esa-sceva/satcom-chunk-collection huggingface dataset:
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 2 Methods
### Cross-Power Spectrum Matrix Acquisition  
According to the classical phase correlation principle, an image \(f_{2}(x,\ y)\) is the image obtained by translating image \(f_{1}(x,\ y)\) with \((x_{0},\ y_{0})\), that is, \(f_{2}(x,\ y)=f_{1}(x-y_{0},\ y-y_{0})\). Let \(F_{1}(u,\ v),\ F_{2}(u,\ v)\) be the two-dimensional DFT results of \(f_{1}(x,\ y),\ f_{2}(x,\ y)\), respectively, and then, according to the Fourier time-shift properties, we can get:  
\[F_{2}(u,v)=F_{1}(u,v)\times e^{-i(ux_{0}+vy_{0})} \tag{1}\]  
Divide the left side of Equation (1) by the right side to get the cross-power spectrum matrix \(Q(u,v)\), per Equation (2):  
\[Q(u,v)=\frac{F_{1}(u,v)F_{2}{}^{*}(u,v)}{|F_{1}(u,v)F_{2}{}^{*}(u,v)|}=e^{-i( ax_{0}+vy_{0})} \tag{2}\]  
Since the low-frequency part of the cross-power spectrum matrix obtained in Equation (2) is in the upper-left corner of the matrix, frequency domain centering is needed in the actual processing process, so that the low-frequency part of the cross-power spectrum matrix is in the center, while the high-frequency part is around it. As shown in Figure 2, Figure 2a represents the cross-power spectrum matrix's phase without frequency domain centering, and Figure 2b represents the cross-power spectrum matrix's phase, where the frequency domain is centralized. It clearly demonstrates that after performing the frequency domain centralization, the areas with clear fringes can be gathered together, which is convenient for subsequent processing.  
In addition, the two-dimensional DFT assumes that the input signal is an infinitely long period signal. However, the image actually does not meet this requirement, which  
Figure 2: Cross-power spectrum and corresponding PSD, where (**a**) is the cross-power spectrum matrix’s phase where the frequency domain is not centralized, and (**b**) is the cross-power spectrum matrix’s phase where the frequency domain is centralized.  
will cause the problem of spectrum leakage in the actual processing. Therefore, windowing is required after the Fourier transformation. In this paper, the Hanning window function is used for windowing [45]. The function is defined per Equation (3):  
\[W_{k}(u,v)=\frac{1}{4}\bigg{[}1+\cos\bigg{(}\frac{u-H/2}{H}\pi\bigg{)}\bigg{]} \bigg{[}1+\cos\bigg{(}\frac{v-W/2}{W}\pi\bigg{)}\bigg{]} \tag{3}\]  
where \(W\) is the width of the image, \(H\) is the height of the image.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### System Details
#### 4.1.2 Control Unit  
A sophisticated navigation computer for navigating to the predetermined target and an autonomous control computer for mission planning are both included in the control unit. The control unit is powered by the controller battery and power supply. Failures in the mission controller's mission planning could result in timeouts and stops, and the emergency buoyancy control device's sudden weight release could interrupt the mission. Furthermore, there are instances in which a mission controller error results in a failure that renders mission execution impossible [79; 80]. Battery failures include overcharging, voltage and current monitoring failures, charging failures, and battery detection failures [81].
