esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study proposes a time-series recovery method using adjacent multi-channel data based on an LSTM neural network. In the case of collecting multi-channel data, the adjacent channel data is used to recover the lost time-series data. Based on the measured data of a subway, a multi-channel data recovery model is established, and the accuracy of the training data is guaranteed by optimizing the parameters of the neural network. This study uses time-domain and frequency-domain data to train the LSTM neural network. The results  
Figure 24: Comparison of acceleration of the one-third octave spectrum from Channel 7 using processed data.  
Figure 25: Relative error of the one-third octave spectrum from Channel 7 using processed data.  
show that the network trained with time-domain data is 95% accurate for time-series data recovery, while the network trained with frequency-domain data is as high as 98% accurate. Time-domain data can better reflect the internal relationship between adjacent channels so that the trained neural network has higher accuracy. This data recovery method is generally feasible and provides a reference value for further exploration of multi-channel time-series data recovery using machine learning. Currently, this method's internal connection of multi-channel vibration signals is data-driven. Therefore, it is necessary to further study the internal relationship of data among channels and optimize the model's algorithm by adding physical information to the neural network. On this basis, weak mechanism modeling theory is used to derive the laws and summarize weak mechanisms. There are two ways to obtain weak mechanisms: first, they can be obtained through derivation from a stronger mechanism, which may be more useful than mathematical analysis, and second, they can be obtained through summarization of the data mainly by means of statistics and machine learning. Then, some weak mechanisms are obtained. Next, proceeding from one or more weak mechanisms, a computable model can be established through a specific combination/fusion, which helps to understand the inherent connection of the data learned by the neural network and further expands the interpretability of the neural network.  
Conceptualization, T.X. and X.Z.; methodology, T.X. and Y.Y.; software, T.X. and Y.Y.; data curation, T.X. and S.W.; writing--original draft preparation, Y.Y. and P.W.; writing--review and editing, T.X. and J.L. All authors have read and agreed to the published version of the manuscript.  
This study is sponsored by the Beijing Nova Program (Z191100001119126), the Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety (R202101), and the Fundamental Research Funds for the Central Universities (2020)BM049). The support from the 111 Project (B20040) is also greatly appreciated.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Results and Discussion
### Computational Complexity  
The complexity of online deployment of the dual-driven MIMO detector is related to two aspects: (i) \(L_{\text{d}}\) fully connected layers in the symbol correction network with a computational complexity of \(\mathcal{O}\Big{(}T\sum_{l=1}^{L_{\text{d}}}(m_{l-1}m_{l})\Big{)}\) and (ii) OAMP-Net with a computational complexity of \(\mathcal{O}(TN_{\text{t}}^{3})\)[25]. \(m_{l-1}\) and \(m_{l}\) are the input and output sizes in the \(l\)th layer, respectively. According to Table 2, the computational complexity of the symbol correction network is \(\mathcal{O}(16T(L_{\text{d}}-1)N_{\text{t}}^{2}+TN_{\text{t}}^{3})\). \(N_{\text{t}}\) can range from hundreds to thousands in the massive access case or the ultra MIMO case. Since \(L_{\text{d}}\) is a constant when the dual-driven network is stable, the overall complexity of the dual-driven MIMO detector is \(\mathcal{O}(TN_{\text{t}}^{3})\), which is same as OAMP-Net.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 5 Performance Evaluation
### Simulation Results
#### 5.2.4 Comparison of RA Success Rate of the Proposed CeRA-eSP and Legacy CeRA Methods  
According to the previous performance analysis results, it can be observed that the legacy CeRA method was significantly affected by PUSCH resources. Because a specific PUSCH resource could have had a significant impact on the legacy CeRA and the proposed CeRA-eSP methods, additional simulations were performed to analyze the RA success rates according to the PUSCH resources. In Figure 10, it can be observed that the RA success rate increased linearly according to the number of resources for both the legacy CeRA and the proposed CeRA-eSP methods. The proposed CeRA-eSP method achieved a higher RA success rate than the legacy CeRA scheme for all the PUSCH resources considered. This is because the proposed CeRA-eSP method utilized the PUSCH waiting message to increase the utilization of the PUSCH resources. Because the utilization of PUSCH resources was high, it can be observed that the performance of the proposed CeRA-eSP method was higher even with the operation of additional PUSCH resources.
