esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 2 Methodology
### Model Evaluation and Optimization  
For the purpose of visual, reliable, and accurate reflection of the forecasting effect of each model, five indexes of Nash efficiency coefficient (\(NSE\)), root mean square error (\(RMSE\)), mean absolute percentage error (\(MAPE\)), mean absolute error (\(MAE\)), and correlation coefficient (\(R\)) were selected as the evaluation criteria [17; 31]. The detailed formulas of the five indicators are  
\[NSE=1-\frac{\sum_{i=1}^{n}\left(y_{i}-y_{i}^{*}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}} \tag{11}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-y_{i}^{  
where \(y_{i}\) refers to the measured value; \(y^{*}\) refers to the simulation value; \(\overline{y}\) refers to the average of the measured values; and \(\overline{y^{*}}\) refers to the average of the simulation values.  
Because each evaluation index analyzes the model performance from different perspectives, the model forecasting results don't necessarily show optimal performance in all aspects. Therefore, this paper adopted the composite rating index method (\(M_{R}\)) to calculate the composite rating of the model by ranking each index. \(M_{R}\) can effectively judge the consistency of the evaluation indexes [32]:  
\[M_{R}=1-\frac{1}{mn}\sum_{i=1}^{n}r_{i} \tag{16}\]  
where \(m\) is the number of models, \(n\) is the number of evaluation indexes, and \(r_{i}\) is the ranking of each model evaluation index (\(i\) = 1, 2, 3). When \(r_{i}\) = 1, it means that the model has the best simulation effect under this evaluation index. The value of \(M_{R}\) is between 0 and 1. A larger \(M_{R}\) value indicates a better model's comprehensive simulation ability.
# Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
## 5 Improving Supply Chain Visibility with a Digital Twins
### Visibility for Sensing and the Digital Twins  
As described in the theoretical context, there are four separate areas to explore when looking at what a Digital Twins might do to increase the supply chain's visibility. Visibility for sensing is the first type, reflecting the degree to which the enterprise can easily acquire real-time knowledge about internal and external processes and adapt to an evolving market climate. For an organization, the introduction of a Digital Twins will be tremendously helpful in this respect. The Digital Twins sensors could obtain new data types, offering more insight into the supply chain flows [70]. The Digital Twins will then identify real-time changes and alert the company about them by aggregating and interpreting the sensors' data. Therefore, as more sensors are introduced and incorporated, the supply chain's exposure will also begin to improve [71]. Naturally, to ensure data consistency that will cause the Digital Twins to draw assumptions that correctly mirror the fact, one must carefully consider the sensor versions. The company must also ensure that sufficient sensors are mounted for each given flow or scenario to allow the most productive use of the data obtained [7].  
When it comes to optimizing supply chain visibility for internal sensing, a Digital Twins will help the company recognize improvements in demand in their supply flows more quickly [72]. With the introduction of Digital Twins to content preparation, the company will provide a more precise understanding of how many items should be ordered, resulting in a visible improvement in sensing exposure when it comes to enhancing supply chain visibility for external sensing. Using a Digital Twins in material preparation would help packaging planning gain greater visibility because packaging could be ordered alongside Digital Twins material orders [73]. This, combined with a Digital Twins that tracks packaging during the packaging period, will allow the company to be more responsive to shifts in demand for packaging. A Digital Twins may also expose the gaps in the packaging cycle, providing the packaging purchasing department with a real-time and precise picture of how demand for packaging improves [74].
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation  
To validate the design and implementation of the proposed device, several tests were applied verifying its two operating modes: as a voltage probe connected to an oscilloscope; and as an autonomous remote instrument with RF communication capabilities.
## 4 Results
### Defect Separation Performance with Experimentally Acquired Railway Noise Signals  
To further validate the practicability of the proposed method for the defect separation, on-site experimental noise is used to represent complicated noise properties and synthetize the two-channel observation signals. Gaussian noises at \(-20\) dB were also added to the observations for the noise resistance investigation. The entire separated signals and a zoomed-in figure of the detailed sections within [0, 0.05] milliseconds are shown in Figures 10 and 11, respectively.  