# Ten Points for High-Quality Statistical Reporting and Data Presentation
## 3 Results
### Distribution of the Total Quality Score  
The total score assessing quality of statistical reporting ranges from 0 to 10. Figure 1 shows the distribution of the quality score among all 160 articles. A total of 14 (8.8%) articles were poor and 42 (26.3%) articles did not reach up to an acceptable level.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & _Lancet_ & _JAMA Psy_ & _IJERPH_ & _JD_ & **All** \\  & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** \\ \hline \multicolumn{5}{c}{**Study design**} \\ \hline Observational studies & 11 (27.5) & 27 (67.5) & 27 (67.5) & 13 (32.5) & 78 (48.8) \\ Experimental studies & 27 (67.5) & 9 (22.5) & 6 (15.0) & 6 (15.0) & 48 (30.0) \\ Reliability & 1 (2.5) & 0 & 2 (5.0) & 2 (5.0) & 5 (3.1) \\ Laboratory works & 0 & 0 & 4 (10.0) & 14 (35.0) & 18 (11.3) \\ Meta-analysis & 1 (2.5) & 4 (10.0) & 1 (2.5) & 5 (12.5) & 11 (6.9) \\ \hline \multicolumn{5}{c}{**Sample size**} \\ \hline \(<\)99 & 0 & 3 (7.5) & 14 (35.0) & 20 (50.0) & 37 (23.1) \\
100–499 & 5 (12.5) & 12 (30.0) & 12 (30.0) & 8 (20.0) & 37 (23.1) \\
500–2999 & 22 (55.0) & 7 (17.5) & 6 (15.0) & 5 (12.5) & 40 (25.0) \\
5000–3000 & 12 (30.0) & 18 (45.0) & 5 (12.5) & 3 (7.5) & 38 (23.8) \\ Missing & 1 (2.5) & 0 & 3 (7.5) & 4 (10.0) & 8 (5.0) \\ \hline \multicolumn{5}{c}{Total number of articles} \\ \hline \multicolumn{5}{c}{_JAMA Psy = JAMA Psychiatry, JERPH = International Journal of Environmental Research and Public Health, and JD = Journal of Dentistry._} \\ \end{tabular}
\end{table}
Table 2: Distribution of study design and sample size of the evaluated articles by the publication journal.  
Figure 1: Column graph of the data analysis reporting quality score of 160 articles. Articles with poor quality (0–2) are denoted with red column color, those with weak quality (3–4) with yellow color and at least acceptable (5–10) articles with green color.  
The distribution of the data analysis reporting score is summarized in Table 3 by article characteristics. The mean (SD) of quality score in all 160 evaluated reports was 5.7 (SD 2.2). The reporting quality was highest in the meta-analyses and lowest in laboratory works. The data analysis reporting quality was associated with the sample size of the evaluated study.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 3 Descriptive Statistics
### Standard Error of the Mean and Confidence Intervals  
The standard error of the mean is a measurement that shows how different the sample mean is from the population mean. Basically, it describes how much of the sample mean would vary if the same study were repeated using different sets of samples within the same population (precision). It is calculated using the standard deviation and dividing it by the square root of the number of samples. The formula for the standard error of the mean is presented in Equation (6).  
\[SE_{M}=\frac{s}{\sqrt{n}} \tag{6}\]  
where \(SE_{M}\) is the standard error of the mean, \(s\) is the standard deviation, and \(n\) is the number of observations.  
\[SE_{M}=\frac{0.7727}{\sqrt{5}}\] \[SE_{M}=0.3456\text{ Log CFU}/\text{g}\]  
Furthermore, another parameter used to describe the results from a study where several replicates were collected is confidence intervals (CIs), which describe a range of values that is likely, with certain percentage of confidence, to include the population mean. Two-sided 95% CI is the most common range used in microbiological research, as it provides an estimate that the mean will fall inside the range with a 95% confidence level (CL). When data are normally distributed, a 95% CI is approximately determined as \(\pm 2\times SE_{M}\). If more certainty is needed, a 99% CI can be approximately estimated as \(\pm 3\times SE_{M}\). It is important to understand that with a bigger percentage of confidence, larger ranges between the confidence limits will be estimated. Moreover, a wider CI suggests that the estimatedpopulation mean have more uncertainty, and possibly more data need to be collected before reaching conclusions. The formula for the estimation of confidence intervals is represented in Equation (7).  
\[CI=\overline{X}_{geom}\pm t_{n-1,\;\alpha/2}\times SE_{M} \tag{7}\]  
where \(CI\) is the confidence interval, \(\overline{X}_{geom}\) is the geometric mean, \(t_{n-1,\;\alpha/2}\) is a factor estimated depending on the number of observations minus 1 and the confidence level (\(\alpha=1-CL/100\)) using Student's \(t\) distribution, and \(SE_{M}\) is the standard error of the mean. In our example, \(n-1\) equals 4 and \(\alpha/2\) equals 0.025, producing a factor value of 2.776.  
\[\begin{array}{c}95\% CI=3.6954\pm 2.776\times 0.3456\\ 95\% CI=(2.7360;4.6548)Log\;CFU/g\end{array}\]  
Note that the approximate upper (UCL) and lower (LCL) 95% confidence intervals around the geometric mean can be converted to CFU/g with the antilog of the confidence limits estimated before.  
\[\begin{array}{c}LCL=10^{2.7360}=545\;\text{CFU/g}\\ UCL=10^{4.6548}=45,164.79\;\text{CFU/g}\end{array}\]  
Also, the estimated confidence limits are asymmetrical around the mean value, and the large confidence interval is due to the outlier used in this example. These results can also express that on 20 occasions out of 100, the sample mean for a repeated test would be expected to fall outside the reported confidence interval.
