esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 3 CPR Using One-Tap NLMS  
A one-tap NLMS filter could be effectively applied in the CPR [20], and its tap weight \(w(k)\) is expressed using the following equations:  
\[w(k+1)=w(k)+\frac{\mu}{\left|x(k)\right|^{2}}x^{*}(k)e(k), \tag{2}\]  
\[e(k)=d(k)-w(k)\cdot x(k), \tag{3}\]  
where \(x(k)\) is the input signal, \(k\) is the symbol index, \(d(k)\) is the desired symbol, \(e(k)\) is the error between the output and the desired symbols, and \(\mu\) represents a parameter of the step size.  
The schematic of the NLMS CPR is illustrated in Figure 2, which can actually be implemented in a feed-forward structure and can be realized in parallel in the field programmable gate array (FPGA) circuits for a real-time operation [4].  
Similar to the definition of the Tx and the LO, a concept of effective linewidth \(\Delta f_{Eff}\) is employed here to describe the total phase noise in coherent transmission systems considering EEPN [17; 19]:  
\[\Delta f_{Eff}\approx\frac{\sigma_{Tx}^{2}+\sigma_{LO}^{2}+\sigma_{EEPN}^{2}}{ 2\pi T_{S}} \tag{4}\]  
\[\sigma_{Tx}^{2}=2\pi\Delta f_{Tx}\cdot T_{S} \tag{5}\]  
\[\sigma_{LO}^{2}=2\pi\Delta f_{LO}\cdot T_{S} \tag{6}\]  
where \(\sigma_{Tx}^{2}\) and \(\sigma_{LO}^{2}\) are the LPN variance of the Tx and LO lasers, respectively, and \(\Delta f_{Tx}\) is the 3-dB linewidth of the Tx laser.  
It has been established that the step size \(\mu\) can be optimized to enhance the performance of NLMS CPR [19]. Therefore, it is significant to find the optimal step size for NLMS CPR, when the EEPN is considered in coherent optical communication systems. Figure 3 shows the optimal step size parameter for various effective linewidths in the 28-Gsym/s QPSK system, applicable to the time-domain and the frequency-domain static dispersion equalizations [22; 23]. In all numerical simulations in this paper, the NLMS algorithm is operated with its corresponding optimal value of the step size.  
Figure 2: Block diagram of the one-tap normalized least-mean-square (NLMS) carrier phase recovery (CPR).
# Anchor Design of a Ring Joint Based on Reliability in a Precast Shear Wall Structure
## 2 Experimental Research
### Results Analysis
#### 2.3.2 Bearing Capacity and Deformation Performance  
The force _versus_ displacement curves were the basis for evaluating the bearing capacity performance, and the results were shown in Figure 6. The curves are initially linear and specimens are in flexible phase. The gradient of the curves decreases and specimens enter the elastoplastic stage with increasing loading. The curves decrease abruptly in the pull-out specimens, and the curves show an increasing trend before the plastic stage in rebar fracture specimens. The curves develop without abrupt changes until the rebar fractures. Obviously, small discrepancies in curves indicates that the bearing performance is stable and reliable. The yield ratio (\(F_{y}\)/\(F_{\text{u}}\)) and ductility coefficient (\(\Delta_{\text{u}}\)/\(\Delta_{y}\)) are important indicators to evaluate the bearing capacity and deformation capacity of the joint.  
Figure 5: Crack and failure mode of interval arrangement in Group B. (**a**) Interval arrangement; (**b**) Crack pattern of B11.  
Figure 6: _Cont._  
In terms of composite height, four levels were used to evaluate the effects on the bearing performance. The yield load increases from 4% to 31% with a single-step increase (Figure 7a), and the yield ratio increases 6%-16% per stage (Figure 7b). The ductility coefficient increases 1%-20% per stage, as can be seen in Figure 7c. In addition, the cracks was significantly reduced with increasing composite height. Therefore, increasing the composite height could significantly improve the anchoring performance of the concrete to ring rebars, thereby improving the bearing performance.  
