esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 5 Testing and Validation  
This paper employs LDPC decoding based on the IEEE 802.16e standard, using BPSK modulation and transmission through an AWGN channel, with a maximum of 30 iterations. Simulations are performed for LDPC codes with a code rate of 1/2 and lengths of 576, 672, and 1440, as well as a code rate of two-thirds for a length of 1248. Frame error statistics are collected, and the simulation terminates when the number of erroneous frames reaches a preset value, at which point the bit error rate is calculated.  
With a target of 1000 erroneous frames, simulations were conducted on the traditional NMS algorithm with correction factor \(\alpha\) ranging from 0.7 to 0.85, accurate to two decimal places, for four different code lengths and rates of LDPC codes. Specifically, simulations for LDPC codes of lengths 576 and 672 were performed at a signal-to-noise ratio (SNR) of 1.8, for a code length of 1248 at an SNR of 1.6, and for a code length of 1440 at an SNR of 1.5. The simulation results, as depicted in Figure 10, confirmed that the optimal \(\alpha\) value is 0.78 for a code length of 576 at a 1/2 code rate, 0.8 for code lengths of 672 and 1440 at a 1/2 code rate, and 0.76 for a code length of 1248 at a two-thirds code rate.  
For LDPC codes with lengths of 576, 672, 1248, and 1440, comparative simulations were conducted between the traditional NMS algorithm with optimal \(\alpha\) values and the TNMS algorithm with \(\beta=1/\alpha\), under various code lengths, rates, and optimal \(\alpha\) values.  
Figure 10: Optimal \(\alpha\) value measurement for four different LDPC codes.  
As illustrated in Figure 11, it was observed that under all these differing conditions, the decoding performance of both algorithms is fundamentally similar.  
The LDPC decoder design was completed using Verilog HDL on the Vivado software platform, implementing an improved algorithm based on the IEEE 802.16e 1/2-rate base matrix with a code length of 672. The simulation results, shown in Figure 12, indicate successful decoding after 10 iterations. Comparing the decoded codeword sequence with the pre-encoded sequence shows no errors in the decoded output.  
The TNMS decoder and the original NMS decoder were compiled using the Vivado software platform, and the comparison of their logic resource utilization is shown in Figure 13. The comparison of these resource utilization reports demonstrates that the improved decoder has reduced logic resource consumption relative to the original.  
Figure 11: Performance comparison of NMS and TNMS algorithms across four different code lengths and rates.  
Figure 12: FPGA decoder simulation diagram for the improved algorithm.
## 4 Qualitative and Quantitative Analyses
### Quantitative Analysis  
The homogeneity principle serves as a fundamental requirement for slope units to function as effective evaluation units. Based on this principle, we selected three quantitative indicators to assess the homogeneity of slope units between the SUD-CGHP and the HPAM: (1) flow direction grid variety, (2) the standard deviation (STD) of slope aspect grid values, and (3) the STD of slope gradient grid values.
## 3 Application of the Proposed MSPSO Algorithm to the IPTBK Problem
### Solving the IPTBK Problem Using the MSPSO Algorithm
#### 3.2.3 Model Validation Phase  
Once the position of the particle in the space search process has been sufficiently updated using Equations (9) and (10), the particle \(p\) has traversed the most probable attack paths back to attack sources (the nest). All the network nodes visited by each particle swarm up to time \(t\) are memorised in a tabu list to prevent the particle swarm from revisiting the same node and creating a circular path during the search process. The aforementioned process is repeated iteratively for a preset number of cycles. Finally, the particle swarms  
Figure 9: Dynamic regrouping for four particle subswarms.  
know the appropriate route that leads them back to the nest (i.e., they have discovered the origin of the attack).  
The model accuracy was evaluated using the coverage percentage (%), which is the ratio of the average number of packets on an attack path to the total number of routing. The coverage percentage is expressed as follows:  
\[\text{Coverage percentage or precision (\%) = (Average number of packets/attack path)/(total number of routing packets),} \tag{13}\]  
where the average number of packets on the attack path is computed as the total number of packets on the route divided by the routing distance (in terms of hop count). If the converged solution is not the true attack node, then the average number of packets on the route is reset to 0 and the search for the true route is resumed. The detailed algorithm for route construction with MSPSO-IPTBK model is presented in Figure 10.  
