esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Step II: Data Collection
#### 3.2.3 Calculating Fuzzy Probabilities  
Defuzzification plays a crucial role in converting FPs into FPr, facilitating an effective decision-making process for risk assessment in power grid systems [48]. For this study, we employ the widely used Center of Area (CoA) technique, which extracts a crisp value from fuzzy sets by calculating the center of the area under the membership function curve. The formula for the defuzzified output X is  
\[\text{X}=\frac{\int\text{u}(\text{x})\text{dx}}{\int\text{u}(\text{x})} \tag{5}\]  
where  
* X is the defuzzification output of i;
* x is the output variable;
* i(x) is the aggregated membership function representing the degree of membership for each value of x.  
In our model, converting FPs to FPr is essential for assessing risks such as equipment failure, human error, and environmental factors. We utilize trapezoidal and triangular membership functions to represent fuzzy sets, as these functions effectively capture the imprecision inherent in expert judgments and uncertain data [27]. The trapezoidal function is beneficial for risk factors with well-defined thresholds, while the triangular function is more suited for risks that exhibit gradual transitions. Trapezoidal and triangular membership functions can be calculated using Equation (6).  
\[\text{\mu A}(\text{x})=\left\{\begin{array}{ll}0,&\text{x}<\text{a1}\\ \frac{\text{x}-\text{a1}}{\text{a2}-\text{a1}},&\text{a1}\leq\text{x}\leq \text{a2}\\ 1,&\text{a2}\leq\text{x}\leq\text{a3}\\ \frac{\text{a4}-\text{x}}{\text{a4}-\text{a3}},&\text{a3}\leq\text{x}\leq \text{a4}\\ 0,&\text{x}>\text{a4}\end{array}\right. \tag{6}\]  
where  
* \(\mu\)A(x) is the membership function for fuzzy set A;
* x is the output variable;
* a1, a2, a3, and a4 are the parameters defining the trapezoidal membership function.  
Figure 6 illustrates the shapes of these membership functions. These functions map expert inputs, represented as linguistic variables, to a numerical scale, integrating subjective expert opinions with FPr. This provides a robust framework for risk assessment.  
In this study, FPs are defuzzified using the CoA method to obtain FPr, ensuring that fuzzy outputs from expert elicitation and fuzzy membership functions are translated into actionable, precise values. Additionally, FPr are not static and can be dynamically adjusted, allowing for flexibility as new risks emerge in power grid systems.  
To enhance the precision and adaptability of the model, hyperparameters such as the fuzzy membership function ranges (e.g., VL range) can be manually adjusted or optimized using methods like Bayesian optimization. This optimization enables the continuous refinement of the fuzzy sets, ensuring that the model accurately captures relevant risk factors and expert insights, resulting in more precise predictions of power grid failures.
## 4 Discussion
### Devices on the Horizon  
Within the scope of this review, it is important to acknowledge additional technologies that hold promise in this field but were not included in our study due to specific inclusion criteria. Notably, no studies within our review used IMUs to monitor training progression or any incorporated body-machine interfaces, which have the potential to be relevant in this context. Furthermore, except for studies that employed Kinect, none integrated human pose estimation algorithms in conjunction with camera-based systems. Given the continuous evolution of this field, it is evident that such systems will likely be integrated into the clinical setting. Similarly, it is worth presenting new robotic devices that are beginning to show evidence in clinical research studies. Among these, AllCore360 promotes the continuous engagement of the trunk muscles while performing rotational plank exercise [135]. [PERSON] et al. [136] used the ProKin system (Technobody) with a trunk module. The ProKin system is composed of a platform with sensors that can function in either a static or dynamic configuration in either the active or passive mode. In addition, there is an extra separated module that includes a non-motorized seat, which can provide either stable or unstable support, along with a sensor that assesses trunk oscillations. The device is also equipped with a screen that allows players to play games and provides feedback on joint movements or trunk oscillations. Differently, the Trunk Support Trainer (TruST) is a robotic device that includes a belt fastened to the trunk, which is powered and manipulated by four cables linked to it. The device operates by applying active or assistive forces tailored to the individual's capacity to control their trunk movements. To date, only one pilot study has tested whether it is possible to improve seated postural control with TruST in a participant with SCI [137]. Finally, it is worth mentioning exoskeletons; despite it not being their primary goal, they have an impact on trunk muscles and trunk mobility [15; 138].
