esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 4 Simulation for Correntropy Induced Metrics and Cyclic Correntropy Spectrum
### The Role of Cyclic Correntropy Spectrum  
Use the amplitude-modulated signal to validate the role of the CCES, the simulation signal is expressed as (15), the sampling frequency is 16 kHz, and the sample points is 4096. The time-domain waveform is displayed in Figure 5a, where the obvious amplitude modulation phenomenon can be seen. Additionally, through the frequency spectrogram in Figure 5b, we can see the carrier frequency of 1000 Hz, and the modulation signal frequency shown by its sidebands is about 100 Hz.  
\[s=(5+\sin(200\times\pi\times t))\times\sin(2000\times\pi\times t) \tag{15}\]  
To compare the envelope spectrum generated by the Hilbert transform and the CCES used in this work, the Hilbert envelope spectrum and CCES are, respectively, used for the above-mentioned simulated amplitude modulation signal. Figure 6 gives the outcome of the two methods.  
Figure 6a is the envelope spectrum generated by the Hilbert transform, which only displays the frequency of the modulated signal 100 Hz. This shows that the envelope spectrum is effective in extracting the characteristic frequency of the modulated signal. However, the CCES in Figure 6b displays the frequency of the modulated signal 100 Hz and  
Figure 5: The Amplitude-Modulation signal. (**a**) Time-domain waveform. (**b**) Frequency spectrum.  
Figure 6: Envelope spectrum and CCES of the simulation signal. (**a**) Envelope spectrum. (**b**) CCES.  
the carrier frequency 1000 Hz. In addition, CCES also shows the sidebands near the carrier frequency, which can also express the frequency of the modulated signal. This means that CCES not only has the ability of the Hilbert envelope spectrum to demodulate and display the frequency of the modulated signal, but its spectrum also contains more information related to the signal itself than the Hilbert envelope spectrum.  
Because CCES is a cyclic spectrum, we can also find the double second harmonic frequency of the modulated signal frequency 199.2 Hz in Figure 6b. In addition, CCES also expresses the carrier frequency of 1000 Hz, which is also a characteristic of the signal we are analyzing. Additionally, the 2000 Hz and 3000 Hz are the second and third harmonic frequency of the carrier frequency. Both the carrier frequency and the modulating wave frequency are the characteristics of the signal. Due to our different application backgrounds, the target frequency is different, but the Hilbert envelope spectrum only expresses the modulating wave frequency. Our CCES not only expresses the frequency of the modulation wave alone, but also expresses the carrier frequency alone, and due to the characteristics of its cyclic spectrum, it distinguishes the two frequency components well: 100 Hz and 200 Hz express the frequency components of the modulation signal; 1000 Hz, 2000 Hz, and 3000 Hz express the frequency components of the carrier signal.
## 4 Conclusions  
The performance of nitrogen removal through partial nitrification coupled with ANAMMOX technology treating livestock wastewater was studied to evaluate the influences of non-biodegradable organic matter and microbial communities in this research. The obtained results are as follows:  
Partial nitrification was successfully achieved for treating the livestock wastewater. At loading rates of 3.08 kg COD/(m\({}^{3}\).d) and 0.93 kg NH\({}_{4}\)\({}^{+}\)-N/(m\({}^{3}\).d), COD removal efficiency was 87.6 \(\pm\) 1.7% and 89.7 \(\pm\) 3.4%, and average nitrite accumulation efficiencies of 78.4 \(\pm\) 1.3% and 64.7 \(\pm\) 3.2% were obtained in IASBR and CASBR, respectively.  
The optimal alternating non-aeration duration was 20-30 min for partial nitrification in IASBR, and the intermittent aeration strategy was discovered to induce a lag reaction for NO\({}_{2}\)\({}^{-}\)-N oxidation in a short time, but had no further effects on the inhibition of NOB activity.  
_Nitrosomonas_ was the key bacteria for NH\({}_{4}\)\({}^{+}\)-N oxidation in the partial nitrification process. Under the intermittent aeration strategy, the quantity of _Nitrosomonas_ increased from 9.3% to 11.8%, but only a 0.4% increase appeared in CASBR. Heterotrophic _Marichromatium_ was the main NO\({}_{2}\)\({}^{-}\)-N oxidation bacteria under the high-concentration non-biodegradable organic matter condition.  
