esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### MAC Layer Design for WBANs
#### 4.4.1 Energy-Efficient Channel Access and Synchronization Mechanisms
Different policies are employed for in-body (implantable) and on-body (wearable) nodes. In the first case, a star topology is considered and the register bank is maintained at the coordinator, whereas in the second case, a mesh topology is assumed, and nodes keep statistics for their neighbors traffic. The second operation phase begins after several cycles, when the protocol converges to a steady state and the beacon interval is adapted to the wakeup times of each node. Finally, another way of coordinating transmissions in order to improve energy efficiency is by employing an additional low-power passive receiver at the nodes. This idea has been adopted in [89], where the nodes are equipped with two radio interfaces: (i) the main transceiver for the transmission of data; and (ii) a passive receiver with ultra-low power consumption, employed as a wakeup radio. During sleep mode, the nodes switch off their main radio interface and maintain only the secondary radio. The nodes exit sleep mode and activate the main transceiver only after receiving a wakeup signal by the coordinator in the secondary channel. Synchronization mechanisms:The implementation of all these energy-efficient schemes usually requires some synchronization between the coordinator and the nodes. A widely adopted synchronization mechanism is the transmission of timestamps within control packets sent by the coordinator (usually beacons or ACKs) [79, 80, 82, 87]. Another approach is proposed in [86], where a predefined time interval, called wakeup fallback time, is employed. In case of communication failure, master and slave reattempt communication after this mutually known fallback time elapses. A more advances synchronization scheme called Adaptive Guard Band Algorithm (AGBA) with Drift Adjustment Factor (DAF) has been implemented within the TDMA-based MedMAC protocol [90]. The AGBA introduces two guard bands in the beginning and the end of each time slot. These guard bands have a variable duration which dynamically increases as time elapses from the previous synchronization point. The DAF is a correction factor employed for the calculation of the guard band duration, taking into account the estimated time offset between the clocks of the nodes and the coordinator. Finally, specific healthcare scenarios can offer unique opportunities for synchronization mechanisms. A typical example is H-MAC [91], a TDMA-based scheme that uses heartbeat rhythm to achieve synchronization. Exploiting the fact that many biosignals share the cardiac rhythm pattern, nodes placed on a single patient can process their sensory data to extract a common time reference, without the need of turning on their radio to receive beacons.
## 3 Inversion of Mechanical Parameters of Rock Mass by Orthogonal Test
### Inversion Scheme  
According to the comprehensive geological histogram of the working face of the mining area, the mining area of the working face can be roughly divided into three types of rock masses, namely, fine sandstone, sandy mudstone, and loose layer. According to experience, the mechanical parameters of rock mass that have a significant effect on rock mass deformation were mainly considered. The elastic modulus (E), Poisson's ratio (\(\upmu\)), cohesion (C), and internal friction angle (\(\upmu\)) of the three types of rock masses participated in the inversion, but not their tensile strength (T) and density (\(\upmu\)). Because FLAC3D numerical simulation software was used for the simulation, it is necessary to convert the elastic modulus (E) and Poisson's ratio (\(\upmu\)) into the bulk modulus (K) and shear modulus (G) according to Equation (1) [41]. A total of 12 parameters participated in the inversion. For each of the 12 parameters, three layers of data were collected. The corresponding relationship between the rock mass and the factors is shown in Table 1, and the test factor and level settings are shown in Table 2.  