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 5 Experimental Section
### Parameters Experiments
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Data** & **0.009** & **0.007** & **0.005** & **0.003** & **0.009** & **0.007** & **0.005** & **0.003** \\ \hline F1-Score & 0.9937 \(\pm\) 0.0011 & 0.9934 \(\pm\) 0.0013 & **0.9940 \(\pm\) 0.0008** & 0.9935 \(\pm\) **0.0008** & 0.9939 \(\pm\) **0.0007** & **0.9942 \(\pm\) 0.0010** & **0.9942 \(\pm\) 0.0007** & 0.9941 \(\pm\) 0.0010 \\ Accuracy & 0.9937 \(\pm\) 0.0011 & 0.9934 \(\pm\) 0.0013 & **0.9940 \(\pm\) 0.0008** & 0.9935 \(\pm\) **0.0008** & 0.9939 \(\pm\) **0.0007** & **0.9942 \(\pm\) 0.0010** & **0.9942 \(\pm\) 0.0007** & 0.9941 \(\pm\) 0.0010 \\ AUC & **0.9833**\(\pm\) 0.0037 & 0.9822 \(\pm\) 0.0036 & 0.9830 \(\pm\) 0.0093 & 0.9816 \(\pm\) **0.0032** & 0.9812 \(\pm\) 0.0025 & 0.9836 \(\pm\) **0.0021** & 0.9826 \(\pm\) 0.0025 & **0.9837 \(\pm\) 0.0028** \\ G-mean & **0.9838**\(\pm\) 0.0037 & 0.9821 \(\pm\) 0.0037 & 0.9829 \(\pm\) 0.0040 & 0.9815 \(\pm\) **0.0032** & 0.9810 \(\pm\) 0.0025 & 0.9835 \(\pm\) **0.0021** & 0.9825 \(\pm\) 0.0025 & **0.9836 \(\pm\) 0.0028** \\ MCC & 0.9646 \(\pm\) 0.0002 & 0.9624 \(\pm\) 0.0073 & **0.9658**\(\pm\) 0.0016 & 0.9632 \(\pm\) **0.0044** & 0.9655 \(\pm\) **0.0039** & 0.9672 \(\pm\) 0.0058 & **0.9673**\(\pm\) 0.0041 & 0.9665 \(\pm\) 0.0054 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of FMFED for different \(\theta\). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Data** & **1.5** & **2.0** & **2.5** & **3.0** & **1.5** & **2.0** & **2.5** & **3.0** \\ \hline F1-Score & 0.9938 \(\pm\) 0.0012 & **0.9942 \(\pm\) 0.0009** & 0.9941 \(\pm\) 0.0009 & 0.9941 \(\pm\) **0.0008** & **0.9941**\(\pm\) 0.0008** & **0.9939**\(\pm\) 0.0010 & **0.9941**\(\pm\) **0.0007** & **0.9941**\(\pm\) 0.0008 \\ Accuracy & 0.9943 \(\pm\) **0.0008** & 0.9942 \(\pm\) 0.0009 & 0.9941 \(\pm\) 0.0009 & 0.9941 \(\pm\) **0.0008** & **0.9941**\(\pm\) 0.0008** & 0.9939 \(\pm\) 0.0010 & **0.9941**\(\pm\) **0.0007** & **0.9941**\(\pm\) 0.0008 \\ AUC & **0.9905**\(\pm\) **0.0035** & 0.9803 \(\pm\) 0.0036 & 0.9794 \(\pm\) **0.0035** & 0.9793 \(\pm\) 0.0037 & 0.9791 \(\pm\) 0.0035 & 0.9785 \(\pm\) 0.0042 & 0.9792 \(\pm\) **0.0032** & **0.9793**\(\pm\) 0.0034 \\ G-mean & **0.9803**\(\pm\) 0.0036 & 0.9820 \(\pm\) 0.0037 & 0.9793 \(\pm\) **0.0035** & 0.9791 \(\pm\) 0.0038 & 0.9798 \(\pm\) 0.0033 & 0.9790 \(\pm\) 0.0032 & 0.9791 \(\pm\) 0.0035 \\ MCC & **0.9675**\(\pm\) 0.0038 & 0.9670 \(\pm\) 0.0050 & 0.9660 \(\pm\) **0.0051** & 0.9660 \(\pm\) **0.0046** & **0.9653**\(\pm\) 0.0049 & 0.9650 \(\pm\) 0.0059 & 0.9662 \(\pm\) **0.0042** & **0.9663**\(\pm\) 0.0046 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of FMFED for different \(\tau_{1}\)
###### Abstract  
Tannic acid (TA) is a major pollutant present in the wastewater generated from vegetable tanneries process and food processing. This work studied TA degradation by two advanced oxidation processes (APOs): UV irradiation at the wavelength of 254 nm in the presence of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and ferrous iron (photo-Fenton) and in the presence of potassium persulfate. The influence of certain experimental parameters such as K\({}_{2}\)S\({}_{2}\)O\({}_{8}\), H\({}_{2}\)O\({}_{2}\), Fe\({}^{2+}\), and TA concentrations, initial pH and temperature was evaluated in order to obtain the highest efficiency in terms of aromatics (decay in UV absorbance at 276 nm) and TOC removals. Chemical oxidation of TA (0.1 mM) by UV/persulfate