Feeding the effluents of partial nitrification into the ANAMMOX reactor, non- biodegradable organic matter showed potential inhibition effects on ANAMMOX activity with the extension of exposure reaction time and the increase in COD concentrations. When the concentration of non-biodegradable organic matter was lower than 168 \(\pm\) 9 mg COD/L, an average NO\({}_{2}\)\({}^{-}\)-N removal rate of 87.4% was achieved.  
Conceptualization, M.Z. and X.C.; methodology, M.Z. and X.X.; validation, X.X.; formal analysis, M.Z. and X.C.; investigation, M.Z. and X.C.; data curation, M.Z. and Z.F.; writing--original draft preparation, M.Z. and Z.F.; writing--review and editing, X.Z.; project administration and funding acquisition, M.Z. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Ref. 51408104).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.
# Simplified Welch Algorithm for Spectrum Monitoring
## 5 Laboratory Tests and Real-Time Validation
### Practical Setup and Real-Time Test Bench  
This section tries to validate the proposed algorithm by representing the results obtained in a real-time test bench. The data acquisition and setup are similar to the ones used in [27; 28] and with a simulation-based debugging before FPGA synthesis. Use of a software-defined radio (SDR) allowed us to compare the results with reliable standardized spectrum monitoring devices and make a real-time comparison with them. The diagram in Figures 15 and 16 presents the laboratory setup used to validate and compare the algorithm to two standard spectrum monitoring devices. For comparison, two commercially available spectrum monitoring devices from Rohde & Schwarz [29] and Tektronix [30] are used to validate the obtained results. In the case of the Tektronix analyzer, the Long-Term Evolution (LTE) without averaging spectrum analyzer was used to display the signal even more clearly.
## 5 The Application of Tensile Strength Tests' Results to Determine the Extension Strain Criterion
### Discussion of the Results  
Although the tensile test, according to the ISRM [22] or ASTM [23] guidelines, ends immediately after the maximum force is applied, looking at the tensile characteristics of sandstone samples (Figure 2a) shows that they are not linear, especially in the last loading phase. It can be assumed that in the case of the sandstone under analysis, stress concentration near the loading platen (Figure 8 red circles) leads to an early shear failure fracture in the rock [28; 29].  
The analysis method according to Equation (8) is adopted, and the extension strain values corresponding to [PERSON]'s criterion obtained for Brazilian sandstone tests are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & \(\sigma_{\text{T}}\) & \(\varepsilon_{\text{ext}}\) \\ \hline  & **MPa** & [–] \\ \hline p\_r\_1 & 4.64 & 0.00058 \\ \hline p\_r\_2 & 4.74 & 0.00059 \\ \hline p\_r\_3 & 5.27 & 0.00066 \\ \hline p\_r\_4 & 4.26 & 0.00053 \\ \hline p\_r\_5 & 4.80 & 0.00060 \\ \hline Average & 4.74 & 0.00059 \\ \hline St. Dev. & 0.32 & 0.00004 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The tensile strength \(\sigma_{\text{T}}\) and extension strain criterion values \(\varepsilon_{\text{ext}}\) corresponding to [PERSON]’s criterion obtained for Brazilian tests.  
Figure 8: Shear failure and fractures as stress concentration in the sample near the loading platen.  
It can be noted that these values differ from those obtained in the UCS tests and are about three times higher. This may have resulted from the failure mechanism in the Brazilian sandstone test, which shows shearing in the loading platen region. For the sandstone studied, these values differ significantly from those also obtained by other authors [1; 21]. In this case, the method cannot be recommended for obtaining the extension strain criterion values corresponding to [PERSON]'s criterion.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 3 Verification of the Numerical Algorithm
### Field Experiment
#### 3.2.2 Experimental Results  
The impedance functions of the pile-group foundation can be obtained in virtue of the inverse properties of the dynamic flexibility matrix and dynamic stiffness matrix. In other words, by comparing the dynamic flexibility of the pile-group foundation measured experimentally with that calculated by the proposed algorithm, the applicability of the latter in calculating the impedance functions of pile-group foundations can be verified.  