Compared with the benchmark group A3, the interval arrangement in group B (Figure 5a) exhibits an 11% decrease in the yield load, and it also results in an 11% decrease in the yield ratio and a 53% increase in the ductility factor. Therefore, the interval arrangement not only decreases the bearing capacity, but also undergoes a greater deformation, that is an unfavorable factor.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 2 Background and Motivation  
Similar to any modern radio system, GNSS receivers are composed of an analog stage, used for the recovery of Radio Frequency (RF) signals (e.g., antenna, amplifiers, Analog-to-Digital Converter (ADC)s), and a digital stage, used for DSP and extraction of the signal data. SyDR mainly focuses on the digital stage's DSP algorithms. As detailed in our previous work [9], the goal of SyDR is not only to propose a software implementation of a GNSS receiver in a high-level programming language--Python--but also to render it modular enough to enable benchmarking of algorithms within a realistic GNSS receiver architecture. The benefits for researchers include, among others, avoiding redundant development and focusing research efforts on the underlying algorithms. Using a well-defined framework also ensures any changes are isolated to the algorithms, thus enhancing their comparability. We start by recalling the typical GNSS processing stages before diving into the motivations to go closer to the hardware through the PYNQ platform.
###### Abstract  
This paper presents the combustion and emissions characteristics including volatile organic compound (VOC) of a common rail direct injection diesel engine fueled with palm oil biodiesel blends contained 0%, 10%, 30%, and 100% (by volume) biodiesel at low idle speed, i.e., 750 rpm. The nitrogen oxide (NOx) emissions of biodiesel blends were lower than that of pure diesel and NOx tended to decrease as the blending ratio increased. Soot opacity and hydrocarbon (HC) were reduced with an increasing blend ratio. Carbon monoxide (CO) varied with the engine load conditions. Under low load, CO emissions tended to decrease with increasing blending ratio and increased under high load. Alkane and aromatic VOCs were mostly emitted. Benzene and tetrahydrofuran accounted for the largest percentage of total detected VOCs in all test conditions. Benzene, toluene, ethylbenzene, xylene (BTEX, toxic aromatic VOCs) were detected for all tests. Among BTEX, benzene has the highest emission ratio, followed by xylene, toluene, and ethylbenzene. Benzene increased for all tests. At low engine load, toluene, ethylbenzene, and xylene decreased with increasing blend ratio. However, these increased at high engine load. When pure palm oil biodiesel was applied at high engine load, benzene decreased.  
palm oil biodiesel; idle; combustion; emission; VOCs; BTEX
# Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM
## 3 Results and Discussion
### Model Analysis  
To analyze the performance of MLFN-21, its non-linear fitting process should be firstly discussed. In the model training process (Figure 5), the predicted values are generally close to the actual values (Figure 5a). Residual values are generally close to zero except for several discrete points (Figure 5b,c). The results of the training process show that the non-linear fitting process of MLFN-21 for classifying the four carbon fiber fabrics is decent. In terms of the model testing process (Figure 6), the predicted values in the testing set are very close to the actual values (Figure 6a), with comparatively low residual values (Figure 6b,c). The results of the testing process show that the MLFN-21 has a strong capacity for classifying the four carbon fiber fabrics, with the use of the four quantified dependent values: 200, 400, 600 and 800.  
To evaluate the reproducibility of the ANNs, it should be firstly noted that the initial values of the weights are chosen randomly at the beginning of training. Then the initial weights are tuned according to the errors between the actual and desired outputs. Therefore, the training results of each single model development with the same component of the training and testing sets are always different, which is reflected in the fluctuation of RMSEs in the testing set. To prove that an ANN has good reproducibility, repeated experiments should be done in order to evaluate whether the RMSEs in the dependent training process have a stable fluctuation. To test the reproducibility of the MLFN-21 of this study, we repeated the training 100 times using the same component of the training and testing sets (Figure 7). It shows that MLFN-21 has a very stable fluctuation in the RMSE in testing during all repeated experiments. All RMSEs in testing are in the range between 21.1 and 41. The repeated experiments show that MLFN-21 has very good reproducibility for classifying the four carbon fiber fabrics.  