achieved 96.32% of aromatics removal and 54.41% of TOC removal under optimized conditions of pH = 9 and 53.10 mM of K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) after 60 min. The treatment of TA by photo-Fenton process successfully led to almost complete aromatics removal (99.32%) and high TOC removal (94.27%) from aqueous solutions containing 0.1 mM of TA at natural pH = 3 using 29.4 mM of H\({}_{2}\)O\({}_{2}\) and 0.18 mM of Fe\({}^{2+}\) at 25 \({}^{\circ}\)C after 120 min. More efficient degradation of TA by photo-Fenton process than UV/persulfate was obtained, which confirms that hydroxyl radicals are more powerful oxidants than sulfate radicals. The complete removal of organic pollution from natural waters can be accomplished by direct chemical oxidation via hydroxyl radicals generated from photocatalytic decomposition of H\({}_{2}\)O\({}_{2}\).**  
Tannic acid; UV/persulfate; photo-fenton; sulfate radicals; hydroxyl radicals; degradation; mineralization
## 2 Methods
### Statistical Analysis  
Two main performance metrics were used to evaluate the LLM outputs: (1) accuracy, defined as the proportion of correctly answered items, and (2) response time, measured as the average number of seconds taken per item. To explore the impact of item features on model performance, a series of inferential statistical tests was conducted. Logistic regression models were used to assess whether item difficulty (\(\beta\)) and DI significantly predicted the likelihood of a correct model response [20]. Additionally, Pearson's correlation coefficients were computed to quantify the linear relationship between item difficulty and model accuracy. Group-level comparisons of correct versus incorrect model responses were analyzed using the Mann-Whitney U test, while chi-square tests assessed associations between categorical levels of item difficulty/discrimination and accuracy, with Cramer's V reported as a measure of effect size. To examine the influence of sampling variability, the accuracy rates at T = 0 and T = 1 were compared using Mann-Whitney U tests.  
To assess the cognitive alignment between human and artificial intelligence systems, Pearson's correlation analysis was conducted on the accuracy rates of humans and LLMs across the 15 anesthesiology knowledge domains. This analysis aimed to determine whether humans and LLMs show similar difficulty patterns across clinical domains or reveal fundamentally different cognitive architectures in medical reasoning.  
For all statistical tests, a two-tailed \(p\)-value \(<\) 0.05 was considered significant, and the False Discovery Rate (FDR) correction was applied to control for multiple comparisons. Where appropriate, [PERSON]'s d and other effect size estimates were reported to aid in interpretation.  
The sample size of 134 human participants was considered appropriate for Rasch modeling and item calibration. Prior psychometric literature indicates that sample sizes between 100 and 200 are adequate for 1 PL models when working with instruments containing 30-100 items, particularly under unidimensional assumptions and when sufficient item variability is present. Moreover, Rasch models are known for their statistical stability and low bias in small-to-moderate samples due to their strong model constraints [20; 21; 28]. While larger samples would enable finer subgroup analysis or exploration of multidimensional constructs, the present study--designed as a benchmarking analysis--offers robust estimates for the intended comparisons.  