## 6 Conclusions  
In this paper, a two-source ATCC-SM over the slow Rayleigh fading MIMO channel is proposed. We analyzed the BER performance of the ATCC-SM scheme through different conditions such as various numbers of receive antennas, modulation techniques, and information lengths. In order to perform a fair comparison, an analysis for the BER performance of ATC-SM in the non-cooperative scenario was also implemented. Through the Monte Carlo simulated results, it can be seen that the cooperative scheme has a significant performance gain as compared to its non-cooperative counterpart under identical conditions. This is due to the fact that the joint ATC decoder results in a BER performance gain. Finally, we carried out a BER performance comparison between the two-source ATCC-SM and two-source ATCC-VBLAST under identical conditions, and the Monte Carlo simulations evidently demonstrate the effectiveness and usefulness of the two-source ATCC-SM scheme.  
[PERSON] conceived the idea. She developed the mathematical models and performed the Monte Carlo simulations. [PERSON] checked the mathematical model and the simulated results. [PERSON] and [PERSON] revised the manuscript. All authors have read and approved the final version of manuscript before its first submission to a journal.  
This research was funded by the National Natural Science Foundation of China, under Grant number 61771241.  
The authors would like to thank the Editor and anonymous reviewers for their precious suggestions, comments and reviews that help in improving the quality of this manuscript.  
There is no conflict of interest related to the content of this manuscript.
## 1 Introduction
This research aims at addressing this challenge as well as enhancing the overall under-reporting detection performance, through the design and development of a supervised machine learning model that predicts Zakat under-reporting and the revenue at risk. Specifically, a deep neural network is designed to classify Zakat declarations into \"under-reporting\" or \"actual declaration\" classes, and predict the expected tax gap. Moreover, the proposed model would support administration efforts to pinpoint the declarartions and/or taxpayers that were assigned to the class \"under-reporting\" and that correspond to a higher revenue at risk. Specifically, the proposed model generates a confidence value that encloses the likelihood of belonging to the class \"under-reporting\" as well as the cooresponding revenue at risk. Thus, a high confidence value is associated with both under-reporting risk and high revenue at risk. Accordingly, the main contributions of this research can be summarized as: (i) designing a deep neural network performing simultaneous classification and regression, (ii) enabling Zakat declarations to categorize taxpayers into compliant or non-compliant, and (iii) determining the shortlist of Zakat payers that should be audited first in order to maximize the expected Zakat income. Further, the model hyper-parameters are investigated to determine the optimal settings. Namely, activation, the batch size, epochs, and the number of layers are investigated during the fine tuning phase. The rest of this manuscript is organized as follows: Section 2 surveys the related works relevant to tax fraud prediction using machine learning techniques. In Section 3, the proposed solution is depicted, while the experiments settings, findings and discussion are outlined in Section 4. Finally, the research conclusions and future work are presented in Section 5.
## 3 Data and Methods
### Conditioning Factors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conditioning Factors** & **Very Low** & **Low** & **Moderate** & **High** & **Very High** \\ \hline Elevation (m) & 1009.10–2416.90 & 2416.90–3120.80 & 3120.80–3578.33 & 3578.33–3983.07 & 3983.08–5496.46 \\ \hline Slope (\({}^{\circ}\)) & 0–14 & 14–24 & 24–32 & 32–43 & 43–86 \\ \hline Topographic Wetness Index & 0–4 & 4–6 & 6–9 & 9–14 & 14–33 \\ \hline Stream Power Index & 0–4.0 \(\times\) 10\({}^{3}\) & 4.0 \(\times\) 10\({}^{-1}\)-1.0 \(\times\) 10\({}^{4}\) & 1.0 \(\times\) 10\({}^{4}\)–5.0 \(\times\) 10\({}^{4}\) & 5.0 \(\times\) 10\({}^{4}\)–1.0 \(\times\) 10\({}^{4}\) & 1.0 \(\times\) 10\({}^{4}\)–8.6 \(\times\) 10\({}^{7}\) \\ \hline Drainage Basin Shape Coefficient & 0–0.25 & 0.25–0.5 & 0.5–0.75 & 0.75–1 & 1–478 \\ \hline Drainage Basin Area (km\({}^{2}\)) & 0–2.47 & 2.47–5.61 & 5.61–9.59 & 9.59–16.09 & 16.09–45.96 \\ \hline Maximum Flow Length (km) & 0–2.50 & 2.50–4.26 & 4.26–6.05 & 6.05–8.85 & 8.85–17.53 \\ \hline Maximum Flow Gradient & 0–0.127 & 0.127–0.204 & 0.204–0.293 & 0.29–0.401 & 0.401–0.732 \\ \hline Seismic Nucleation Density & 4.4 \(\times\) 10\({}^{-5}\)–4.8 \(\times\) 10\({}^{-5}\) & 4.9 \(\times\) 10\({}^{-5}\)–5.1 \(\times\) 10\({}^{-5}\) & 5.2 \(\times\) 10\({}^{-5}\)–5.4 \(\times\) 10\({}^{-5}\) & 5.4 \(\times\) 10\({}^{-5}\)–5.6 \(\times\) 10\({}^{-5}\) & 5. Figure 6: Conditioning factor maps used for DFS assessment. (**a**) Elevation; (**b**) slope; (**c**) aspect; (**d**) topographic wetness index; (**e**) stream power index; (**f**) drainage basin shape coefficient; (**g**) drainage basin area; (**h**) maximum flow length; (**i**) MFG—maximum flow gradient; (**j**) seismic nucleation density; (**k**) fault distance; (**l**) normalized difference vegetation index; (**m**) soil erosion; (**n**) distance to road; (**o**) annual rainfall; (**p**) population statistics; (**q**) land use.