Figure 11 shows the vertical and horizontal (in both north-south direction and east-west directions) dynamic flexibility of the pile-group foundation obtained by the proposed algorithm and field experiment. There exists interference from passing trains and large-scale mechanical operation around the test site, the low-frequency component of which is large. Meanwhile, in consideration of the limitations of the test equipment, the data within the range of 8-20 Hz are analyzed and compared.  
It can be seen that the dynamic flexibility of the pile-group foundation in three directions obtained from the proposed algorithm is in good agreement with the results from the field experiment in the frequency range of 8-20 Hz, which once again confirms the applicability of the proposed algorithm for solving the dynamic impedance of pile-group foundations.  
Figure 11: _Cont._
# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion
### Generalization  
To validate the generalizability of our model, we applied the trained model to the \"Di Ting\" artificial intelligence seismology training dataset, which was furnished by the National Earthquake Science Data Center, with a sampling rate of 50 Hz and a length of 9000 sample points. For compatibility with our model's input length, we designated waveforms from the initial 6000 points as signal samples and the subsequent 3000 points as noise samples. Following these procedures, we yielded 1500 data points with an SNR below 0 dB. After normalization, we utilized DeepDenoiser, DnRDB, and our model for denoising. Table 2 presents the denoising outcomes for each model, including the SNR and correlation coefficient improvement of the signal before and after denoising and the RMSE reduction. As indicated in Table 2, our model outperforms DeepDenoiser and DnRDB, elevating the SNR by 3.72 dB and 2.63 dB, respectively, augmenting the average correlation coefficients by 0.1492 and 0.0272, and diminishing the RMSE by 0.1346 and 0.1587, respectively. These results underscore our network's significant advancements over other methods and its robust denoising proficiency, thereby corroborating its exceptional generalizability.  
In addition, to verify the denoising performance of the models on real noise-containing signals, we selected 771 signals with SNRs below 5 dB from the STEAD dataset. However, since the corresponding clean signals for these noise-containing signals are unknown, we could not evaluate the denoising effect using the SNR, correlation coefficient (r), or RMSE. Because our goal is to improve the subsequent P-wave picking accuracy, we assessed  
Figure 9: Waveform comparison of the noise reduction results of various methods for an SNR of 10 dB: (**a**) noisy signal; (**b**) original signal; (**c**) U-Net denoising result; (**d**) DnRDB denoising result; (**e**) DeepDenoiser denoising result; and (**f**) our model’s denoising result. The red line represents the true first arrival time of the P-wave, whereas the blue line represents the result of picking up the first arrival of the denoised signals from each network using the STA/LTA algorithm [53].  
the denoising performance by comparing the P-wave picking accuracy before and after denoising, as shown in Table 3. Here, the STA/LTA algorithm is used for P-wave picking. The results show that the P-wave picking accuracy of the denoised signals obtained by U-Net, DeepDenoiser, and DnRDB did not improve, probably because these models remove the P-wave first-arrival information, as shown in Figure 10. The P-wave picking accuracy of the signal after the denoising of our model is improved by 6.75%, and there is no missed picking. This experiment demonstrates that our model effectively denoises real noise-containing signals and enhances the P-wave picking accuracy after denoising.  
Figure 10: P-wave picking results of various models after denoising real noisy signals.
## Materials and Methods
### Data and Methods
#### 2.2.4 Satellite Chlorophyll Fluorescence from GOME-2  
Chlorophyll fluorescence data were derived from GOME-2, which was launched on the MetOp-A platform on 19 October 2006. The footprint size was 80 km \(\times\) 40 km for main-channel data, and we resampled it into 50 km \(\times\) 50 km. The default swath was 1920 km, which enabled global coverage of the Earth's surface within 1.5 days. Since MCD43A4 is generated every 8 days with a period of 16 days, all the other datasets were generated to match this sampling frequency to make them comparable. We averaged the daily retrievals of SIF every 8 days with an average over the 16-day period. In our study, the retrieval was centered at 740 nm. The footprints of GOME-2 of about 0.5 degrees are known to be mismatched with that of the EC sites, of about 500 m [46]. Thus, we selected sites with nearly homogeneous cover, where the percentage of dominant land cover with the 50 km pixel centered on the EC tower accounted for 87.6% for FI-Sod (ENF), 75.3% for RU-Cok (OSH), and 73.3% for FI-Hyy (ENF).