## 2 Materials and Methods
### Bioinformatic Filtering  
Whole exome reads were aligned to GRCh38 and variants were identified with FreeBayes as previously described [38; 46]. Candidate variants were filtered by SnpEff (version 4.1g) [50] along with custom scripts to retain only non-synonymous SNPs, coding indels, and variants affecting splice sites. Known artifacts and variants whose frequency was greater than 0.05 in the ExAC database (r0.3) [51] were also stripped. If the variant was annotated in the dbNSFP database (version 3.0) [52; 53], it was retained only if at least one of the included variant prediction algorithms (SIFT, Polyphen2, LRT, MutationTaster) scored it as \"damaging\", signifying that the resulting change to the encoded protein had a predicted functional consequence. Variants that were not shared by all sequenced members of the family were not retained. Variants with a Minor Allele Frequency (MAF) < 0.05 that remained after the above filters were applied were retained for further analysis in separate gene sets. This MAF threshold was intentionally set higher than typical rare variant thresholds to account for the prevalence of the disease (2-3% of the general population), working under the hypothesis that low frequency variants may contribute to the high prevalence of this disease.
## 3 Improved CKF Localization Algorithm Based on Volume Criterion
### CKF Parameter Adaptive Algorithm Based on Sigmoid-like Functions  
The Sigmoid function is a common S-shaped growth curve in biology, which is characterized by strict monotonicity and good continuity. It is an excellent threshold function [24], with the following expression (18):  
\[f(x)=\frac{1}{1+e^{-x}} \tag{18}\]  
In the CKF algorithm, the calculation of the measurement noise matrix \(\mathbf{R}\) and error covariance matrix \(\mathbf{P}\) is closely related to the accuracy of individual measurement errors. Based on the CKF algorithm, this study defines a kind of Sigmoid function, combines the advantages of the function to freely adjust the boundaries of the function, and updates the measurement noise matrix \(\mathbf{R}\) in real time. Thus, the proposed algorithm can obtain higher estimation accuracy and stronger stability.  
\[\mathbf{R}[e(k)]=\lambda\frac{\mathrm{e}^{\mu e(k)}-\mathrm{e}^{-\mu e(k)}}{ \mathrm{e}^{\mu e(k)}+\mathrm{e}^{-\mu e(k)}} \tag{19}\]  
where \(\mathbf{e}(k)=\mid\mathbf{x}(k)-\mathbf{\hat{x}}(k)\mid\) represents the absolute value function of the difference in the position of the label at a given moment. \(\mathrm{e}\) is the base of the natural logarithm, and \(\lambda,\mu\) represents a real number.  
In the standard CKF algorithm solving process, the ranging anomalies have a significant impact on the results of the measurement update. The proposed algorithm is based on a kind of Sigmoid function of the CKF parameter adaptive method and the real-time update of the measurement noise matrix \(\mathbf{R}\). Thus, the correction of \(\mathbf{P}_{zz,k|k-1}\) is as follows (20):  
\[\mathbf{\overline{P}}_{zz,k|k-1}=\frac{1}{2n}\sum\limits_{i=1}^{2n}\mathbf{Z}_{i,k|k- 1}\mathbf{Z}_{i,k|k-1}^{\mathrm{T}}-\mathbf{\hat{Z}}_{k|k-1}\mathbf{Z}_{k|k-1}^{\mathrm{ T}}+\mathbf{R}[e(k)] \tag{20}\]  
\(\mathbf{\overline{K}}_{k}\) represents the system Kalman filter gain matrix.  