Input: Parameters of the MSPSO-IPTBK model, including the initial values of max_gen, c\({}_{1}\), c\({}_{2}\), \(x\) and \(v\) for the particles set, and the test data generated using the Waxman theory [24] Output: Predicted accuracy of model
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
###### Abstract  
Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.  
wireless sensor networks; multi-target tracking; sensor scheduling; target priority; task allocation; tracking accuracy; energy efficiency +
Footnote †: journal:
## 4 Secure and Precise DM Scheme Based on A Multi-Carrier Multiple Antenna Arrays Model
### The Architecture of the LU Based on a Multi-Carrier Multiple Antenna Arrays Model
Specifically, when the [PERSON] is meant to eavesdrop the \(l\)th symbol, the received signal at time \(t\) in Equation (28) can be further reorganized as  
\[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]2 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]3 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]4 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]5From Equation (29), it is observed that the \(l\)th symbol is interfered by the equivalent mixed noise and the AWGN. The [PERSON] wants to intercept it requiring a higher signal-to-noise ratio (SNR). What is more, if the [PERSON] is intended to eavesdrop the other \(K-1\) symbols, e.g., the \(m\)th symbol (\(m\in\{1,\cdots,l-1,l+1,\cdots,K\}\)), the received signal in Equation (28) can be further converted into  
\[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]6 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]7 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]8 \[y_{k}^{{}^{\prime}}(t)=\mathbf{h}^{H}(\theta_{dk})\mathbf{x}_{k}(t)\cdot e^{ -2\pi f_{ck}t}+n_{k}^{{}^{\prime}}(t), \tag{25}\]9  
where \(\varepsilon_{k}=\left[\mathbf{u}_{k}(t)e^{j2\pi(f_{dk}-f_{\text{ on}})t}\right]\mathbf{h}^{H}(\theta_{e})\). It can be observed from Equation (30) that each of the other \(K-1\) received symbols includes five parts. The first is the distorted signal, the second is the injected AN, the third is equivalent noise caused by the \(l\)th rotated symbol, the fourth is the equivalent mixed noise introduced by the other \(K-2\) symbols, and the last is the AWGN. Therefore, even if the Eve is aligned with or very close to one of the transmit directions, it still cannot recover the confidential information. Obviously, the transmission security is enhanced significantly owing to the use of multiple antenna arrays, which outperforms the traditional single antenna array based DM schemes.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 3 Proposed Method of Fast Empirical Wavelet Transform
### Trend Estimation Method Based on the Key Function
Figure 4: (**a**) The trend components and boundaries of different reconstruction points after inverse fast Fourier transform (iFFT): \(\text{B1}=4\), \(\text{B2}=10,\text{B3}=30,\text{B4}=60,\text{B5}=100,\text{B6}=200,\text{B7}=300\); (**b**) trend components and boundaries of different reconstruction points after iFFT: \(\text{B1}=20,\text{B2}=25\), \(\text{B3}=30,\text{B4}=35,\text{B5}=40\). For signals with modulated components and bandwidth, it is necessary to estimate the trend to extract signals. The number of reconstructed points should be \(\text{B}\in[10,60]\). In Figure 1, for two signals whose spectra are close to each other, it is necessary to divide them accurately in order to extract the signals. Rolling bearings are one of the most easily damaged parts in rotating machinery. When a rolling bearing fails, the rolling body will impact periodically when it touches the fault position [31; 32]. Shock information exists with modulating components but does not exist alone. The impact and modulation components show a cluster of modulation information containing a side band in the spectrum. How to extract the modulation information of each cluster becomes the most critical step in the process of splitting the spectrum. The vibration acceleration sensor is sensitive to impact information. Trend components represent the trend of spectrum change, and can better fit the similar modulation information, which may contain shock information in each cluster of the spectrum. Therefore, the trend estimation method based on the key function has advantages in dealing with rolling bearing fault diagnosis. The method based on FFT and iFFT is fast in calculating trend components. It has obvious advantages over the traditional EWT in dealing with analog signals (see Section 4) and practical signals (see Section 5). Choosing too few points from the key function for the inverse transformation will lead to rough partition that cannot distinguish the information with close frequency, but it can well contain the modulation information with side-band frequency. The proposed method can preserve and exclude the useless components to a certain extent. For simulation signals, when many points are selected for inverse transformation, some details can be extracted, such as the components whose spectra are close to each other, but this will cause the spectrum to be divided into too many areas. This paper proposes a threshold noise-reduction method to optimize the above problems.
## 4 Analysis of the Field Measurement Data  
### Automatic Monitoring Arrangement and Monitoring Content  
The TM50 total station produced by Leica (Heerbrugg, Switzerland) was utilized to monitor the deformation of the existing Line 1 tunnel automatically and manually check it regularly in this project. The monitoring focused on the excavation of the foundation pit and the new Line 7 shield crossing the existing line. Two stations were arranged in the upper and lower sections of the track operation area of Line 1, forming two automatic monitoring networks. The monitoring length of each section was approximately 200 m. A monitoring section was arranged every five rings in Section Line 1 for the foundation pit construction stage of the station, which corresponded to the projection range of the foundation pit, and a monitoring section was arranged every ten rings outside the projection range. In the construction phase of the shield tunneling machine of Line 7 across Line 1, a monitoring section will be arranged every 2 to 3 ring intervals in the projection range and 15 m outside the projection range of the shield tunnel; a monitoring section will be arranged every five ring intervals in the 15 m-55 m outside the projection range. The monitoring range of the up-line and down-line was set to 31 sections. Among them, 15 sections were used in the foundation pit construction stage, in which 16 sections were encrypted in the middle when the shield was crossed. The arrangement of monitoring points is depicted in Figure 8. Two sets of monitoring prisms were positioned on either side of each monitoring section to track both the vertical and horizontal displacement of the track ballast. Refer to Figure 9 for the detailed layout of the monitoring prisms.  