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 2 Path Planning for Nonholonomic Robots
### Path Planning Based on Improved Hybrid A* Algorithm  
#### 2.3.1 Grid Environment Modeling  
The planning of paths was determined according to the positions of vacancies and obstacles on the constructed maps. As introduced in Section 2.1, grid mapping approach was used for the construction of the map, and the map was meshed into grids with certain numbers presenting starting point, end point, obstacles, security areas, and other relevant information. As shown in Figure 3, the black regions in the grid map represent obstacle areas, while the white regions represent areas which are available for travel, and both areas can be updated in real time according to the positions of obstacles and vacancies when the environment is changed.
## 2 Materials and Methods
### Experimental Set-Up  
Two KS attached to the hand and body were used in this study (Figure 1). Microcontrollers with WiFi communication modules integrated into the sensors enabled the reading of the KS data and their transfer to a separate LabVIEW for Loops application running on a laptop. The samples from both kinematic devices were read by the main program loop. The procedure was controlled and timed in cycles of 5 ms. Communication was established via User Datagram Protocol on a high loaded ISM band. Since packet diversity could result in data loss, error corrections were arranged so that the possible lost samples were replaced with previous values. The validity of the results was confirmed through channel quality monitoring.  
Two cameras (Figure 1) were used to record the testing. The cameras were positioned 2 metres from the participant and placed laterally (left and right) relative to the athlete on a tripod that stood 1.3 metres from the floor. This provided enough space for the task to be performed within the field of view and for all stages of execution to be captured. The LabVIEW application received signals from multiple sensors timed with the video camera signal and recorded everything into files for further processing. This kind of approach has advantages over sensor-only application, such as combining images from two cameras at different viewpoints and recognising human action in a more comprehensive manner, allowing for the identification and distinction of the different phases of a movement.  
The main advantage of the applied procedure is the combination of multiple sources and the heterogeneity of information such as images and inertial data. Four temporally synchronised data modalities were used in order to provide more accurate information. The applied method reduced uncertainty regarding the obtained data, as well as providing detailed analysis of technique. Given that the frame per second (FPR) of the cameras and the sampling rate of the sensors differed, the synchronisation of the beginning and ending of a movement was achieved using time stamps. This kind of synchronisation allowed an accurate assessment of the fixed time delay between the camera and the sensor devices. For each technique modality and each trial, the data were stored in two video (.avi and.MP4) files. The inertial sensor data were stored using the LabVIEW application as.tdms files.
## 2 Materials  
### Studied Classrooms  
Four classrooms in a school for approximately 1000 students located in a quiet residential neighborhood in Lisbon, Portugal were selected for this study. Figure 1 shows the plan view of the school's third floor on the right-hand side, while the left-hand side shows the detailed plan view and elevation of the main school building and east wing. The location of the three monitored classrooms is highlighted. An additional classroom at the second floor of the east wing was also monitored.  
The school used in this study was built in the 1940s and retrofitted to contemporary standards in the early 2000s. A comprehensive characterization of Portuguese school building architecture and the standards used in retrofitting Portuguese school buildings is presented in [4; 5].  