All statistical analyses were conducted using Python (v3.9) and R (v4.3.2), with Rasch-specific modeling performed in eRm, and general statistical analyses conducted using base R and the stats models and scipy libraries in Python.
## 2 Related Works
### ML Approaches  
K-nearest neighbor (KNN) is a supervised ML algorithm used in predictive and forecasting models [30]. The accuracy of the KNN algorithm is considerably good [31]. [PERSON] et al. [32] proposed another KNN-based automated skin cancer diagnostic system. Their proposed system employed a median filter to remove the image noise using a collection of statistical and textural features. Textural features were extracted from a curvelet domain, whereas statistical features were extracted from lesion images. Furthermore, the proposed framework classified the input images into noncancerous or cancerous. The KNN model requires a long time to perform the output predictions, and it is unsuitable for a big dataset. Moreover, the KNN algorithm operates worst with improper feature information of high dimensional input data, making the algorithm unsuitable for the skin lesion classification [33].  
[PERSON] et al. [34] applied SVM to discover eczema. The approach in [35] manipulates several steps: image segmentation, feature determination applying texture-based data, and finally deciding the type of eczema with SVM. [PERSON] et al. [36] extracted orientation histogram, gradient, and location of skin lesion features. These features were fused and classified as malignant or benign using an SVM algorithm. The SVM algorithm is unsuitable for managing the noisy input image [37]. If the number of training samples is less than the number of feature vector parameters, SVM gives a lower performance.  
The Bayesian algorithm is another approach used in skin lesion classification with multiple trained skin image databases [38]. Performing the Naive Bayes algorithm for multiobjective areas is not easy [39]. The decision tree [40] model has been widely used for skin lesion classification, forecast of under limbs lesions, and cervical disease. [PERSON] et al. [41] presented intelligence techniques, decision tree, and Naive Bayes to diagnose malignant melanoma. The extracted features are based on principle component analysis and hybrid discrete wavelet transform. These features become the input to different classification methods, such as decision tree and Naive Bayes, for classifying the lesions as benign or malignant. The decision tree algorithm demands big training data for achieving considerable accuracy. Moreover, the decision tree model requires a large amount of memory and more computational time [42].
## 5 Conclusions  
This paper proposes a learning-based model predictive control energy management strategy for legged robots powered by a battery-supercapacitor hybrid energy storage system. A dual-layer LSTM network is constructed to predict the load power and improve the prediction accuracy under various operation conditions. Taking the predicted load power as the measurable disturbance, a model predictive control-based energy management strategy is proposed with deep learning-based weighting factor adjustment. In the multi-objective optimization function of the energy management strategy, the battery capacity loss, battery power fluctuation, and supercapacitor SoC maintenance are normalized and optimized simultaneously. The trained deep neural network adjusts the corresponding weighting factors under general power, high power, and high feedback power demands.  
Hardware-in-the-loop experiments show that the proposed learning-based model predictive control energy management strategy helps to improve the kinematic performance of the legged robot (by maintaining the supercapacitor SoC at a relatively high level) and is also beneficial in reducing battery capacity loss. The actual output power of the hybrid energy storage system can track the power demand more closely than the conventional model predictive control method. Meanwhile, the battery capacity loss can be reduced by 12.7%, yielding results closer to offline dynamic programming.  
Future research will focus on the quantitative assessment of kinematic performance tracking, i.e., the dynamic optimal control of the power supply/demand, not only the power allocation. An application extension to quadrupedal robots, e.g., four-legged robots/robot dogs, will also be investigated.  
Conceptualization, Z.H., Y.W. and H.L.; Methodology, B.S.; Software, B.S.; Validation, W.R.; Investigation, W.R.; Writing--original draft, B.S.; Writing--review & editing, Y.W. and H.L.; Supervision, Z.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**EMS** & **MPC** & **Rule-Based** & **Proposed** & **DP** \\ \hline Battery capacity loss (\%) & 2.3124 \(\times\) 10\({}^{-7}\) & 2.2842 \(\times\) 10\({}^{-7}\) & 2.0183 \(\times\) 10\({}^{-7}\) & 1.8901 \(\times\) 10\({}^{-7}\) \\ Improvement & benchmark & 1.22\% & 12.72\% & 18.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Battery capacity loss comparisons of conventional MPC method, rule-based method, DP, and the proposed energy management strategy.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Special thanks to [PERSON] from CRRC ZhuZhou Institute Co., Ltd. for offering help with the software.  
