esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Interference Rejection Performance of CRPA Software Receiver  
A dataset is collected by the signal collection hardware [24] in open field (low multipath environment) to examine the interference rejection performance of the CRPA software receiver. The RF front-end of signal collection hardware is a Universal Software Radio Peripheral (USRP) [25] with a Bitshark USRP Broadband configurable RF receiver (BURX) daughter board [26]. Its electrical details are as follows. Sampling rate is set as 4 MHz. Intermediate frequency bandwidth is set as 4 MHz. Noise figure is 6.8 dB. Gain is set as 20 dB. Bits per sample at ADC output are 14 bits for both I/Q. For injecting the interference, a MATLAB script is written to generate the synthetic interferers and combine them with original dataset. The direction and signal specification of interferers can be specified in the script. Two scenarios are built. In the first scenario, a single, broadband and high J/S interferer is injected. In the second scenario, multiple broadband/narrowband with moderate J/S interferers are injected. The power spectral density of broadband and narrowband interferers is shown in Figure 12. The 3 dB-bandwidth of broadband interferer is 2 MHz and narrowband interferer is a continuous sinusoidal wave.  
Figure 11: Execution flow of the software receiver within 1 ms. Multithread programming is used to make the software capable of real-time execution.
## 4 Conclusions  
This study developed a modified ferrite treatment process incorporating H\({}_{2}\)O\({}_{2}\) preoxidation for treating high-concentration PVA wastewater. Through optimized H\({}_{2}\)O\({}_{2}\) preoxidation and iron ion addition strategies, the process effectively maintained pH stability of the reaction system and achieved efficient removal of high-concentration PVA. A two-stage treatment achieved 83% COD removal for 7 g/L PVA solution. Microscopic morphology and structural analyses revealed PVA's crucial role in material formation. Under 100 \({}^{\circ}\)C drying conditions, PVA molecular chains served as templates, guiding the formation of dispersed Fe\({}_{3}\)O\({}_{4}\) nanoparticles and fibrous network structures. When heated to 650 \({}^{\circ}\)C, the material evolved into rod-like structures and aggregates due to PVA decomposition and crystal reorganization. This structural evolution directly influenced the material's magnetic properties, with sample B2 exhibiting the highest magnetization (10.30 emu/g) at 100 \({}^{\circ}\)C, demonstrating excellent magnetic characteristics. The PVA concentration exhibited a threshold effect, transitioning from inhibiting crystallization at lower concentrations to promoting specific crystal plane growth at higher concentrations. The findings from this study provide a new technical approach for treating high-concentration organic wastewater  
Figure 10: The multistage interaction mechanism between PVA and iron oxides during the formation of magnetic nanoparticles through the H\({}_{2}\)O\({}_{2}\)-modified ferrite process.  
while opening pathways for resource utilization in wastewater treatment through the recyclability of magnetic precipitates. This strategy, combining wastewater treatment with functional material preparation, holds significant scientific and practical value for developing environmentally friendly wastewater treatment technologies.  
Conceptualization, V.K.S.H.; methodology, Y.-C.F.; formal analysis, Y.-C.F.; investigation, Y.-C.F. and V.K.S.H.; resources, V.K.S.H.; data curation, Y.-C.F.; writing--original draft preparation, V.K.S.H.; writing--review and editing, V.K.S.H.; supervision, V.K.S.H.; funding acquisition, V.K.S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council, Taiwan, grant number NSTC 113-2221-E-260-003-MY2.  
The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Thorium Recovery with Crown Ether-Polymer Composite Membranes
## 2 Materials and Methods
### Procedures and Methods
#### 2.2.3 Nanofiltration Experiments  
Nanofiltration of the thorium nitrate solution was carried out (Figure 2) at a pressure of 5 to 9 atmospheres through the composite membrane (ABCE-sPEEK) and for comparison through the sPEEK membrane. The module of nanofiltration, of a dead-end filtration type, is equipped with a propeller stirrer that performs a turbulent agitation of the feed solution. The feed solutions have a concentration of \(10^{-4}\) mol/L and \(10^{-5}\) mol/L thorium nitrate in ultrapure water also containing nitric acid. The pH variation of these solutions is conducted with nitric acid solutions of 1 mol/L, \(10^{-1}\) mol/L, \(10^{-2}\) mol/L, \(10^{-3}\) mol/L, and \(10^{-4}\) mol/L. The surface of the membrane in the nanofiltration module is 100 \(\pm\) 0.3 cm\({}^{2}\)  
Figure 1: Preparation of sPEEK membranes from PEEK solution in sulfuric acid [46].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Organic Compounds** & **Name and Symbol** & **Solubility** & **pKa** \\ \hline H2N & 4’–Amino–Benzo–15–Crown–5 Ether (ABCE) & organic polar solvents & 4.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The characteristics of the components of the composite membrane preparation.  
