except _Pan Flute_). Table 1 shows that _Tenor Tombone_ is played at C3 and _Pan Flute_ at C5. Theenor Trombone sound is not bright, which indicates that there is little spectral energy at the higher frequency end of the spectrum. Thus, the HB version synthesized with fewer partials than \(K_{\text{max}}\) was perceived as identical to the original by some listeners. The Pan Flute sound contains a characteristic breathing noise captured as AM-FM elements in eaQHM. However, the breathing noise in the full-band version sounds brighter than the original recording and most listeners were able to tell the difference.  
Figure 7: Result of the listening test on perceptual similarity of full-band (FB) and half-band (HB) resynthesis with eaQHM compared to the original recording. The sounds used in the listening test appear **in bold** in Table 1.
## 3 Results  
### Calculation of Gas Velocity in the Gas-Liquid Interface  
Determination of the gas flow rate in the gas-liquid interface was the key point of this experiment. The closed air chamber is a rectangular chamber, and with a certain length (\(L\)), width (\(W\)), and height (\(H\)) from the liquid level to the gas chamber end, the sectional area (\(S\)) and volume (\(V\)) of this gas chamber can be determined by these constants:  
\[V=LWH=LS \tag{1}\]  
Therefore, the average velocity of the gas flow in the gas chamber is  
\[u=\frac{F}{S}=\frac{F}{WH} \tag{2}\]  
where \(u\) is the gas flow rate in the volatilization chamber (cm\(\cdot\)s\({}^{-1}\)) and \(F\) is the gas flow meter in the pipeline reading from the gas flowmeter (cm\({}^{3}\cdot\)s\({}^{-1}\))  
In Formula (2), we can see that \(u\) is the average gas flow rate in the volatilization chamber, and obviously, the gas flow rate above the water surface is uneven in the volatilization chamber. If \(u\) is used to describe the gas flow rate in the liquid surface, the height from the water surface to the top of the volatilization chamber must be small enough, and only in this way can we approximate the average gas flow rate in the volatilization chamber as the gas flow rate in the \"gas-liquid\" interface.  
In this research, we conducted experiments with six different pumping rates. The results are shown in Figure 1. When the pumping rate was above 2.4 L\(\cdot\)min\({}^{-1}\) (equivalent to 40 cm\({}^{3}\cdot\)s\({}^{-1}\)), continuing to increase the pumping rate did not significantly increase the amount of ammonia volatilization. This implies that when the gas velocity reaches a certain value, all the ammonia volatized from the system can be absorbed completely and quickly, and the gas flow rate is not the limiting step of the ammonia volatilization.  
In this experiment, when the space height of the volatilization chamber above the solution level was unified to 0.5 cm, the width of the volatilization chamber was 5 cm, the volatility of the gas flowmeter was adjusted to 40 cm\({}^{3}\cdot\)s\({}^{-1}\) (\(H\) = 0.5 cm, \(W\) = 10 cm, \(F\) = 40 cm\({}^{3}\cdot\)s\({}^{-1}\)), and all data were constant, the gas flow rate in the \"gas-liquid\" interface could be calculated using Formula (2):  
\[u=\frac{40}{10\times 0.5}=8\ \text{cm}\cdot\text{s}^{-1}=0.08\ \text{m}\cdot\text{s}^{-1}\]  
Because the ammonia volatilization process is positively correlated to the pH, NH\({}_{4}\)\({}^{+}\) concentration, and temperature, and the experimental factors we set in this pumping rate experiment were the maximal value, so setting the pumping rate to 2.4 L\(\cdot\)min\({}^{-1}\) and the corresponding gas flow rate to 0.08 m\(\cdot\)s\({}^{-1}\) in our subsequent experiments was appropriate.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 1 Introduction
The ATSC 3.0 standard is a new international broadcasting standard formulated by ATSC in 2013. The ATSC 3.0 standard gives an encoding algorithm for LDPC codes and supports code rates in the range of 2/15 to 13/15, and it has capacity-approaching performance and low encoding/decoging complexity [18; 19; 20; 21]. In this paper, we construct spatially coupled (SC)-LDPC codes and QC-LDPC codes based on ATSC LDPC codes. The structure of QC-LDPC codes is convenient for memory storage and has low encoding/decoging complexity, while the variable nodes at the boundary of SC-LDPC codes are more likely to be successfully decoded. Due to the special structures of QC-LDPC codes and SC-LDPC codes, we can further improve the reconciliation efficiency by constructing QC-LDPC codes and SC-LDPC codes based on ATSC LDPC codes and then employ these codes to the CVQKD system. The numerical results show that the proposed codes have higher reconciliation efficiency than the original codes, which can improve the performance of secret key rate and transmission distance. This paper is organized as follows. In Section 2, we provide preliminaries on multidimensional reconciliation protocol and LDPC codes in ATSC 3.0. In Section 3, we introduce the proposed SC-LDPC codes and QC-LDPC codes based on ATSC LDPC codes for information reconciliation. Section 4 shows the performance of our proposed LDPC codes. Finally, the conclusions are drawn in Section 5.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 2 System Model
### Centralized Collaborative Spectrum Sensing System Model  
In cognitive radio systems, centralized collaborative spectrum sensing has drawn extensive attention due to its high perceived accuracy and low complexity of perceived terminals. As shown in Figure 1, the centralized collaborative spectrum sensing system consists of multiple cognitive users and a data fusion center.  