Figure 5: Training results of MLFN-21. (**a**) Predicted values versus actual values; (**b**) residual values versus actual values; (**c**) residual values versus predicted values.
## 3 NRL Analysis:  
* **Regular Traffic:** Generally, it can be noticed from Figure 25 that both AODV and DSR provide higher NRL values at different node speeds, which is mainly related to frequent route discovery requests. DSDV maintained consistent NRL across different node speeds, as it uses routing tables.
* **Irregular Traffic:** Figure 26 indicates that DSDV provided bad performance at different node speeds due to increased traffic load, while DSR showed a superior behavior, benefiting from the \"ON/OFF\" traffic pattern.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 4 Joint Simulation of the Calibration Process  
### Model Description  
Independent transient simulations for the pistonphone and microphone, as shown in Figures 6 and 10, show that the sound pressure amplitude of both the pistonphone and the microphone will decrease at low frequencies. Meanwhile, the phase will be ahead of their excitation. Due to the more severe pressure leakage and heat conduction loss in the microphone, the amplitude attenuation and phase lead are earlier in microphone than in pistonphone, and thus the phase variation in microphone sensitivity will happen at low frequencies.  
The key to the simulation of the primary calibration process is to apply the calibrating sound pressure in the pistonphone to the microphone diaphragm. However, the size difference between the pistonphone and the microphone is quite large; in particular, the equalizing hole of pistonphone, the vent of microphone, and the gap between the diaphragm and the back-plate are only several microns. If two models are directly coupled, the calculation is computationally expensive. Therefore, a joint simulation method is adopted to firstly calculate the calibrating sound pressure in the pistonphone and then add it to the diaphragm of the microphone model to obtain the desired phase sensitivity of the microphone.  
In addition, depending on whether the vent of microphone is installed in or out of the calibrating field during calibrations, as shown in Figure 11, the calibration results will be very different. Thus, these two calibration states are studied. For vent in field calibrations, the sound pressure in the pistonphone is quite uniform at low frequencies, so that the incident sound pressure on the diaphragm and the outer end of the vent are both the calibrating sound pressure obtained from the independent pistonphone simulation. Meanwhile, for vent out field calibrations the incident sound pressure on the outer end of the vent is 0.  
The \"Structural mechanics, diaphragm\" interface is now added to the microphone model to apply the calibrating sound pressure on the diaphragm, as shown in Figure 12. The edge of the diaphragm is set fixed and a tension preload \(T_{\text{m0}}\) is applied to it. The \"thermosivscous acoustics-structure boundary\" coupling module is used to couple the diaphragm and the sound field. The \"Moving Mesh\" interface is used to accurately simulate the diaphragm vibration [33].  
Figure 10: (**a**) Sound pressure waveforms of the associated sound pressure at 1 Hz. (**b**) Sound pressure waveforms of the associated sound pressure at 0.1 Hz.  
When the electrical characteristics of the microphone and its matched preamplifier are considered as constant, the diaphragm deformation of the microphone is equivalent to its voltage output. Therefore, the phase difference between the diaphragm deformation of the microphone and the calibrating sound pressure in the pistonphone is the desired phase sensitivity of the microphone.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum
#### 2.3.2 Interpolation by Matching Pursuit (IMAP)  
The IMAP is a least-squares-based method proposed to mitigate the spectral leakages in the spectrum and can be used to regularize seismic data ([PERSON], 2016). In IMAP, a frequency \(\omega_{1}\) corresponding to the largest peak in the spectrum ([PERSON], 2016) is selected, i.e., a frequency \(\omega_{1}\) such that Equation (7) (or just its numerator) is maximized. Then the contribution of the sinusoidal basis functions of frequency \(\omega_{1}\) to the time series is subtracted from it to obtain the residual series. In the next step, the residual series is treated as a new input time series to choose a frequency \(\omega_{2}\) corresponding to the largest peak in the new residual spectrum, and this procedure continues until the desired frequencies are estimated. This iterative method mitigates the spectral leakages in the spectrum and results in a faster convergence. A flowchart of IMAP is shown in Figure A3.  