## 3 Methodology  
The integration of intelligent machine learning-based algorithms harnesses the advantages offered by SDWSN-IoT architecture and is considered a promising solution for optimizing the network performance. By intelligently acquiring and utilizing real-time network data, these algorithms can determine optimal routes for the efficient transfer of network traffic while conserving network resources [33]. This section provides a comprehensive introduction to the unique characteristics and emerging technical issues that have guided the design of our proposed energy-efficient routing scheme for the SDWSN-IoT network architecture.  
We begin by identifying a critical concern that is often overlooked when designing multi-objective energy-efficient RL-based routing protocols and present a proposed solution. The key issues identified include the slow learning process, conflicts among multiple objectives, and the uncertainty of learned values, which can result in additional overhead during the learning process. Further details on the proposed routing scheme are provided below.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 5 Performance Evaluation
### Experimental Setup  
Algorithms Evaluated  
We test our proposed approach and compare it with three baseline methods. In particular, we evaluate and compare the following models using the aggregated November and December datasets, with number of base stations \(n_{\text{BS}}=200\) or \(n_{\text{BS}}=100\) for stress testing.  
* **Patched Estimation**: estimate the cell phone activity distribution by patched piece-wise constant estimation, that is, assume cell phone activity density is distributed uniformly within each  
Figure 1: The cell phone activity distributions of Milan. It shows the metropolitan area of Milan, Italy, and the area covered by the 2726 grid squares. (**a,b**) show the heat map of cell phone activities (Call + SMS) during November and December respectively.  
Figure 2: The location distributions of sampled base stations and the areas in charged by them. (**a,b**) shows the sampled base station distributions for \(n_{\text{BS}}=200\) and \(n_{\text{BS}}=100\); (**c**) shows the areas in charged by different base stations for \(n_{\text{BS}}=100\).  
sub-region \(\Omega_{B_{i}}\), i.e., the area covered by base station \(B_{i}\), and estimate each square's activity volume by (7).
* **Patched Estimation + SSR 1**: first estimate _only base station_ activity volumes by (7). Use these sparse points to fit a smooth surface by running Spatial Spline Regression to obtain the estimated cell phone activity in all squares.
* **Patched Estimation + SSR 2**: as opposed to the previous model, get the initial estimation of the activity volume of _all squares_ by Patched Estimation. Then, use all these points to fit a smooth surface by running Spatial Spline Regression to obtain the final estimated cell phone activity in all squares.
* **Constrained Spatial Smoothing**: first get the initial estimation of the activity volume of all squares by Patched Estimation, then run Constrained Spatial Smoothing algorithm to get the final activity volumes estimation of all squares.
* **Constrained Spatial Smoothing + Features**: in this case, we incorporate the geographical features into Constrained Spatial Smoothing algorithm.  
We set the penalty parameter \(\lambda=1\) when \(n_{\text{BS}}=200\) and \(\lambda=10\) when \(n_{\text{BS}}=100\), for all methods that utilizes SSR. The geographical features of Milan are only incorporated in the last algorithm described above. In addition, for the implementation of Spatial Spline Regression, we use the _fdaPDE_ R Package [30].  
To compare different approaches, we evaluate the performance by the Mean Relative Error (MRE) of the produced activity estimates for the true activity values. The relative error of an estimation \(\hat{f}(\mathbf{p}_{j})\) compared to the true value \(f(\mathbf{p}_{j})\) is defined as \(|\hat{f}(\mathbf{p}_{j})-f(\mathbf{p}_{j})|/f(\mathbf{p}_{j})\).
## 3 Methodologies  
In this section, we are going to give an overview of the main methods used for watermarking. There are three types of main frequency domain watermarking algorithms in the literature; discrete cosine transformation (DCT), discrete Fourier Transformation (DFT) and discrete Wavelet transformation (DWT). There are several reasons to use frequency domain in watermarking. The main reason is that the nature of the human visual system (HVS) is better acquired by the spectral coefficients. Another reason is that high frequencies demonstrate the object edges in the image which are very efficient locations to embed watermarks. Frequency domain watermarking algorithms are powerful against image processing attacks such as filtering, contrast adjustment, histogram equalization, adding noise, blurring, compression and brightness.