\[\mathbf{\overline{K}}_{k}=\mathbf{P}_{xx,k|k-1}\mathbf{\overline{P}}_{zz,k|k-1}^{-1} \tag{21}\]  
The state estimate \(\mathbf{x}_{k|k}\) and the estimated covariance \(\mathbf{P}_{k|k}\) at moment \(k\) are as follows (22):  
\[\left\{\begin{array}{ll}\mathbf{x}_{k|k}=\mathbf{\hat{x}}_{k|k-1}+\mathbf{\overline{K}}( \mathbf{z}_{k}-\mathbf{\hat{z}}_{k|k-1})\\ \mathbf{P}_{k|k}=\mathbf{P}_{k|k-1}+\mathbf{\overline{K}}_{k}\mathbf{\overline{P}}_{zz,k|k-1} \mathbf{\overline{K}}_{k}^{\mathrm{T}}\end{array}\right. \tag{22}\]
## 2 Materials and Methods
### Characterization  
The main elements of three carbon materials were measured by an Elemental Analyzer (EA, C440, made by Elementar Analysensysteme GmbH, Hanau, Germany). The surface functional groups in biochar were detected by X-ray Photoelectron Spectroscopy (XPS, Escalab 250 Xi, made by Thermo Fisher, Waltham, MA, USA). The surface areas were determined by the N\({}_{2}\)-BET (C418, made by Micromerities Instruments Corporation, Norcross, GA, USA). The surface microscopic morphology of the carbon materials was detected by SEM (SU8020, made by Hitachi High-tech, Tokyo, Japan).
# Smart Healthcare Network Management: A Comprehensive Review
## 6 Architecture Implementation Plan
### Smart Healthcare Service Management  
Service management is the focus of the management system to ensure service continuity. Due to the sensitivity of the smart healthcare system, healthcare services should be provisioned without anomalies. Service malfunctions can happen because of hardware or software faults, which cause network elements to produce incorrect outputs. When the smart healthcare system is deployed on a massive scale, it becomes hard to monitor and manage such services, especially with the reception of a considerable number of alarms and sensor-related management data. To provide efficient alarm filtering and real-time decisions, we propose using deep learning techniques for this purpose. To further elaborate on the use of deep learning in fault management, the workflow of the fault management process is outlined as follows:  
* Collect monitoring and performance alarms.
* Provide feedback to users to ensure service continuity.
* Filter, eliminate, and correlate the alarms.
* Diagnose faults through analysis and testing to eliminate the cause of the fault.
* Determine a plan for correction, display correction options to patients, and implement the plan.
* Verify the fault is eliminated.
* Record data and determine the effectiveness of the current fault management process.  
Deep learning can help in filtering, classifying, and correlating alarms and recognizing basic fault patterns. However, to identify the exact fault location, a further process is necessary. This procedure involves human intervention to make some decisions that might  
Figure 8: SDN-based traffic classification and management.  
be deemed impossible by deep learning alone. Some work suggests using case-based reasoning to analyze the data further and run tests to identify the exact location of the fault [104]. However, one recent work suggests an architecture for deep learning that explains its reasoning process and learns by observation [107].
## 3 Results and Discussion
### Screening and Identification of HSP47 Small Molecular Inhibitors  
The binding mode between HSP47 and the collagen peptide shown above was used to design a virtual screening for potential HSP47 inhibitors, focusing on the footprint of collagen residues 0, -3, and -4 upon the surface of HSP47. After initial screening, we manually selected 58 compounds for subsequent inhibitory activity assay according to the diversity and potential interactive mechanisms between the compounds and HSP47. Collagen molecules in a buffer of physiological pH spontaneously associated to form fibrils [17; 35; 36], while the turbidity gradually increased in a concentration-dependent manner and then reached a plateau phase (Figure 4a). HSP47 retarded this fibril formation process within the range of 2.0-5.2 uM (Figure 4b). An HSP47 concentration of 2.6 uM was used for the compound activity assay (Figure 4c-e), while a previously reported HSP47 inhibitor, Com II, was selected as a positive control.  
Figure 4: Screening and discovery of HSP47 small molecular inhibitors. Collagen fibril formation was measured in the absence (**a**) and presence (**b**) of HSP47. Collagen fibril formation was monitored at 313 nm in phosphate buffer. Inhibition effect of Com II (**c**), Hs1 (**d**), and Hs55 (**e**) against HSP47 in turbidity assay. (**f**) The binding activity of Hs1 against HSP47 in the label-free MST experiment.  