Figure 8: Sectional view of the existing tunnel monitoring points.
## 3 Results
### Nanemulsion Stability During Storage
#### 3.3.1 Characterization of Nanoemulsions Enriched with Lycopene and Phenolic Extracts  
Oil-in-water nanoemulsions were prepared using the previously studied edible oils (OPO, CLA, SFO, CO, and FO) enriched with lycopene, ascorbic acid, and phenolic extracts. These emulsions were subsequently characterized in terms of droplet size and distribution, viscosity, turbidity, and whiteness, in an attempt to correlate the bioaccessibility of lycopene and phenolic compounds with the physicochemical properties of nanoemulsions, as well as to select the adequate lipid phase for the preparation of the nanoemulsion-apple juice functional beverage. The initial concentration of lycopene was calculated as 10.65 \(\pm\) 0.2 mg lycopene/mL for OPO, 10.70 \(\pm\) 0.30 mg lycopene/mL for CLA, 13.4 \(\pm\) 0.8 mg lycopene/mL for CO, 12.0 \(\pm\) 0.5 mg lycopene/mL for SO, and 11.1 \(\pm\) 0.3 mg lycopene/mL for FO, and the initial TPC was 7.85 \(\pm\) 0.85 mg GAE/mL for OPO, 7.31 \(\pm\) 0.15 mg GAE/mL for CLA, 7.58 \(\pm\) 0.24 mg GAE/mL for CO, 7.77 \(\pm\) 0.35 mg GAE/mL for SO, and 7.79 \(\pm\) 0.33 mg GAE/mL for FO. In contrast to the previously examined nanoemulsions, the droplet size of those enriched with lycopene and phenolic extracts was significantly influenced by the type of oil used (\(p<0.05\)), with corn oil and fish oil resulting in the largest droplet size, as can be seen in Table 5. Also, the incorporation of phenolics in the nanoemulsion significantly increased the droplet diameter, to ca. 297.02 nm compared to 269.56 nm for the control and 279.1 nm for the nanoemulsion with ascorbic acid. Furthermore, the presence of ascorbic acid and phenolic compounds broadened the droplet size distribution, with droplets smaller than 100 nm and larger than 1000 nm appearing in the DLS graph (Figure 4). The addition of ascorbic acid and phenolics significantly influenced the whiteness of the samples (\(p<0.05\)): the whiteness of blank samples was approximately 20, while with the addition of AA whiteness increased to 40.7 and further increased to 41.8 with the addition of phenolics. However, no significant differences were observed among the samples regarding the viscosity and turbidity values (Table 5). Turbidity values were relatively low, in agreement with
## 4 Discussion
### Influence of Rock Structure on Electrical Properties  
Characterization of rock properties is partly related to permeability and electrical conductivity, which are related to geometry, pore connectivity and microstructure [6]. Indeed, the rock's ability to let the fluid pass through is directly linked to rock structure. As seen before, electrical factors (\(F_{lab}\), \(m_{lab}\) and \(\tau_{c}\)) are related to porosity, which induces a relation between porosity and permeability. A lot of relations between porosity and permeability have been carried out. Some connect the two by a \(n\) factor [15; 16; 54], while others take into account the grain diameter \(d\) in a non-fractal [15; 16; 22; 55; 56] and in a fractal dimension [57; 58], the critical pore size \(d_{c}\)[23; 24], or the specific surface with the Kozeny factor \(c\)[27; 59; 60; 61]. All these relations have been tested on our data set, and the results are summarized in Table 4.  