Some relevant characteristics of the monitored classrooms are presented in Table 1.  
\begin{table}
\begin{tabular}{p{227.6 pt}} \hline \hline
**Geometry:** Approximately 54 m\({}^{2}\) floor area and 3 m headroom. \\ \hline
**Usage:** 5 th to 12 th grade classes with approximately 20 students per class and a 1:1 girls–boys ratio; casual dress code; classes (expository learning) taught from mid-September to mid-June, Monday to Friday, from 8.10 a.m. to 6.30 p.m. (excluding Bank, Christmas, February half-term (Carnival), and Easter holidays). Note: Term dates exclude the hot summer period from mid-June to mid-September. \\ \hline
**Windows:** Three manually operated windows per classroom; wood framing and double glazing; _very low air permeability_ (class 3 or higher according to [64]); each window (1.4 \(\times\) 1.9 m\({}^{2}\)) divided into lower and upper pivoting parts. \\ \hline
**Shading:** Fixed vertical and horizontal shading along the perimeter of the window; horizontal shading between upper and lower window parts; movable roller blind (fabric) placed on the outside allowing _independent manual control of shading and window opening/closing_. \\ \hline
**Lighting and equipment:** Recessed luminaires using fluorescent tubular lamps (10 W/m\({}^{2}\)); one video projector and one desktop computer. \\ \hline
**Other:** Massive (65 cm thick) stone masonry exterior walls; reinforced concrete slab floor with a cementitious covering; dropped ceiling made of perforated (sound absorbing) gypsum boards. _Classrooms are neither heated nor cooled_. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the monitored classrooms.  
Figure 1: The studied school, with three of the monitored classrooms highlighted.  
Figure 2 presents a photograph of a classroom taken on a warm day. The photograph shows the lower and upper window pivoting parts in the open position and the movable roller blinds (placed on the outside) covering different windows both partially and totally.  
Because using the blinds influences the windows' air permeability, the air change rates used in this study result from the combined effects of opening/closing both the windows and the blinds.
## 3 Materials and Methods
### Electromagnetic Interference (EMI) Shielding Properties  
The EMI shielding effectiveness properties were evaluated using a P9372A Keysight Streamline Vector Network Analyzer (Keysight, Santa Rosa, CA, USA) and two sets of microwave standard 15 dB gain waveguides (WR 187 and WR 147, respectively, obtained from Advanced Technical Materials Inc. (ATM, Patchogue, NY, USA) covering a broad frequency band in the range of 3.2-7.0 GHz (C-band), which is used for long-distance radio telecommunications, such as satellite communications transmissions, Wi-Fi devices, cordless telephones, weather radar systems, etc. In particular, every sample was placed in the middle of each set of waveguides, and its scattering parameters (S-parameters; S\({}_{11}\), S\({}_{12}\), S\({}_{22}\), S\({}_{21}\)) were recorded.
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Approximation SWIPT Relay to AF Relay
#### 4.5.3 Approximated Outage Probability  
The outage probability \(P_{\rm out}\) is defined as the probability that the channel mutual information \(I\) falls below the required transmission rate \(T_{R}\). For the considered relayingsystem, the mutual information is expressed as \(I=\frac{1}{R+1}\log_{2}(1+\gamma_{\text{tot}})\), where \(\gamma_{\text{tot}}\) can be either \(\gamma_{\text{id}}\) or \(\gamma_{\text{cb}}\). The scaling factor \(1/(R+1)\) accounts for the use of \((R+1)\) orthogonal time slots or channels required to transmit data from the source to the destination through \(R\) relays. Accordingly, the outage probability can be approximated as:  
\[\begin{split} P_{\text{out,id}}&=\text{Pr}\bigg{(} \frac{1}{R+1}\log_{2}(1+\gamma_{\text{id}})\leq T_{R}\bigg{)}\\ &=\text{Pr}\Big{(}\gamma_{\text{id}}\leq 2^{(R+1)T_{R}}-1\Big{)} \\ &\simeq\text{Pr}\Big{(}\gamma_{\text{id}}^{\text{AF}}\leq 2^{(R+1)T_{R} }-1\Big{)}=P_{\text{out,id}}^{\text{AF-App}}.\end{split} \tag{43}\]  
Based on the CDF expressions \(F_{\gamma_{\text{id}}^{\text{AF}}}(\gamma)\) and \(F_{\gamma_{\text{cb}}^{\text{AF}}}(\gamma)\) given in (40), the approximated outage probabilities are given by  
\[\begin{split} P_{\text{out,id}}^{\text{AF-App}}&=F _{\gamma_{\text{id}}^{\text{AF}}}\Big{(}2^{(R+1)T_{R}}-1\Big{)}\\ P_{\text{out,cb}}^{\text{AF-App}}&=F_{\gamma_{\text {cb}}^{\text{AF}}}\Big{(}2^{(R+1)T_{R}}-1\Big{)}.\end{split} \tag{44}\]  
In addition, the outage probability of the direct SD link (i.e., without relaying) is expressed as  
\[P_{\text{out,sd}}=F_{\gamma_{0}}\Big{(}2^{T_{R}}-1\Big{)} \tag{45}\]  
where the CDF of \(\gamma_{0}\) is given by \(F_{\gamma_{0}}(x)=\Big{(}1-\exp\Bigl{(}-\frac{x}{\gamma_{0}}\Bigr{)}\Big{)}u(x)\).