Our measurement showed IC\({}_{50}\) values for Com II in the range of 50-100 uM (Figure 4c), which was comparable to the reported value of 26.6 uM [17]. Amongst our selected compounds, compound Hs1 and Hs55 have the most promising activity, with IC\({}_{50}\) values of 97 uM and 55 uM, respectively (Figure 4d,e). As Hs1 had better water solubility, we determined its binding affinity to HSP47 using microscale thermophoresis (MST). This revealed that Hs1 binds human HSP47 with a dissociation constant (Kd) of \(\sim\)39 uM (Figure 4f), consistent with its blocking activity in inhibiting collagen fibril formation (Figure 4d).
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
###### Abstract  
This paper presents a novel method designing an optimized waveform with high spectral efficiency based on minimum out-of-band radiation criterion. Introducing the Pan-function model, Fourier series, and constraints, we achieve the numeric solution of the optimized baseband signals. The optimal waveform provides an excellent bandwidth efficiency, and its bit error rate (BER) performance is improved using the minimum cross-correlation function. To verify the proposed model, we present a simulation of the signal modulation and demodulation schemes using MATLAB software. The results are consistent with expected results, verifying the correctness of this method.  
optimized waveform; spectral efficiency; Pan-function model +
Footnote †: journal: Applied Sciences
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 6 Conclusions  
In this paper, we reviewed different equalization techniques for SDM transmission systems. Efficient implementations of the proposed equalization algorithms in FD were also studied.  
We introduced the novel FD-SSA and its corresponding MIMO implementation, and compared it to the FD-LMS in terms of performance (OSNR-BER), convergence speed, and frequency offset tolerance. The main advantage of the FD-SSA filter update rule is its insensitivity to frequency offsets and phase noise. SSA equalizer can effectively achieve convergence in scenarios with high frequency offsets and linewidths, which may result in instabilities for the FD-LMS equalizer.  
However, SSA suffers from slow convergence for big number of taps. Therefore implementations of the SSA based on algorithms capable of speeding up the convergence, such as the power-directed or the noise-directed filter update rule [29], may be desired in the future. Another alternative is reducing the impulse response by careful fiber design. Those systems may take advantage of SSA equalization technique without needing further optimization of the convergence speed.  
This work has been supported by the EC through H2020 project ROAM (grant no: 645361, www.roam-project.eu).  
[PERSON] prepared the manuscript intensive feedback, proofreading and discussions from all authors.  
The authors declare no conflict of interest.
## 7 Conclusions  
Currently, with the accelerated transition from fossil fueled vehicles to electric vehicles (EVs), research in the literature is focused on solving the Electric Vehicle Routing Problem  
Figure 12: Comparison of minimum travel distances of the 3 approaches.  
Figure 13: Comparison of execution times of the three approaches.  
(EVRP). This study addresses the Capacitated Electric Vehicle Routing Problem (CEVRP) by proposing a novel Q-learning algorithm to optimize routing for electric vehicles.  
The ESOGU-CEVRPTW dataset, which is based on real-world geographical data, is introduced to provide a realistic test environment. The proposed method is evaluated using the ESOGU-CEVRPTW dataset. The experimental results demonstrate that the proposed Q-learning method is capable of generating near-optimal solutions in significantly shorter computational times compared to traditional exact methods. These findings highlight the algorithm's potential for real-time applications in last-mile delivery and logistics planning, particularly in scenarios where rapid decision making is critical. The integration of real-world data into the model ensures that the solutions are not only theoretically sound but also practically applicable, filling an important gap in the existing literature where synthetic datasets are commonly used.  
Despite its strengths, this study has certain limitations. The current Q-learning approach does not incorporate time windows (TWs), which are a critical constraint in many real-world logistics scenarios. Future research is planned to focus on extending the Q-learning framework to handle problems with narrow and wide time windows. Additionally, deep reinforcement learning methods for solving the CEVRPTW will be addressed.  
Conceptualization: I.S. and O.A.Y.; methodology: O.A.Y.; formal analysis and investigation: O.A.Y.; writing--original draft preparation: O.A.Y.; writing--review and editing: I.S. and A.Y.; funding acquisition: A.Y.; supervision: O.A.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 22 AG040. This research is supported by the OPEVA project that has received funding within the Key Digital Technologies Joint Undertaking (KDT JU) from the European Union's Horizon Europe Programme and the National Authorities (France, Belgium, Czechia, Italy, Portugal, Turkey, Switzerland) under grant agreement 101097267. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or KDT JU. Neither the European Union nor the granting authority can be held responsible for them. This research is also supported by the TUBITAK, Contract No 222N269.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.