\[K=\frac{E}{3(1-2\mu)} \tag{1}\]  
\[G=\frac{E}{2(1+\mu)} \tag{2}\]  
In this experiment, the orthogonal design table L27313 was selected as the orthogonal design table of three levels and 13 factors [42]. Among them, the first 12 columns were the 12 parameters involved in the inversion, and the 13 th column was used as the error column  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Rock Mass** & **K (GPa)** & **G (GPa)** & **C (MPa)** & **\(\upmu\)** & **\(\upmu\)** (\({}^{\circ}\)) \\ \hline Fine sandstone & K1 & G1 & C1 & \(\upmu\)1 \\ Sandy mudstone & K2 & G2 & C2 & \(\upmu\)2 \\ Loose layer & K3 & G3 & C3 & \(\upmu\)3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correspondence between the rock mass and factors.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Levels** & **K1** & **G1** & **C1** & **\(\upmu\)1** & **K2** & **G2** & **C2** & **\(\upmu\)2** & **K3** & **G3** & **C3** & **\(\upmu\)3** \\ \hline
1 & 1.0 & 0.8 & 0.8 & 24 & 1.0 & 0.8 & 1.0 & 24 & 0.2 & 0.1 & 0.1 & 18 \\
2 & 1.2 & 1.0 & 1.0 & 26 & 1.2 & 1.0 & 1.2 & 26 & 0.4 to participate in the significance test of the orthogonal test. According to the orthogonal design table L27313, the factors of each column of the orthogonal design table and their corresponding test factor levels can be obtained. The different levels were replaced by the corresponding rock mass mechanical values to form the test scheme.
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 5 Radio Coverage Planning
#### Application of Radio Planning in LoRaWAN Networks  
LoRaWAN elements and components typically need to operate on batteries and are often deployed over vast geographical areas, which aligns with the general concept of the Internet of Things (IoT) [66; 67]. Therefore, given the aforementioned benefits of radio coverage planning, its application to LoRaWAN networks seems essential, particularly in the context of smart cities and related issues such as the following [68]:  
1. Gateway location selection: Given the long range of LoRa, selecting optimal gateway locations is critical to ensure the best coverage for end nodes.
2. Signal propagation analysis: Due to the long-distance nature of LoRa technology, it is important to understand how signals will propagate in different environments, including urban, rural, and industrial settings.
3. Optimizing transmitting power and receiver sensitivity: To balance power consumption and range, coverage planning must consider gateway transmitting power and the sensitivity of receiving equipment.
4. Interference management: It is important to manage potential interference in environments with many LoRa or other devices operating on similar frequencies.
5. Redundancy planning: To ensure network reliability, coverage planning may involve placing additional gateways to guarantee service continuity in case one of them fails.  
* Compliance with regulations: Ensuring compliance with local regulations on radio emissions and band usage is crucial for LoRaWAN networks to meet legal requirements.  
Ensuring optimal radio coverage is crucial for latency-critical IoT applications. In such cases, achieving the desired level of Quality of Service (QoS) is possible through the use of dedicated radio interface and network architecture solutions that primarily benefit from flexibility and service-oriented approaches [56].
# Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment
###### Abstract  
The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP.  
soft sensor; wastewater treatment; modelling; resource efficiency; exhaustive search Article
## 3 Methodology
### Research Technique  
After the initial assessment of the data, it was observed that the data does not meet the assumption of approximate normal distribution. Therefore, a nonparametric statistical testing approach was adopted ([PERSON], 1963). The research questions were tested with a binomial test and Wilcoxon signed test, which assumes independent observations, symmetry (about zero), and no ties ([PERSON], 1963). The binomial tests show the possibility of occurrence of one of the two possible outcomes. In this research, it was used to see the respondent's point of view on the significance of dependent variables. The significance of each dependent variable in all cases were tested with a cut-off point set at 3, which means all organizations are divided into two groups. First, is less than and equal to 3, are those whose opinions are from very strong to moderate intense, and group 2 is greater than 3, whose responses are from