Overall, as shown in Figure 10, the proposed method finds the most accurate projection vector for separation and delivers the best recovery performance. Nevertheless, the separation performances in ICA and SNN are relatively poor, significantly affected by the residual Gaussian noise and abnormal railway noise. The noises with obvious amplitudes still exist in the extracted crack signal. After Z-score standardization, the root mean square (RMS) of the averaged residual noise in ICA from Figure 10a was 0.3882, approximately three times higher than the RMS of the residuals in TFBSS at 0.1075 and TFSNN at 0.1029. The RMS of the residuals in the SNN was 0.151, also reflecting a significant increase. This suggests that the robustness of ICA and SNN against Gaussian noise is lower compared to the time-frequency domain methods. Particularly, ICA is based on the assumption of pure Gaussianity in the time domain, which may result in its reduced robustness against random Gaussian noise interference and restrain its further application in practice. In contrast, some pulse abnormal noise with minor amplitude still exists in the extracted crack signal of the TFBSS result. Compared to the three algorithms, the proposed method is clearly more effective in detecting rail crack signals in real noise environments, accurately recovering the occurrence of crack signals from noisy mixtures in practice. Even though this is a promising result, it still needs to be systematically compared using an entire group of 100 test signals to further analyze the stability and give a comprehensive conclusion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Crack** & **Square Pulse** & **Ramp** \\ \hline ICA & 0.9470 & 0.9723 & 0.9952 \\ SNN & 0.9854 & 0.9947 & 0.9958 \\ TFBSS & 0.9980 & 0.9994 & 0.9984 \\ TFSNN & 0.9982 & 0.9997 & 0.9992 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Similarity coefficients between separated signals and sources using different methods.
## 5 Analysis of Results and Field Testing
### Field Tests
#### 5.5.1 Water Level Line Detection Test and Analysis
In contrast, other water level detection algorithms such as [16] detect the water level line position directly by detecting the lower edge of the water gauge, and [35] uses DeepLabv3+ to directly segment the water gauge, which has a greater accuracy than detecting the lower edge of the water gauge position. The study in [11] uses color information to segment the water level, rendering the algorithm ineffective during the night. The study in [18] can be considered as an improvement on [35], as it crops a small area of the water gauge close to the water surface and then segments the small area to obtain the water level line position. These algorithms rely to some extent on the body characteristics of the water gauge itself, which impact on the accuracy and reliability of the algorithm.
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 3 Experiments and Analysis
### Comparative Simulation Test of Anti-Noise Performance of the PnP Method  
The above simulation tests demonstrated the smaller accuracy error and better stability of the Hidden PnP, compared with other PnP algorithms, in different cases. Due to the interference of the environment and electronic equipment in practice, the collected information contained noise, so it was necessary to compare the anti-noise performance of the Hidden PnP algorithm and other PnP algorithms, which is a vital index of the comprehensive performance of a PnP algorithm. Accordingly, the following anti-noise performance simulation tests were conducted, in which the 3D spatial reference point n was set to be 10, and 10 different noise zero-mean Gaussian noises were set in Ordinary 3D, Planar Case, and Quasi-Singular case, respectively. The pixel value distribution started from \(\delta\) = 0.5 pixels and increased to \(\delta\) = 5 pixels by arithmetic sequence, with a tolerance of 0.5 pixels. In the above three cases, each PnP algorithm was independently tested 500 times for each fixed noise pixel value. Due to the large number of algorithms, compared in the simulation test, and the large amount of data, the following data curve could only be used as a macroscopic performance comparison display. Refer to Appendix B (Tables A4-A6) for the specific test data. Figures 6-8 shows the error comparison of zero-mean Gaussian image noise from 0.5 to 5 pixels in ordinary 3D, coplanar, and quasi-singularity cases, respectively. For all figure groups, the four sub-figures were: a. mean error of the rotation matrix; b. median error of rotation matrix; c. mean error of translation vector; d. median error of translation vector. The spatial reference point n = 10.  