The capacity of the module is 2000 cm\({}^{3}\). The propeller stirrer has a diameter of 7 cm and can be rotated from 50 rpm to 250 rpm.  
The result of the nanofiltration was presented in the form of the permeate flux (J) (Equation (1)) or of the nanofiltration retention [50] (R) (Equation (2)):  
\[\mathrm{J}=\frac{\mathrm{V}}{\mathrm{S}\cdot\mathrm{\Delta t}}\Big{(}\mathrm{L}/ \mathrm{m}^{2}\mathrm{h}\Big{)} \tag{1}\]  
where: J--permeate flux; V--permeate volume; S--surface of the membrane; \(\mathrm{\Delta t}\)--operating interval;  
\[\mathrm{R}(\%)=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{f}})}{\mathrm{C}_{0 }}\times 100 \tag{2}\]  
where: R--retention (%); C\({}_{0}\)--concentration of feed solution; C\({}_{\mathrm{f}}\)--final concentration.  
The concentration of the thorium ion was determined by the Thorin spectrophotometric method. The specific determinations are carried out through coupled extraction separation and spectrophotometric detection at \(\lambda\) = 495 nm. The detection limit of the method is 0.04 \(\mathrm{\mu}\)g Th (IV) mL\({}^{-1}\), and the RSD (\(n\) = 10) is 1.4% [51]. The obtained results have a total precision of 0.1 ppm imposed both by the traceability of the samples taken and by the spectrophotometric methods approached.  
When establishing the accuracy of 0.1 ppm, the following factors were taken into account: the measurement deviation of the sample volume taken from nanofiltration, the pH adjustment deviation of the permeate solution, the deviation from the selected wavelength compared to that of the absorption maximum, the temperature variation during the determinations, the validation on two different spectrometric devices, the independent work of two analysts, and the reduced number of repeated determinations (five).
## 4 Experimental Results and Discussion
### Network Lifetime  
The proposed CERP's network lifetime is compared with the conventional algorithms in Figure 6. This method attained a higher network lifetime than the remaining methods. Table 4 shows the network lifespan and energy consumption of the proposed E-CERP and conventional approaches.  
Figure 5: Energy consumption comparison.  
Figure 6: Comparison of the network lifetime.  
From the simulation results, it is evident that the network lifetime is extended by the proposed method for 100 nodes (5908 rounds). In contrast, the lifetimes of an existing cross-layer-based adaptive threshold technique and cross-layer fuzzy logic approaches are 5395 and 4904 rounds, respectively. Moreover, as the number of nodes increases, the network lifetime decreases. Hence, data transmission efficiency is based on the increased network lifetime.
## 3 Numerical Examples
### Inversion Effects in the Ideal Case
Figure 8a,b shows the P- and S-wave velocity models obtained by performing elastic full-waveform inversion using source wavelets obtained by the L2 EWI method, respectively. Figure 8c,d shows the P- and S-wave velocity models obtained by performing elastic full-waveform inversion using the source wavelets obtained by the CNEWI method, respectively. Since the cross-correlation objective function is used in the elastic full-waveform inversion process, the accuracy of the phase information is very important for the inversion. Both inverted source wavelet results contain good phase information. The elastic full-waveform inversion results of the two methods in Figure 8 are very close. It shows that under ideal circumstances, both  
Figure 5: Source wavelet inversion results in an ideal case. Figure 6: Direct wave comparison in an ideal case. (**a**) Z-component observed direct waves; **(b)** Z-component synthetic direct waves using an L2 EWI-estimated source wavelet; **(c)** Z-component synthetic direct waves using a CNEWI-estimated source wavelet. Figure 7: Objective function curve comparison in an ideal case. (**a**) Objective function curves of the L2 EWI process; **(b)** objective function curves of the CNEWI process.