little to no when measured on the respective test scale. The test proportion was set at 50%. On the other hand, the [PERSON] sign test showed if the mean rank of two related samples differ, or in other words, it allows the analysis of matched-pair data, based on differences, or for a single sample ([PERSON], 1963). In this research, this test was used to see which dependent variables have more or less significant impact within same group, when compared to other variables by comparing signs and mean values. In addition to above mentioned statistical tests, a descriptive analysis was completed to see the respondents view point on a maturity time line for innovative technologies. The data was analyzed using statistical software SPSS,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organization Type** & **Frequency** & **\%** \\ \hline Association & 02 & 3.90 \\ Other & 05 & 9.80 \\ Supplier & 08 & 15.7 \\ Consultant & 12 & 23.5 \\ Highway operator/Toll agency & 24 & 47.1 \\ \hline Total & 51 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Breakup of participating organizations.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 4 Proposed Compensation Scheme for NPMSA
### Simulation Results
After combining the PS-PMSA with the INPMSA, the improved PS-PMSA can further achieve decoding capabilities consistent with the NPMSA, and still discard the unnecessary operations in check node updating. For the design of the hardware to the 5G NR standard, we simulated and analyzed the decoding performance using the 5G NR BGs 1 and 2. In BG 1, the analysis of the BER performance was based on the rate-11/13 (9984, 1536) LDPC codes that have early termination and different improving algorithms, a scaling factor \(\gamma\) = 0.75, row weight \(w_{r}\) = 19, and maximum iteration = 6. Figure 5a shows that the performance between the NPMSA-2 and NPMSA-4 has a 0.38 dB coding gain when the BER = \(10^{-6}\). Figure 5a also shows that the INPMSA-2 significantly improves the BER performance when \(\alpha\) is selected using a smaller value of error probability listed in Table 2. However, the BER performance of INPMSA-4 with \(\alpha\) = 0.5 and \(\beta\) = 0.5 is worse than that of the NPMSA-4. The coding gain of the INPMSA-4 with \(\alpha\) = 0.25 and \(\beta\) = 0.75 can be achieved. Figure 5a indicates that the decoding capability of the NPMSA starts to converge after a SNR = 4.5 dB, and this characteristic ensures the decoding capability of the INPMSA with \(\alpha\) = 0.25 and \(\beta\) = 0.75, SMA-MSA, and the rExMin algorithm, is the same. In BG 2, the BER performance was based on the rate-5/7 (5376, 1536) LDPC codes, which have early termination and different improving algorithms, a scaling factor \(\gamma\) = 0.75, a row weight \(w_{r}\) = 10, and a maximum iteration = 6. Figure 5b shows that the performance between the NPMSA-2 and NPMSA-4 has a 0.4 dB coding gain when the BER = \(10^{-5}\). Figure 5b also shows that the INPMSA-2 significantly improves the BER performance. The BER performance of the INPMSA-4 with \(\alpha\) = 0.5 and \(\beta\) = 0.5 is worse than that of the NPMSA-2. However, the INPMSA-4 with \(\alpha\) = 0.25 and \(\beta\) = 0.75 improves the BER performance when \(\alpha\) is selected using a smaller value of error probability listed in Table 2. The coding gain between the best performance of the INPMSA-2 and INPMSA-4 is 0.25 dB when the BER = \(10^{-6}\). Figure 5b also indicates that the decoding capability of the INPMSA-4 with \(\alpha\) = 0.25 and \(\beta\) = 0.75, SMA-MSA, and the rExMin algorithm starts to converge after the SNR = 4.5 dB. Figure 4: Simulation results of the improved PS-PMSA in the NAND flash channel model.
## 3 HSP-QGA Algorithm Design
### Algorithm Design  
#### 3.2.1 Chromosome Coding  
In the HSP-QGA, the chromosomes of individuals are encoded by qubit. Unlike the binary, gray, and real number methods used in the traditional GA, the qubit is a two-state quantum system. It can be formed using a standard orthogonal basis \(\{\ket{0},\ket{1}\}\)[25] in addition to residing in the superposition state, thus increasing the amount of information carried by the chromosome, shown in Formula (3). In addition, \"0\" represents the spin-down state, \"1\" represents the spin-up state, \"1\" represents a quantum state, \(\alpha\), \(\beta\) represents probability amplitude pair, which exists in the plural, and \(|\alpha|^{2}+|\beta|^{2}\ =\ 1\,.\ |\alpha|^{2}\) and \(|\beta|^{2}\) represent the probabilities of \"0\" and \"1\" states, respectively.  