Figure 6: Error comparison in ordinary 3D case.  
Figure 8: Error comparison in Quasi-singularity case.  
Figure 7: Error comparison in Planar case.  
The above simulation test curve and data revealed that the LHM algorithm exhibited the worst anti-noise ability in the above three cases, and the solution accuracy error of the DLS and EPnP+GN algorithms jumped with larger noise pixels. The anti-noise ability of the RPnP algorithm was better than that of the DLS and EPnP+GN algorithms, but worse than that of the OPnP, SRPnP, RDLT, and ASPnP algorithms. It was obvious that the Hidden PnP algorithm had the best anti-noise ability among the above nine algorithms.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Model Building and Training  
#### 4.4.1 Model Building  
The 1D-CNN model of the motor fault diagnosis is built in this paper based on the multi-sensor fusion, which consisted of six sub-models with the same parameters, each including an input layer and four convolution-averaging pooling layers. Multi-source heterogeneous sensor data as the input of each submodel and converted into a set of feature maps through the convolutional layer, then the feature mapping was down-sampled in the average pooling to reduce the number of parameters. Four convolutional-pooling layers are repeated, the output of the final pooling layer in each submodel is concatenated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Capacity** & **Sample Width** & **Fault Category** & **Labels** \\ \hline
800/200 & 1000 & Fault-free motor & 1 \\
800/200 & 1000 & Stator fault & 2 \\
800/200 & 1000 & Rotor fault & 3 \\
800/200 & 1000 & Bearing outer ring fault & 4 \\
800/200 & 1000 & Bearing inner ring fault & 5 \\
800/200 & 1000 & Bearing rolling body fault & 6 \\
800/200 & 1000 & Synthetic fault of the front and rear bearings & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Labeling of motor fault modes.  
Figure 6: Time domain diagram of data under different fault states.  
to a Flatten layer to flatten the multi-channel one-dimensional data features into a one-dimensional array, and then a fully-connected layer is used to perform feature fusion on the data features of multi-source heterogeneous sensor information. Then the information is passed to the Softmax function output layer. Finally, the probabilities of each fault category value are obtained, and one of the biggest probability categories was the recognition result. Figure 7 and Table 4 show the specific structure of the model and the main parameters of each structure.
## 3 Results and Discussion
### Sgs Cases
#### 3.2.4. SGS Cases: Underlying Mechanism
The slight pressure drop at 90% SGS severity causes minor changes in vibration patterns (reflected in POD mode energy changes) without significantly altering overall vocal fold dynamics. The much larger pressure drop at 96% severity, however, leads to the substantial changes in vocal fold vibration we observed. Our findings are consistent with previous simplified model studies [14] regarding several fundamental mechanisms, including the threshold of 75% stenosis for initiating significant changes, the progression sequence where flow changes precede vibration changes, the dominant roles of flow resistance and pressure drop, and the relationship between area ratios and onset of changes. Beyond these common features, our patient-specific model revealed distinct differences in severe cases. Particularly, the anterior-posterior opening patterns and focused jet effects led to changes in vocal fold dynamics. As detailed in Section 3.2.2, these changes manifested as a leftward shift in the glottal area waveform  
Figure 12: The pressure drop change across the vocal fold compared to the baseline case for each SGS severity case. (versus rightward in simplified models), decreases in the open quotient (versus increases), and increases in fundamental frequency (versus decreases), along with significant changes in flow acceleration/deceleration duration. These findings provide valuable insights into the progression of SGS effects on voice production, suggesting that while flow changes may occur at lower severities, significant alterations in vocal fold dynamics only manifest at very high stenosis levels.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 2 Lithium-Ion Battery Data Explanation and Health Factor Selection  
### Battery Data Description  
This paper uses the well-known NASA lithium-ion battery dataset as the research object, in which the battery model is the 18,650 lithium iron phosphate battery. The following experimental operations are performed on B0005, B0006, B0007, and B0018 batteries at a battery experimental temperature of 24 \({}^{\circ}\)C and a rated capacity of 2 Ah. The first step is to charge the four batteries with constant current and constant voltage. Under the current environmental conditions, they are first charged with a constant current of 1.5 A. When the battery voltage reaches 4.2 V, they are charged with constant voltage and current. When the current reaches 20 mA, the first step ends; that is, the charging stage ends. The second step is to discharge the battery with a constant current of 2 A and discharge B0005, B0006, B0007, and B0018 to cut-off voltages of 2.7 V, 2.5 V, 2.2 V, and 2.5 V, respectively [30]. At this point, a charge and discharge experiment is completed. Then, in order to accelerate battery aging, the above operation is repeated. When the actual capacity of each battery is less than 1.4 Ah, the end-of-life condition is reached. The capacity degradation curves generated by the four batteries are shown in Figure 1. The corresponding parameters are shown in Table 1.