In the cognitive wireless communication system, the available frequency band \(B\) is usually divided into \(N\) copies of non-overlapping sub-channels. During the period of spectrum sensing, \(K\) primary users randomly occupy different sub-channels for communicating (\(K\) is much smaller than \(N\)). In the stage of spectrum sensing, cognitive users typically use the energy detection method [10] to sense the spectral state and send the perceived results to the data fusion center, before the system enters into the centralized fusion stage.  
According to the authenticity of the data transmitted by the cognitive user, we will divide the cognitive users into honest cognitive users (HUs) and malicious cognitive users (MUs). Malicious cognitive users tamper with local perceived results to the data fusion center and with the development of the perceived technology, the malicious cognitive users increase its degree of concealment by reducing the attack probability. This makes it difficult to quickly and correctly identify malicious cognitive users, thereby reducing the perceived performance [11; 12]. Therefore, it is an urgent problem to identify malicious cognitive users accurately and efficiently in the case of high concealment of malicious cognitive users.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 3 FDWA Algorithm
### Fuzzy Rules and Clarification  
First, the input and output variable domains of the fuzzy controller are defined as \([0,1]\), which is a continuous domain. The fuzzy sets are defined as 0, PS, PM, and PB, i.e., zero, positive small, positive medium, and positive large. Tables 1 and 2 show the design method fuzzy rules, \(\alpha\) and \(\beta\), as follows.  
1. When \(O_{d}\), \(\theta_{d}\in\) PB. The mobile robot should reduce the value of \(\theta_{d}\theta_{d}\) as shown in Figure 3, \(\theta_{d}=|\theta_{1}-\theta_{2}|\), and should reduce the value of \(\alpha\) and \(\beta\) so that the difference between the current trajectory and the desired trajectory is reduced and the path is smoother. This will make, the velocity function increase in weight and make the robot accelerate toward the target.
2. When \(\theta_{d}\in PB,O_{d}\in PS\). The robot will reduce the value of \(\alpha\) to make the path smoother. The \(\beta\) value should be large to make the robot avoid the obstacle.
3. When \(\theta_{d}\in PS\), \(O_{d}\in PB\). The value of \(\alpha\) should be made moderate to maintain the smoothness. The value of \(\beta\) should be deceased; this will make the velocity function increase its share in the weight and make the robot accelerate toward the target.
4. When \(\theta_{d}\), \(O_{d}\in PS\). The value of \(\alpha\) should be increased and the value of \(\beta\) should be increased to ensure that the robot passes the obstacle safely.  
Figure 2: Membership function graph. (**a**) Input membership; (**b**) output membership.  
The fuzzy logic inference uses the Mamdani inference method. The clarity value \(U\) is (\(\alpha\), \(\beta\)). The exact value of \(\alpha\), \(\beta\) at the current moment is obtained after defuzzification to prevent the FDWA algorithm from falling into local minima. The maximum membership averaging method can effectively eliminate extreme cases, so the maximum membership averaging method is used to solve the problem, as in Equation (12). \(A(o_{j})=\max(A(o))\), \(j=1,2\ldots n\).  
\[U=\sum\
olimits_{j=1}^{n}A\left(o_{j}\right)/2 \tag{12}\]
## 3 Results and Discussion
### Electrochemical Behavior of CFTX in the Presence of Different Ions
#### 3.1.2 Spectrophotometric Study  
UV-Vis spectra of CFTX, before and after CV performed on platinum electrode in solutions of sodium halides were recorded (Figure 2). As shown in Figure 2, the CFTX spectral characteristics in the presence of F\({}^{-}\), Cl\({}^{-}\), Br\({}^{-}\) ions (Figure 2a-c) are similar to that obtained in distilled water (inserted spectrum in Figure 2a). In the water, the CFTX absorption maxima were recorded at same wavelength (\(\lambda_{\text{max1}}\) = 270 nm, \(\lambda_{\text{max2}}\) = 240 nm) as the ones obtained in the presence of F\({}^{-}\), Cl\({}^{-}\), Br\({}^{-}\) ions.  