## 4 Discussion
Initially, the integration of advanced camera modules, such as those with higher resolution or global-shutter sensors, along with high-precision lasers, is expected to minimize noise and enhance scanning accuracy and precision. Second, the automation or semi-automation of the tuningof numerous intrinsic and extrinsic parameters will help ensure consistent performance across diverse scanning conditions. Finally, further experimentation is necessary to validate the method in real industrial or clinical environments, where challenges such as specular reflections, limited visibility, or temperature fluctuations may significantly affect data quality.
## 4 Experimental Results and Analysis
### Extended Security Analysis and Formal Verification  
#### 4.3.1 Potential Vulnerabilities and Security Considerations  
While the proposed ECC-based authentication scheme provides enhanced security for IoT networks, it is essential to analyze its resilience against potential attack vectors. Below, we examine key vulnerabilities and the corresponding security mechanisms implemented to mitigate them:  
* Replay Attacks: The scheme prevents replay attacks by incorporating unique session keys for each authentication exchange. Additionally, timestamps and random nonces are utilized to ensure that previous communications cannot be reused by an adversary.
* Man-in-the-Middle (MitM) Attacks: The use of authenticated key exchange ensures that only legitimate devices can participate in secure communication, preventing unauthorized interception.
* Key Compromise Impersonation: Since the security of ECC is based on the hardness of solving the Elliptic Curve Discrete Logarithm Problem (ECDLP), compromising a single key does not enable an attacker to impersonate another entity in the system.
* Side-Channel Attacks: The proposed model can be strengthened by implementing countermeasures such as constant-time cryptographic computations, randomization techniques, and hardware-level protections like tamper-resistant modules.
* Desynchronization Attacks: Key synchronization is a significant challenge in IoT environments. The model employs periodic key updates and a key propagation mechanism to ensure synchronization across heterogeneous devices while preventing unauthorized access due to outdated keys.  
The resilience of the proposed scheme is further supported by its ability to adapt to the computational constraints of IoT devices, ensuring robust protection without imposing excessive processing overhead.  
\begin{table}
\begin{tabular}{l l} \hline \hline Attack Type & Resilience Provided by proposed ECC based Model \\ \hline Replay Attack & Unique session keys for each communication \\ \hline Man-in-the-Middle Attack & Authenticated key exchange prevents interception \\ \hline Key Compromise Impersonation & Keys are derived from secure ECDLP calculations \\ \hline Side-Channel Attack & potential use of tamper-resistant hardware \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative evaluation with NIST general algorithms.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 4 Backtest Analysis
#### Construction of Stock Selection Strategy Based on Clustering Results  
The stock selection strategy revolved around a hypothesis: stocks within the same cluster have similar price movements and achieve similar returns over a given period of time. During the observation period, we first selected stocks that were above or below average by setting different thresholds. During the testing period, we bought stocks based on those above average and sold stocks based on those below average, as shown in Figure 9.  
By selecting the stocks we needed to buy and sell in the testing period, we learned that in most cases, the stocks selected had higher returns than the market average returns, for example, in the S&P 500 index market shown in Table 5, in the cases where the thresholds were set to 0.02, 0.04, 0.06, 0.08, 0.1, the stocks to buy revealed higher returns than the market average returns. The strategy outperformed the market average by 0.77% when the threshold was set to 0.1. Similarly, in the CSI 300 index market shown in Table 6, the return reached a maximum of 2.41% (annual return can reach 28.89%) when the threshold was set to 0.02. Of course, there is some uncertainty in the market, for example, in the S&P 500 index market when the threshold was set to 0.02, the return to buy was lower than the average market return, and this was also reflected in the CSI market.  
From the above results, it can be verified that TCN-Deep Clustering can be combined with a stock selection strategy based on historical price performance to obtain stocks that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CSI 300**} & \multicolumn{6}{c}{**Different Thresholds**} \\ \cline{2-6}  & **Th = 0.02** & **0.04** & **0.06** & **0.08** & **0.1** \\ \hline To buy & 0.9455 & 0.9192 & 0.9253 & 0.9290 & 0.9476 \\ To sell & 0.9135 & 0.9454 & 0.9256 & 0.9104 & 0.9245 \\ Average & 0.9214 & 0.9214 & 0.9214 & 0.9214 & 0.9214 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Returns in CSI 300.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**S\&P 500**} & \multicolumn{6}{c}{**Different Thresholds**} \\ \cline{2-6}  & **Th = 0.02** & **0.04** & **0.06** & **0.08** & **0.1** \\ \hline To buy & 1.0113 & 1.0068 & 1.0081 & 1.0077 & 1.0125 \\ To sell & 1.0070 & 1.0025 & 1.0066 & 0.9899 & 0.9866 \\ Average & 1.0048 & 1.0048 & 1.0048 & 1.0048 & 1.0048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Returns in S&P 500.  