## 1 Introduction
Therefore, in this study, a hybrid activation function in the form of a combination of the hyperbolic tangent function and the rectified linear unit function with a weighting factor was proposed to improve the forecasting accuracy at a high water surface elevation and for a long leading forecasting time. The LSTM in Tensorflow, a deep-learning open-source software library provided by Google, was applied and the hourly hydrological data from 2009 to 2018 were used to forecast the water surface elevation in Hangang River, Korea. Finally, the hybrid activation function was applied to evaluate whether the accuracy of forecasting improved at a high water surface elevation the range of leading time was reviewed for highly accurate forecasting.
# Structural Evolution of Polyimide-Derived Carbon during Phosphoric Acid Activation
## 1 Introduction  
The activation of various precursors with phosphoric acid is a widely used industrial process for the production of highly porous carbon adsorbents [1, 2, 3, 4, 5, 6, 7]. Phosphoric acid activation has several advantages, primarily high yields and low activation temperatures compared to physical activation [8, 9, 10]. In addition, phosphoric acid activation does not have the disadvantages, such as corrosion problems, insufficient recovery of the activator, and environmental problems inherent in the zinc chloride activation process [11].  
Many scientists, however, are unaware of the possible inclusion of phosphorus compounds into the carbon structure during activation with phosphoric acid, which can modify the surface chemistry of the carbon adsorbent. Recently it was shown that phosphorus compounds, which are incorporated into the structure during activation with phosphoric acid, give an acidic character to the surface of activated carbon, which determines their catalytic properties and adsorption capacity for metal ions [12]. The XPS technique is most commonly used to identify the structure of phosphorus compounds in phosphorus-containing carbon materials [13, 14, 15, 16, 17]. However, there are disagreements in the literature regarding the interpretation of experimental XPS spectra, which may lead to different conclusions regarding the structure of phosphorus-containing compounds. On the one hand, some researchers conduct deconvolution of the P 2p peak with varying bindingenergy and assign the resulting values to specific substances with close binding energy. New peaks are added until there is no increase in the quality of fit [15; 16; 17; 18; 19; 20; 21; 22]. On the other hand, other investigators perform deconvolution of the P 2p region to 3-4 peaks with _a priori_ fixed binding energy values corresponding to certain phosphorus compounds [23; 24; 25; 26; 27; 28]. Thus, different structures of phosphorus-containing compounds in activated carbon can be obtained from the same P 2p spectrum depending on the deconvolution scheme. The use of an auxiliary method, such as \({}^{31}\)P NMR, can assist in identifying the structure of phosphorus compounds in carbon adsorbents and predicting their characteristics [29; 30; 31; 32; 33; 34; 35; 36; 37].  
In this study, an analysis of the structure of phosphorus-containing compounds in activated carbons obtained from polyimides was carried out using XPS and NMR techniques and the relationship of the structure with acid-base characteristics and adsorption capacity for copper ions is established.