\[|\varphi\rangle\ =\ \alpha|0\rangle+\beta|1\rangle \tag{3}\]  
In this paper, the existing large and complex quantum coding approach is mapped in a reasonably simplified way [26]. Denote each chromosome as a processor serial number and each individual as a processor scheduling strategy. Each individual is composed of \(n\) chromosomes, and the number of chromosomes equals the total number \(n\) of tasks in the heterogeneous system. Each chromosome can be represented by \(m\) quantum genes, and each quantum gene is represented by \(\begin{pmatrix}\alpha\\ \beta\end{pmatrix}\) where \(m\ =\ \left[\log_{2}^{p}\right]\), \"\(p\)\" means the number of processors in the system, and \"\(\lceil\ \rceil\)\" means rounding up. As shown in (4), \(\mathbf{S}\) represents  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Task** & \(v_{1}\) & \(v_{2}\) & \(v_{3}\) & \(v_{4}\) & \(v_{5}\) & \(v_{6}\) & \(v_{7}\) & \(v_{8}\) & \(v_{9}\) & \(v_{10}\) \\
**Rank** & 94 & 64 & 45 & 67 & 70 & 65 & 46 & 35 & 42 & 15 \\
**Order** & 1 & 5 & 7 & 3 & 2 & 4 & 6 & 9 & 8 & 10 \\ \hline \end{tabular}
\end{table}
Table 1: Task scheduling priority sample table.  
a chromosome code consisting of m quantum genes. As shown in (5), \(\mathbf{I}\) is described as a quantum-coded individual in which each chromosome is represented, as shown in (4).  
\[S\ =\ \begin{pmatrix}\alpha_{11}&\alpha_{12}&\cdots&\alpha_{1m}\\ \beta_{11}&\beta_{12}&\cdots&\beta_{1m}\end{pmatrix} \tag{4}\]  
\[I\ =\ \left(S_{1}\ S_{2}\ S_{3}\cdots\cdots S_{n}\right) \tag{5}\]
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System  
In this paper, a data compression technology-based IDAQ system was developed on the basis of a digital-software-design (D-S/W-D), by means of the CAFB already developed in the previous study. The CAFB is a dynamic data compression technology. It can improve on some shortcomings of wired measurement SHM, such as complicated cabling, costly installment, and constant maintenance. It also works on some restraints of wireless measurement SHM, such as limited performance of sensor nodes, analogue type of filter bank, and slow and short distance communication to make it better. The CAFB-based IDAQ system was composed of a logging & a controller system (L and Cs), a multi-I/O system (MIMO), a two-way wireless communication system (TWCs), a central monitoring and control system (CM and Cs), and embedding of the CAFB. Design and components of a CAFB-based IDAQ system are shown in Figures 6 and 7.  
Figure 6: Design of a CAFB-based IDAQ system.  
Figure 7: Design of a CAFB-based IDAQ system: (**a**) logging and controller system (L and Cs); (**b**) multi-input and multi-output system (MIMO); (**c**) two-way wireless communication system (TWC); (**d**) central monitoring and control system (CM and Cs); and (**e**) embedding of the CAFB.
## 4 Calculation for Axial Compressive Bearing Capacity
### Load Bearing Capacity of Composite Wall  
For the composite shear wall with the steel plate and concrete, based on the above discussion, the buckling effect of the steel plate, and increasing strength of the confined concrete, should be considered in the calculation of the axial compressive bearing capacity. Taking into account that the mid-high section of the composite wall is the weakest section of the force, the equation of the axial compressive bearing capacity was given as follows:  
\[N=\eta f_{y1}A_{y1}+f_{cc}A_{c}+f_{y2}A_{y2} \tag{45}\]  
where \(f_{y1}\) is the yield strength of the steel plate, \(f_{y2}\) is the yield strength of the vertical reinforced bar (for composite wall with built-in steel plate) or internal vertical separator (for composite wall with two skins of steel plate), \(A_{y1}\) is the cross-sectional area of the steel plate, \(A_{y2}\) is the cross-sectional area of the vertical reinforced bar or internal vertical separator, \(f_{cc}\) is the axial compressive strength of the confined concrete with the method of Sections 4.2.1 and 4.2.2, an d \(A_{c}\) is the cross-sectional area of the concrete. \(\eta\) is the influence coefficient of the buckling steel plate, where \(\eta=1\) for the composite wall with the built-in steel plate. The \(\eta\) value can be obtained using Equation (17) for the composite wall with two skins of steel plate.  