Figure 9: Visual explanation of buying and selling.  
outperform the average returns. Specifically, investors can select a period in the past for clustering analysis and choose the corresponding threshold to invest according to their investment risk tolerance, at which time the corresponding buying pool and selling pool will appear, corresponding to stocks in the buying pool that can be bought or added to and stocks in the selling pool that can be sold or reduced.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 4 Discussion  
This review identified a broad range of CRISPR-based strategies that have been explored for targeting herpesvirus infections, highlighting their potential for future therapeutic applications. However, notable discrepancies in antiviral efficacy across studies targeting the same virus were observed. These inconsistencies may be attributed to differences in targeted genes, gRNA design, CRISPR systems, delivery vectors, host models, viral strains, methodologies for assessing antiviral activity, or experimental conditions. These different aspects will be discussed below with a focus on the balance between treatment efficiency and safety. Notably, only two of the eight human herpesviruses--HSV-2 and HHV-7--have not yet been targeted using CRISPR-based approaches, likely due to limited research focus or perceived lower clinical relevance.
## 3 Results and Discussions
### Classification Result of Decision Tree
#### 3.3.2 Accuracy Assessment  
Table 6 shows the classification accuracy of the common objects in spring/summer (images from May /June 2021) of the study area computed by the confusion matrix. The overall accuracy is 83.15% with a kappa coefficient of 0.803. In addition, we also found that the OA is 82.56% with a kappa coefficient of 0.816 in the season of autumn/winter.  
From the accuracy evaluation in Table 6, it is concluded that water has obtained the highest classification accuracy of both PA (92.47%) and UA (94.50%). Vegetation and metal rooftop observed good PA at 96.74% and 93.41%, respectively. The OA, however, declined to 89.90% and 93.33% for vegetation and metal rooftop, respectively. The bare soil observed the smallest classification accuracy both of PA (76.68%) and OA (73.63%), which is often  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Referenced Category**} & \multicolumn{6}{c}{**Classified as**} \\ \cline{2-10}  & **Vegetation** & **Plastic Film** & **Water** & **Bare Soil** & **Desert** & **Metal** & **Cement** & **Total** & **PA (\%)** \\ \hline Vegetation & 89 & 0 & 0 & 3 & 0 & 0 & 0 & 92 & 96.74 \\ Plastic Film & 1 & 147 & 0 & 13 & 0 & 11 & 9 & 181 & 81.21 \\ Water & 1 & 0 & 86 & 0 & 0 & 6 & 0 & 93 & 92.47 \\ Bare Soil & 8 & 13 & 0 & 148 & 12 & 0 & 12 & 193 & 76.68 \\ Desert & 0 & 8 & 0 & 18 & 96 & 0 & 1 & 123 & 78.05 \\ Metal & 0 & 0 & 5 & 1 & 0 & 85 & 0 & 91 & 93.41 \\ Rootrop & & & & & & & & & \\ Total & 99 & 176 & 91 & 201 & 113 & 102 & 126 & 908 & - \\ UA (\%) & 89.90 & 83.52 & 94.50 & 73.63 & 84.96 & 83.33 & 82.54 & - & 83.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of classification results of the study area, based on CART.  
incorrectly classified as the desert or the plastic film, because their spectral reflectance are comparable (see Figure 4).