# Mdpi
###### Abstract  
Advanced metering infrastructure (AMI) is an important application of smart grid communication technology used for the remote monitoring and control of smart meters. Broadband powerline communication (BB-PLC) systems could perhaps be used for AMI; however, impulsive noise (IN) greatly degrades performance. In addition to the fast Fourier transform (FFT)-based orthogonal frequency-division multiplexing (OFDM), IEEE 1901 specifications have defined the other physical layer called wavelet-based OFDM. Even though many existing studies have reported the IN mitigation algorithms for the FFT-based OFDM system, these approaches may not directly apply to the wavelet-OFDM-based PLC systems. In this paper, we propose a robust receiver for PLC systems based on wavelet-OFDM. The proposed receiver comprises a pre-IN mitigation block, an adaptive inverse discrete wavelet transform, and an iterative IN reconstruction block. The iterative cancellation of strong IN samples leads to a gradual improvement in the quality of the received signal. Instead of using the frequency domain approach, we reduce the inter-dependency of the channel estimation and per-subchannel equalization by using the time domain signal processing. Besides, we apply variable step-size adaptive algorithms to reduce the impact of IN during the training processes for the channel estimator and per-subchannel equalizer. In accordance with IEEE 1901 specifications, we built a simulation environment to evaluate the effectiveness of the proposed method. Simulation results demonstrated that conventional blanking devices fall short in terms of IN mitigation, and that the proposed scheme is able to achieve performance values approaching those obtained in cases without IN.  
advised metering infrastructure (AMI); wavelet-OFDM; impulsive noise (IN); broadband powerline communication (BB-PLC) +
Footnote †: journal: _Energies_  
_Article_  
Mitigating Impulsive Noise for Wavelet-OFDM  
Powerline Communication
## 3 Results and Discussion
### Shelf Life Analysis of Produced Cookies
#### 3.4.2 Texture Analysis  
Texture analysis on cookies was conducted to monitor the hardness change during storage time at different storage temperatures (25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C) (Figure 4a-c). Data analysis on the acquired data points showed that the hardness vs. storage time for all cookies at all monitored temperatures follow an exponential curve of three parameters with good R\({}^{2}\) (0.89-0.99) (Equation (6); Figure 4a-c). The addition of SCG resulted in harder cookies, as also reported in the literature [48]. It was observed that the hardness increased with storage time. This increase was more pronounced for 7% SGC-added cookies, followed by 4% SGC-added and control cookies. The same was observed in previous studies [49; 50; 51]. [PERSON] et al. (2018) reported that the increase in hardness corresponded to a decrease in the moisture content of the product during storage at different temperatures [51]. The availability of water in biscuit products affects gel formation due to the starch retrogradation process, which causes hardness in such products. The hardening due to moisture loss is one of the main factors that determines the shelf life of baked products, including cookies [52]. The fitted parameters of Equation (6) are presented in Table 5. The parameter \(H_{\mathit{inf}}\) represents the final value of hardness at t \(\rightarrow\) infinity. A Duncan test was performed to examine the effect of SCG% on the \(H_{\mathit{inf}}\) parameter for each temperature separately. Mean value \(\pm\) standard error. Model parameters for the hardness change are calculated by fitting the data in Equation (6). Different superscript letters (a, b, and c) in the same column for each sample (control, 4% SCG-added, and 7% SCC-added) indicate significant differences between equation parameter means for the different storage temperatures, and different superscript capital (A, B, and C) letters indicate significant differences between equation parameter means for the samples at each storage temperature, calculated by [PERSON]'s multiple range test for a significance level of \(p\) = 0.05. Figure 4: Hardness (\(H\)) of cookies vs. storage time for (a) the control cookie, (b) the 4% SCG cookie, and (c) the 7% SCG cookie at 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C. The number of replications = 3. The lines represent the fitting of the mathematical model (Equation (6)) to the experimental data. \begin{table}