Analyzing the UV-Vis spectra of CFTX shown in Figure 2 it was found that (i) in the presence of F\({}^{-}\), the spectra recorded before and after CV are superimposed (Figure 2a), indicating that the antibiotic is stable, acting by adsorption on the platinum electrode surface; (ii) Cl\({}^{-}\) ions activate CFTX molecules leading to their partial decomposition, after CV, which causes the concentration decrease and the absorbance one, respectively, with out altering the CFTX spectrum shape or the baseline and wavelength corresponding to the absorbance maximum (Figure 2b); (iii) Br\({}^{-}\) ions affect the CFTX spectrum, after CV, highlighting a single absorption maximum (Figure 2c), confirming that CFTX molecules interact with the electrogenerated species leading to structural modifications of the functional groups and/or of the initial structure; (iv) in the presence of iodide ions the CFTX spectrum before CV is completely different from those previously discussed, probably due to the instantaneous chemical interactions/interferences between the antibiotic and the iodide species from solution (Figure 2d). Thus, after the CV, the environment composition is completely changed, and CFTX is no longer identifiable (Figure 2d) due to its total degradation.  
Correlating the data obtained from cyclic voltammetry, when an external stimulus was applied for a short time (40 s), with those provided by UV-Vis spectrophotometry, it was found that (i) CFTX is stable in the presence of the fluoride ion; (ii) CFTX degrades, in the presence of the chloride ion, without highlighting the occurrence of a reaction product; (iii) CFTX degrades in the presence of the bromide ion revealing the appearance of a new reaction product, in a short time; (iv) CFTX interacts rapidly with iodide species, causing the drastic change of reaction environment composition and CFTX chemical structure alteration, before measurements begin.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.8 Power Saving in PONs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} PON \\ **(Downlink/Uplink)** \\ \end{tabular} & \begin{tabular}{c} **Max Power** \\ **Budget** \\ \end{tabular} & \begin{tabular}{c} **Split Ratio for** \\ **100 Mb/s, User** \\ **Activity 10\%** \\ \end{tabular} & \begin{tabular}{c} **Split Ratio for** \\ **1 Gb/s, User** \\ **Activity 50\%** \\ \end{tabular} & \begin{tabular}{c} **OLT-Transceiver** \\ **Power** \\ **Consumption** \\ \end{tabular} & \begin{tabular}{c} **ONU/ONT-Transceiver** \\ **Always ON** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} GPON(2.5G/1.25G) \\ XGON(10G/2.5G) \\ NG-PON2(10G/10G) \\ \end{tabular} & 28 dB & 1:64 & 1:8 & 3.8 W & 3.2 W & 0.6 W/0.1 W \\
\begin{tabular}{c} GPON(1.25G/1.25G) \\ NG-PON2(10G/2.5G) \\ \end{tabular} & 35 dB & 1:256 & 1:32 & 12.0 W & 5.6 W & 1.2 W/0.1 W \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A power consumption example which shows the impact of sleep mode. Figure 9: Typical OLT/ONU/ONT block diagram of TWDM-PON. As it is shown in Figure 9, a typical OLT or ONU/ONT is equipped with the following components [39, 41]:  
* AWG device, as WDM coupler/decoupler. A Fabry-Perot (F-P) laser as a transmitter (DML/EML lasers in GPON/XG-PON, respectively or uncooled tunable lasers in the case of ONU/ONT). APD as a receiver (or PIN Diode in GPON). Burst-mode (BM) laser driver to drive the F-P laser. Burst-mode trans-impedance amplifier (BM-TIA) to translate and amplify downstream photocurrent into voltage. Limiting post-amplifier (LP-A) to reshape the output voltage of TIA. Clock and data recovery (CDR) system. Serializer/de-serializer (S/Des) of data. However, several degrees of integration are applicable to these components that produce different energy consumption levels, as is indicated in Table 6[42].