Unlike ALFT, IMAP matches the basis functions to the data rather than the data being mapped to the basis-function domain ([PERSON] et al., 2016). Since the correlation between the sine and cosine basis functions are considered in IMAP, its spectrum generally shows much fewer leakages than ALFT and ASFT, and so it performs better for regularization. Although IMAP improves the spectral leakages in the spectrum, it does not explicitly account for the correlations among the sinusoids of different frequencies, and so its spectrum contains many frequencies due to leakages that increase the computational cost and reduce the accuracy of the regularization results. [PERSON] et al. (2016) have also studied estimating many frequencies per iteration and have documented several de-aliasing schemes.
## 3 Results and Discussion
### Indirect Tensile Strength Test  
A plot of load against displacement for the curing period of 7, 28 and 90 days was shown in Figure 5. The continuous reduction in displacement is observed with the treated soil samples with fibre. At the curing period of 7 day, the load keeps increasing with 165.63 N, 1066.85 N and 4023.85 N respectively by decreasing the displacement to 1.74, 1.00 and 0.36 mm for all specimens. The trend on increasing load with the decreasing displacement was same as the 28 and 90 days which applied to 234.93, 1966.31 and 10,115.89 N to 1.50, 1.05 and 0.62 mm for 28 days and 280.33, 2269.21 and 12,841 N to 1.66, 0.82 and 1.12 mm for 90 days curing. Meanwhile, it is noticeable that there is a high contrasting obtained for CFAF for all curing period compared to untreated soil which indicates soil stiffness. It can be explained due to the inclusion of treated fibre with alkaline activation resulting in changes of the behaviour of soil and improvement of soil strength. According to [51], the tensile strength of modified fibre with lime soil can be explained by the altered surface morphology which results in the soil strength enhancement.
## 5 Experiments and Results
### Experimental Results
#### 5.4.1 Comparison Results of Running Performance  
The operational performances of the four models are presented in Table 3. Among them, the Faster-RCNN model exhibited, significantly, the lowest performance, with the highest number of parameters (136.75 \(\times\) 10\({}^{6}\)), memory consumption (534.2 MB), and video memory usage (5.6 GB), as well as the longest training duration (7.1 h) and the lowest frame rate (12.80 f\(\cdot\)s\({}^{-1}\)). On the other hand, the YOLO series models, which are single-stage algorithms, demonstrated significantly faster running performances. The YOLOv7-tiny model had the fewest parameters and minimal memory requirements, while achieving higher frame rates for YOLOv5s and YOLOv8s, along with a faster execution speed.  
When considering identical datasets, it can be concluded that the Faster-RCNN model required a superior running performance and environment configuration, whereas the YOLO model series required a lower hardware and software environment configuration, while offering faster training speeds. Consequently, the YOLO model series algorithms are highly suitable for the lightweight deployment of real-time detection tasks on UAV platforms.
## 5 Conclusions  
To summarize, our model demonstrates significant improvements in efficiency and accuracy in multibeam echosounding measurements through advanced optimization techniques and intelligent algorithms. The model dynamically adapts to complex seabed terrain, ensuring extensive coverage and minimal survey line overlap, achieving an effective coverage area of \(1.1897\times 10^{8}\) m\({}^{2}\) with a total survey line length of only 434,662 m. This high efficiency is maintained alongside excellent accuracy, and data integrity remains strong even under varying environmental conditions. Our model is rated high in robustness and generalization ability, proving its reliability and adaptability in diverse marine environments.  
Furthermore, robustness analysis indicates that our model maintains high performance under varying depth and slope conditions, showcasing its versatility and practicality in real-world applications. Despite some limitations in extreme conditions, the overall performance significantly surpasses existing models, highlighting the potential of our model to enhance the accuracy, efficiency, and reliability of marine measurements. Continuous optimization and field testing will further strengthen these findings and extend the model's applicability to a broader range of marine measurement scenarios.  