## 1 Introduction  
The Cryogenic Dark Matter Search (CDMS) can be described as a series of specially designed experiments that are used to detect Weakly Interacting Massive Particles (WIMPs), i.e., dark matter, using an array of semiconductor detectors at extremely low temperatures (mK). The exact definition of WIMPs does not exist; however, they are described as hypothetical new elementary particles (candidates for dark matter) that interact via gravity and other forces and are not part of the Standard Model. The product of every particle interaction in the germanium and silicon substrate produces ionization and phonons, which are measured using CDMS detectors [1]. The measurement of ionization and phonons determines the energy deposited in the crystal for each interaction and provides information on which kind of particle caused the event. Every particle's interaction with atomic electrons (electron recoils) and atomic nuclei (nuclear recoils) results in different ratios of ionization and phonon signals. The majority of particle interactions are electron recoils, while WIMPs are expected to be nuclear recoils. Although the WIMP scattering events are unique, they are rare when compared to the vast majority of unwanted interactions.  
Two types of experiments exist, i.e., CDMS and SuperCDMS. The CDMS experiment provided the most-sensitive test of potential WIMP-nucleon interactions, as reportedin [2; 3]. The XENON experiment [4] later surpassed the CDMS experiment. The SuperCDMS experiment [5] is the successor of the CDMS, which uses new and improved detectors and an increased target mass to improve sensitivity by a factor of 5-8 factor and is limited by the residual cosmogenic background at the current location.  
One of the challenges in CDMS and SuperCDMS experiments is the detection accuracy of particle interactions and the reconstruction of particle interaction locations, which could be greatly improved with the use of machine learning (ML) algorithms. In recent years, scientists have implemented various ML algorithms to estimate or detect dark matter using data obtained from CDMS/SuperCDMS experiments. Some of these research papers are briefly described in the following subsection.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification  
### The Structure of the DSOGI-PLL-Based Grid-Connected Inverter  
Figure 2 shows the structure of the DSOGI-PLL-based grid-connected inverter system. In the control system, \(i_{d}\), \(i_{d\text{det}}\), \(i_{q}\), and \(i_{q\text{ref}}\) represent the \(dq\)-axis sampled current and current reference at the PCC, respectively. \(m_{dq}\) and \(m_{abc}\) represent the \(dq\)- and \(abc\)-axis modulation signals, respectively. \(p_{1-6}\) represents the switching signals. The single closed-loop control strategy is adopted in the control system, where \(H_{1}\) represents the PI controller.  
For the power circuit, the AC side is connected to the grid through an \(L\)-type filter, and the DC side is connected to a constant voltage source to simplify the analysis. \(V_{\text{g}}\) and \(V_{\text{dc}}\) represent the grid voltage and DC-side voltage, respectively. \(R_{\text{g}}\) and \(L_{\text{g}}\) represent the resistance and inductance of the grid impedance, respectively. \(\mathbf{v_{\text{t}}}\) and \(\mathbf{i_{\text{t}}}\) represent the three-phase voltage and current at the point of common coupling (PCC), respectively.  
The control structures of the SOGI and the DSOGI-PLL are shown in Figure 3, where \(v\) represents the input voltage signal; \(\varepsilon\) and \(k\) represent the error signal and damping coefficient, respectively; \(\omega\) represents the resonance frequency; \(v^{\prime}\) and \(qv^{\prime}\) represent two orthogonal output signals, respectively; \(v_{\text{p}\ast}\), \(qv_{\text{p}\ast}\), \(v_{\text{p}\beta^{\prime}}\), and \(qv_{\text{p}\beta^{\prime}}\) represent the \(\alpha\)- and \(\beta\)-axis components of the voltage signals processed by the SOGI, respectively; \(v_{\text{p}\ast}\) and \(v_{\text{p}\beta^{\prime}}\), represent the \(\alpha\)- and \(\beta\)-axis positive sequence components, respectively; \(v_{\text{p}d\ast}\) and \(v_{\text{p}\ast}\) represent the \(d\)- and \(q\)-axis positive sequence components, respectively; \(k_{\text{p}}\) and \(k_{\text{i}}\) represent the proportional gain and integral gain of the PI controller in the PLL, respectively; and \(\theta\) represents the output angle of the PLL.  
Figure 3: The structure of the SOGI and DSOGI-PLL.  
Figure 2: The basic structure of the DSOGI-PLL-based inverter.