The authors declare no conflicts of interest.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Rotating Disk Experiments  
A rotating disk experimental setup was built, providing a range of velocities at all possible beam-to-flow angles, or, a range of inter-frame displacements at full 360\({}^{\circ}\) range of directions. This type of setup has been used to study the performance of velocity estimation methods [29; 43].  
A cylindrical homogeneous phantom was created using a 15% polyvinyl alcohol (PVA) solution [39]. The solution was poured into a cylindrical mold (Oinner = 20 mm), containing a fixating-structure (see Figure 2) to attach the phantom onto the motor unit during the experiments, and subjected to three freeze-thaw cycles. Shrinkage of the phantom was present during assemblage, resulting in a final diameter of 18.2 mm. The phantom was attached to a motor unit (Closed Loop Step Motor, ARM69 AC, Oriental Motor USA Corp., Torrance, CA, USA) and placed in a water tank (see Figure 2). It was rotated at a constant angular velocity, resulting in a maximum velocity at the outer edge of 18.2 cm/s. The phantom was positioned perpendicular to the transducer's scan plane at a depth of 3.4 cm. Plane wave data were acquired continuously using a PRF of 8 kHz for 4 s.  
Two different approaches were used for processing the acquired data. One approach resembling flow velocity estimation and the second approach analogous to the processing performed for the vessel wall displacement simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulations** & **PRF** & **IF Displacement Range (\(\upmu\)m)** \\ \hline Blood flow & 15 kHz & 0–100 \\  & 2.5 kHz & 0–600 \\ \hline Vessel wall & 25 Hz & 0.5–11.5 \\  & 50 Hz & 2.5–72 \\ \hline
**Rotating Disk Experiments** & **PRF** & **IF Displacement Range (\(\upmu\)m)** \\ \hline  & 8 kHz & 0–23 \\ Blood flow scenario & 4 kHz & 0–46 \\  & 2 kHz & 0–91 \\  & 1 kHz & 0–182 \\ \hline Vessel wall scenario & 8 kHz & 0–23 \\  & 4 kHz & 0–46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Imaging pulse repetition frequency (PRF) and resulting inter-frame (IF) displacements.
## 3 Results and Discussion
### Effect of Independent Variables on Viscosity and In Vitro Release Studies
Figure 5: Surface response plot and contour plot showing the influence of three independent variables (concentration of paraffin oil, Tween 80 and sodium carboxymethyl cellulose) on viscosity of insulin loaded niosome emulgel. Figure 6: Surface response plot and contour plot showing the influence of three independent variables (concentration of paraffin oil, Tween 80 and sodium carboxymethyl cellulose) on in vitro release of insulin from niosome emulgel. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Responses**} \\ \cline{2-7}
**Check Point** & \multicolumn{2}{c}{**In Vitro Release (\%)**} & \multicolumn{2}{c}{**Viscosity (cp)**} & \multicolumn{2}{c}{**Cumulative Amount**} \\
**Batch** & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Observed**} & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Observed**} & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Observed**} \\ \hline F9 & 26.79 & 27.38 & 18,397 & 18,529 & 82.85 & 83.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the observed value with predicted value of check point batch. The normal gels were of lower viscosities compared to the emulgel formulations, hence showed higher percentages of insulin release. Interestingly, when comparing the percentages of insulin released from emulgels with that released from niosome gels after 6 h, there were no statistical differences. However, insulin niosome emulgels showed a significant reduction in insulin diffusion across cellulose membranes (\(p<0.05\)). This could be ascribed to the resistance of niosome lipid membrane to release the trapped insulin that represent additional barrier for drug diffusion in addition to high emulgels viscosity [36]. The gelling effect was clear on insulin release in gel formulations when compared to control (gel solution). The kinetic analysis (Table 5) showed that the mechanism of insulin release across cellulose semipermeable membrane from all formulations was obeying Higuchi diffusion model except solution form of insulin that followed zero order kinetics. This result excludes the effect of cellulose semipermeable membrane on the amount of insulin released from niosomes, gels and emulgels niosome/emulgel combinations. Hence, the vehicle in which insulin was dispersed was the sole factor that affected insulin release across cellulose membrane.