# High Power All-Fiber Supercontinuum System Based on Graded-Index Multimode Fibers
## 4 Conclusions  
We reported an all-fiber laser source based on GRIN fiber to generate supercontinuum. By directly splicing a 50/125 \(\upmu\)m graded index fiber to a 30/250 \(\upmu\)m fiber of a picosecond ytterbium amplifier, a spectrum with broad flatness covering the visible and near-infrared bands (480-2440 nm) was obtained with an average power and repetition frequency of 25 W and 8 MHz, respectively. The results also showed that the increase in the length of the GRIN fiber and the peak power of the pumped picosecond pulse can significantly improve the width and flatness of the supercontinuum. The beam self-cleaning effect caused by the Kerr self-cleaning effect was observed at a high level of peak power pumping. In conclusion, high power supercontinuum output can be achieved through the high-power picosecond fiber laser and GRIN fiber. In the future, the average output power can be increased by increasing the repetition rate and higher pump power.  
Conceptualization, H.Z.; methodology, H.Z. and J.Z.; software, J.Z.; validation, J.Z., X.L., H.X. and J.C.; formal analysis, J.Z. and X.L.; investigation, J.Z.; resources, H.Z.; data curation, J.Z.; writing--original draft preparation, J.Z. and X.L.; writing--review and editing, H.Z.; supervision, H.Z.; and project administration, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China No. 61475081.  
Not applicable.  
The authors declare no conflict of interest.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 2 Design of the Proposed System
### Extraction of Parameters  
Walking windows detected from the previous step were used for the extraction of gait parameters related to the motion analysis. These parameters were extracted from each walking window and could be divided into two main groups: metrics related to the quantity of walking (show in Table 1) and metrics related to the quality of walking in frequency and time domains (shown in Table 2).  
Parameters illustrated in Table 1 were directly considered for the final report on the subject's state of health. Instead, all frequency and temporal measures shown in Table 2 were computed as weighted means before inserting into the final report on the subject's state of health. They were extracted from each walking window and then averaged, assigning weights proportional to the duration of the walking bout to which they belong. Therefore, a longer bout is more significant than a shorter one and has a greater influence on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Axis** \\ \hline Total number of walks & n & AP \\ Total walking duration & \% & AP \\ Total number of steps & n & AP \\ Median walking bout duration & s & AP \\ Median number of steps for bouts & n & AP \\ Cadence & steps/minute & AP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gait parameters related to the quantity of walking.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Units** & **Axes** \\ \hline \multirow{5}{*}{Frequency measures} & Dominant frequency & Hz & V, AP, ML \\ \cline{2-3}  & Amplitude of & PSD & V, AP, ML \\ \cline{2-3}  & dominant frequency & Hz & V, AP, ML \\ \cline{2-3}  & Width of dominant frequency & Hz & V, AP, ML \\ \cline{2-3}  & Slope of dominant frequency & PSD/Hz & V, AP, ML \\ \cline{2-3}  & Harmonic ratio & \# & V, AP, ML \\ \hline \multirow{5}{*}{Temporal measures} & Step time & s & AP \\ \cline{2-3}  & Stride time & s & AP \\ \cline{2-3}  & Stance time & s & AP \\ \cline{2-3}  & Swing time & s & AP \\ \cline{2-3}  & Double support time & s & AP \\ \cline{2-3}  & Single support time & s & AP \\ \cline{2-3}  & Step time variability & s & AP \\ \cline{2-3}  & Stride time variability & s & AP \\ \cline{2-3}  & Stance time variability & s & AP \\ \cline{2-3}  & Double support time variability & s & AP \\ \cline{2-3}  & Step regularity & \# & V, AP, ML \\ \cline{2-3}  & Stride regularity & \# & V, AP, ML \\ \cline{2-final values of the parameters calculated in that window. At the end of this operation, all metrics were collected in the final report that represents a summary of the subjects' gait characteristics (in terms of quantity and quality of walking) related to motion analysis for the entire recording. As shown in Figure 5, the final report illustrates all the extracted parameters from the proposed algorithm and a histogram for the visualization of the patient status with respect to his standard status condition.