**Author Contributions:** All authors made significant contributions to the conceptualization and design of the study. The field experiments and data collection were conducted by [PERSON] under the supervision of [PERSON]; [PERSON] provided assistance with the modeling. The manuscript was primarily written by [PERSON] and critically reviewed and edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (No. 12250410247) and the Ministry of Science and Technology of China (No. WGXZ2023054L).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors would like to thank [PERSON] for his support and guidance in this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 3 BSS-VQ Algorithm for ISF Quantization
### Encoding Procedure of BSS-VQ  
In the encoding procedure of BSS-VQ, the cumulative probability \(P_{cum}(M|bss_{k})\) is firstly defined as the sum of the top \(M\)\(P_{hit}(m|bss_{k})\) in \(bss_{k}\), that is:  
\[P_{cum}(M|bss_{k})=\sum_{m=1}^{M}P_{hit}(m|bss_{k})\,,\ 1\leq M\leq CSize. \tag{13}\]Subsequently, given a threshold of quantization accuracy (\(TQA\)), a quantity \(M_{k}(TQA)\) represents the minimum value of \(M\) that satisfies the condition \(P_{cum}(M|bss_{k})\geq TQA\) in \(bss_{k}\), that is:  
\[M_{k}(TQA)=\underset{M}{\text{argmin}}\{M:P_{cum}(M|bss_{k})\geq TQA\},\;1\leq M \leq C\text{{Size}},\;0\leq k<BSize. \tag{14}\]  
For a given \(TQA\), a total of 512 \(M_{k}(TQA)\)s are evaluated by Equation (14) for all the subspaces, and the mean value is then given as:  
\[\overline{M}(TQA)=\frac{1}{BSize}\sum_{k=0}^{BSize-1}M_{k}(TQA). \tag{15}\]  
Illustrated in Figure 1 is a plot of the average number of searches \(\overline{M}(TQA)\) corresponding to the values of \(TQA\) ranging between 0.90 and 0.99. Given a \(TQA\) = 0.95 as an instance, a mere average of 14.58 codeword searches is required to reach a search accuracy as high as 95%. In simple terms, the search performance can be significantly improved at the cost of a small drop in search accuracy. Furthermore, a trade-off can be made instantly between the quantization accuracy and the search load according to Figure 1. Hence, a BSS-VQ encoding procedure is described below as Algorithm 2.  
```
Step 1. Given a \(TQA\), \(M_{k}(TQA)\) satisfying Equation (14) is found directly in the lookup table in \(bss_{k}\). Step 2. Referencing Table 3 and by means of Equations (9) and (10), an input vector is assigned to a subspace \(bss_{k}\) in an efficient manner. Step 3. A full search for the best-matched codeword is performed on the top \(M_{k}(TQA)\) sorted codewords in \(bss_{k}\), and then the output is the index of the found codeword. Step 4. Repeat Steps 2 and 3 until all the input vectors are encoded.
```  
**Algorithm 2** Encoding procedure of BSS-VQ  
The BSS-VQ algorithm is briefly summarized as follows. Table 3 is the outcome by performing Equation (8) and is saved as the first lookup table. Subsequently, the second lookup table concerning \(P_{hit}(m|bss_{k})\) and the corresponding codeword is built for each subspace according to the training procedure. Accordingly, the VQ encoding can be performed using Algorithm 2.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 5 Exploratory Data Analysis
### K-Fold Cross-Validation  
K-fold cross-validation is the most commonly applied process for model estimation. In this process, the available data are divided equally into 'K' parts. Each part is used for model validation (testing) in turn, while the remaining parts are used to train the model. In the end, the best model is the one that has better average performance for all different sets of validation subsets [80]. This process resolves overfitting issues and makes the model robust for application on new datasets [81].
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
###### Abstract  
Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the \(\beta\) function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (\(\beta\)-MIAMC), which involves comprehensively assessing the cognitive user's performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the \(\beta\)-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.  
collaborative spectrum sensing; spectrum-sensing false data; \(\beta\) function; feedback iteration; false alarm probability of malicious users +
Footnote †: journal:  
_Future Internet_**2017**, 9, 79; doi:10.3390/f9040079