Table 4 presents the axial forces of the different parts at the ultimate load, using Equation (45), in which \(N_{p}\) is the axial force of the steel plate, \(N_{c}\) is the axial force of the concrete, \(N_{s}\) is the axial force of the vertical reinforced bar or internal vertical separator, \(N_{cal}\) is the sum of axial forces of the different parts, and \(N_{test}\) is the test axial compressive bearing capacity. In Table 4, the measured strength values are considered in the calculation of the axial force, including \(N_{p}\), \(N_{c}\), and \(N_{s}\). For specimen HGW-2, the axial force of the steel plate included the stiffening ribs. For specimen HGW-3, the buckling effect of the steel plate on the axial compression was considered along the long sides, but not the short sides. The calculated values \(N_{cal}\) using Equation (45) are in good agreement with the test values \(N_{test}\), with errors no higher than 5%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen No.** & \(N_{p}\)**/**k**N** & \(N_{c}\)**/**k**N** & \(N_{s}\)**/**k**N** & \(N_{cal}\)**/**k**N** & \(N_{test}\)**/**k**N** & \(N_{cal}\)**/**\(N_{test}\)** \\ \hline HGW-1 & 6394 & 12,349 & 1394 & 20,092 & 20,427 & 0.984 \\ HGW-2 & 7296 & 15,037 & 1394 & 23,727 & 23,203 & 1.023 \\ HGW-3 & 6899 & 17,340 & 1275 & 25,514 & 26,139 & 0.976 \\ \hline \end{tabular}
\end{table}
Table 4: Axial forces of different parts at ultimate load.  
Figure 15: Comparison between the numerical results and experimental results. (**a**) HGW-1; (**b**) HGW-2; (**c**) HGW-3.
## 2 Method of Calculation
### Stress Intensity Factors
#### 2.2.2 Approximate Solution  
The stress intensity factor of a closed crack can be accurately calculated using Equation (24). However, applying this equation in engineering practice is challenging due to the difficulty of integrating Equation (24) and the frequent occurrence of non-integrable conditions. To make the stress intensity factor calculation formula more widely applicable, this paper proposes configuring the number of discrete points on the crack surface with an interval length based on Equation (24). In order to have better control over the number and placement of individual points, the distribution of these points follows the Chebyshev polynomials.  
\[\left\{\begin{array}{l}x_{i}=a\cos\frac{(2i-1)\pi}{2M}\\ \\ \omega^{i}=\frac{\pi a}{M}\sin\frac{(2i-1)\pi}{2M}\end{array}\right. \tag{29}\]  
Combining Equations (24) and (29) leads to the following equation:  
\[K_{\rm II}(\pm)=\frac{1}{\sqrt{\pi a}}\sum_{i=1}^{M}\frac{\tau_{ef}(a\cos\frac {(2i-1)\pi}{2M})(1\pm\cos\frac{(2i-1)\pi}{2M})}{\sin\frac{(2i-1)\pi}{2M}}\omega ^{i} \tag{30}\]  
Since Equation (30) is calculated using the local coordinates of the cracked surface, the corresponding effective tangential stresses must also be converted into those coordinates. A schematic representation of the calculation for scattered effective tangential stress is shown in Figure 3.  
From Figure 3, it can be seen that the starting point satisfies the equation in global coordinates.  
\[\left\{\begin{array}{l}x_{0}=b\cos\theta\\ y_{0}=b\sin\theta\end{array}\right. \tag{31}\]  
Using the starting point, the position of the discrete point can be calculated in global coordinates.  
\[\left\{\begin{array}{l}x^{j}=x_{0}+(a+x_{i})\cos\beta\\ y^{j}=y_{0}+(a+x_{i})\sin\beta\end{array}\right. \tag{32}\]  
The effective tangential stress corresponding to the local coordinate system can be derived by combining Equation (28)  
\[\tau_{ef}^{j}=(\sigma_{x}^{j}-\sigma_{y}^{j})\sin\beta\cos\beta-\tau_{xy}^{j} (\cos^{2}\beta-\sin^{2}\beta)-u\Big{[}\sigma_{x}^{j}\sin^{2}\beta+\sigma_{y}^ {j}\cos^{2}\beta-2\tau_{xy}^{j}\cos\beta\sin\beta\Big{]} \tag{33}\]  
The solution of the stress intensity factor from Equations (30) and (33) is referred to as the approximate solution.