Firstly, in line 1 of Table 4, good correlations are given with the general law \(k\propto\phi^{n}\)[15; 16], consistent with laboratory measurements (Table 1). In line 2, adding critical porosity \(\phi_{c}\) and fixing \(n=4\)[54] give similar permeabilities for chalks, while underestimate the ones for limestone (lower than 1 mD). Inserting calculated porosities and permeabilities from Table 1 in line 3[15; 16] give grains diameters values \(d\) consistent with the ones measured and observed in Figure 1. Using these grain diameters \(d\), various \(n\) factors are tested using the equation in line 4[22] and higher permeabilities have been found. Similar values have been calculated using critical pore size \(d_{c}\), extracted from centrifugation (line 5) [24]. In contrast, as it has been carried out using equation in line 6[23], taking into account formation factor \(F\) instead of porosity makes permeabilities significantly decrease. These equations enable us to average dolomite permeability. Based on observations in Figure 1, an average grain diameter of 30 \(\upmu\)m can be stated for dolomite samples, which gives permeabilities between 15 and 50 mD (line 3) [15; 16], while lower with line 4 equation [22] and \(n=6\) fixed. A critical pore size \(d_{c}\) of 11.5 \(\upmu\)m gives opposite permeabilities when taking into account porosity (line 5 equation [24]) or formation factor (line 6
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 8 Conclusions  
This paper formulates a runnable periods optimization problem for AUTOSAR control applications and provides an analytical solution based on the Lagrange multiplier method. Our method can find near-optimal solutions that maximize a given system's control performance regardless of the size and complexity of the application. Since the complexity of automobile control applications is rapidly growing due to the recent development of various advanced driver assistance systems and autonomous driving applications, it is no longer feasible to use traditional ad-hoc methods or time-consuming search-based optimization algorithms. Due to the analytical nature of our proposed runnable periods optimization method, we consider that our solution can be readily used in the automotive industry when designing their complex industry-scale AUTOSAR control applications.  
Although our method provides a promising solution for optimizing complex applications, our method is only usable when the control cost is given or approximated as a linear function. As the approximation can induce overestimated control costs, we plan to extend our optimization method to nonlinear control cost functions in our future work.  
Conceptualization, J.-C.K.; Data curation, D.C. and T.-W.K.; Methodology, D.C.; Software, D.C. and J.-C.K.; Supervision, J.-C.K.; Visualization, T.-W.K.; Writing--original draft, T.-W.K.; Writing--review & editing, J.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported partly by the Ministry of Land, Infrastructure, and Transport (MOLIT), Korea, through the Transportation Logistics Development Program (20 TLRP-B147674-03, Development of Operation Technology for V2X Truck Platooning) and partly by the National Research Foundation (NRF) grant funded by the Korea government (MSIT; Ministry of Science and ICT) (No. 2017R1C1B5018374) and partly by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2014-0-00065, Resilient Cyber-Physical Systems Research).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RTE & Runtime Environment \\ DAG & Directed Acyclic Graph \\ RTOS & Real-Time Operating System \\ L\&L & Liu and Layland \\ RM & Rate Monotonic \\ EDF & Earliest Deadline First \\ LQG & Linear Quadratic Gaussian \\ \end{tabular}  
Figure 13: Normalized average control costs of our method for large DAGs compared with the _PSO_ method.
## 6 Experimental Results
### Stability
#### 6.3.1 Training  
This section presents the training process along with the evolution of the Pareto front, which depicts the convergence of the model and, therefore, justifies the training time. QLHH-II has been trained on 50 instances over 10 epochs, resulting in 500 episodes. The evolution of the three MO metrics during training is illustrated in Figure 9. In terms of IGD, the algorithm features a very fast convergence in the first episodes and then continues to improve at a slower pace. When considering HV, it appears that QLHH-II improves solutions steadily with a major increase around the 300 th episode. With regard to the third metric, \(\Delta\), it converges until the 50 th episode and then worsens. This might be  
Figure 8: Solutions obtained with heuristics generated by QLHH and QLHH-II on two instances (the \(x\)-axis uses a logarithm scale due to the huge gap between heuristics).  
explained by the fact that adding any new non-dominated to the front may completely disrupt the diversity.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 3 Materials and Methods
### Data Preprocessing  
After data collection, we applied the following preprocessing steps to the audio files.  
Noise Reduction: The received audio recordings contained various noise and background sounds, as they were collected in different environments. The Audacity ([[https://www.audacityteam.org](https://www.audacityteam.org)]([https://www.audacityteam.org](https://www.audacityteam.org)), accessed on 10 November 2021) software was used to identify the noise in our dataset and helped in reducing it.  
Data Segmentation, Resampling, and Silence Truncation: Each recorded audio by a participant contained all the pronunciations, which were to be segmented into separate pronunciations for each vowel. This was done using audio segmentation based on the silence between each segment. We made all the segments in equal time duration of one-second padding the shorter one with silence. Since the received recordings were collected from different sources, the audio sampling rate varies from one sample to another; so, the audio recordings were resampled to a 16 kHz sample rate. Finally, we tested the segments of audio files to ensure intelligibility and clarity of pronunciation by listening to each segment and removing any sounds that were not clear or had the incorrect pronunciation of an alphabet.  
Data Labeling: To label each recorded instance belonging to a class, each short vowel is coded with a unique number for further processing. Table 2 presents the short vowels and their class labels.