## 3 Resonant Converter Families
### Classification of Resonant Converter
#### 3.2.3 Series-Parallel Resonant Converters  
Series-parallel resonant converters (_SPRCs_) combine the advantages of the _SRC_ and _PRC_. The _SPRC_ has an additional capacitor or inductor linked in the resonant tank circuit [23]. Figure 20a shows an _LCC_-type _SPRC_, in which an additional capacitor is placed in series with the resonant inductor. _LCC_ offers both load-independent output voltage and output current. This topology is dominated by parallel resonant frequency, and subsequently it presents the same shortcomings as the _PRC_. _LCC_ cannot operate safely with an open circuit or a short circuit. Figure 20b indicates an _LLC_-type _SPRC_, in which an additional inductor is connected in parallel with the resonant capacitor in the _SRC_. _LLC_ has the ability to achieve no load regulation that was not possible in the _SRC_ by employing an inductor in parallel to the resonant capacitor. Through such modification, the _LLC_ topology allows the regulation of the output voltage from zero to maximum under any load condition with relatively small switching frequency variation. _LLC_ also has some restrictions, for instance, startup and short-circuit protection are difficult to achieve due to a flat gain above the series resonant frequency \(f_{rs}\). Then, as with _LCC_, the _LLC_ topology cannot operate safely with an open circuit at frequencies close to \(f_{rs}\) and cannot operate safely with a short circuit at frequencies close to parallel frequency \(f_{rp}\)[80; 108]. However, there are many possible combinations of the resonant tank circuit. Detailed analysis can be found in [109]. In this context, the type of _LLC_ was discussed as one of the types of this technology, as shown in the equivalent circuit in Figure 27. The _LLC_ equivalent circuit of Figure 27 has a capacitor \(C_{r}\) connected in series with the inductor \(L_{r}\), and the load is connected in parallel to the inductor \(L_{p}\). _VAB_ is the fundamental component of a square waveform _Vin_ that drives the switches of the _CSN_. The inductor \(L_{p}\) can be replaced by the magnetizing inductance if a transformer is used in the circuit. The topology has two resonant frequencies: the series resonant frequency \(f_{rs}\) due to the resonant element _LsCs_  
Figure 26: Equivalent circuit of the _DC-DC_ parallel resonant converter [80].  
and given by Equation (21), and the parallel resonant frequency \(f_{rp}\) due to all the \(R\)T\(N\) elements and given by Equation (22). Note that \(f_{rs}>f_{rp}\) and \(f_{rp}=0\).  
\[f_{rs}=\frac{1}{2\pi\sqrt{L_{r}C_{r}}} \tag{21}\]  
\[f_{rp}=\frac{1}{2\pi\sqrt{(L_{r}+L_{p})C_{r}}} \tag{22}\]
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods
#### 2.1.2 The Wavelet Transform Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[51] & Fault feature & Reactor coolant & Vibration &
\begin{tabular}{c} Rotor crack faults \\ \end{tabular} \\
[52] & \begin{tabular}{c} Feature \\ extraction \\ \end{tabular} & \begin{tabular}{c} Hydraulic pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Loose slipper fault \\ \end{tabular} \\
[53] & \begin{tabular}{c} Feature \\ extraction and fault \\ diagnosis \\ \end{tabular} & \begin{tabular}{c} Monoblock \\ centrifugal pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Bearing fault, impeller defect, bearing, \\ and impeller defect together \\ and cavitation \\ \end{tabular} \\
[55] & \begin{tabular}{c} Fault \\ diagnosis \\ \end{tabular} & Hydraulic pump & \
\begin{tabular}{c} Vibration, pressure, \\ and sound signal \\ \end{tabular} &
\begin{tabular}{c} Swash plate wear, loose slipper, slipper \\ wear, and central spring wear \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pump condition monitoring using wavelet transform.
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment
### Statistical Analysis  
As can be seen in (b) of Figure 6, the proposed method outperforms the comparison method in all three metrics. The proposed method is outstanding due to the fact that this paper uses a median filter, which is more accurate than DCP at nighttime. The more accurate the atmospheric light value is, the higher the quality of the processed image. Therefore, our method is higher than DCP in terms of PSNR and UQI. SSIM indicates the extent to which information is retained in the processed image. As this paper uses the light source influence matrix to participate in modifying the transmittance near the light source, it can better retain the image information.  
Furthermore, a non-parametric test was used to verify the differences between the methods in this paper and others. As the samples did not conform to a normal distribution and were independent of each other, the Wilcoxon Signed-Rank Test was used to analyze the samples. We calculate the difference between the comparison method and the proposed method (Z-value in Table 4) and the corresponding \(p\)-value. As shown in Table 4, the \(p\)-value is all less than 0.05, indicating that the proposed method is significantly different from the comparison method in all indicators. According to Table 3, the proposed method is higher than the comparison method in terms of mean and median.  
Figure 5: The objective experimental results. The defogging effect can be clearly seen at the red box (1) Nighttime hazy image. (2) DCP [14]. (3) NDIM [25]. (4) OSFD [35]. (5) OURS.