esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
Optimization of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) Concentration for Efficient Erythiritol Biosynthesis by Y. lipolytica Using Glucose and Post-Crystallization Filtrate  
In the first stage of this study, the concentration of the nitrogen source, (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), was optimized for efficient biosynthesis of erythroid from glucose. (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) had already been used in preliminary studies involving the Wratislavia K1 UV15 strain [16]. Notably, this compound was identified as the optimal nitrogen source for erythroid production from glycerol [17]. Four cultivation processes were conducted with (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) concentrations of 2.6, 3.1, 3.6, and 4.1 g/L, respectively (Table 1--Production medium 1).  
The total glucose concentration was 300.0 g/L, with initial cultures grown in a medium containing both glucose (25.0 g/L) and compounds present in the filtrate obtained after erythiritol crystallization from the post-culture liquid (130.0 mL), including erythroid, mannitol, arabitol, and citric acid. The working volume at the start of cultivation was approximately 1400 mL, and additional portions of concentrated glucose solution, 200.0 g/L and 75.0 g/L, were added after 24 and 48 h, respectively. The working volume of the bioreactor reached 2 L after the final glucose addition. Results obtained from these cultivations are presented in Figure 2. The cultivations continued until all glucose was consumed, which took between 140 h for 4.1 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) and 216 h for 2.6 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (Figure S1). The highest erythiritol concentration, 146.3 g/L, was achieved in the process conducted with 3.1 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (Figure 2A). Under these conditions, key cultivation parameters such as yield (0.49 g/g) and volumetric erythroid production rate (0.87 g/Lh) were also the highest and significantly differed from the parameters obtained in the other three cultivations (Figure 2B).  
In the collected samples, the presence of other polyols, such as mannitol and arabitol as well as small amounts of citric acid, was also detected. The contribution of these products to the total metabolites produced did not exceed 19%. The highest selectivity, 86% (with the lowest relative amount of by-products), was observed in the cultivation conducted with 3.6 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (Figure 2B).  
Figure 2: The increase in erythroid concentration depending on the amount of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) in the cultivation medium (**A**). Process parameters: total yield of the erythroid process production (Y), productivity of total erythroid process production (Q), erythroid specific production rate (q), and the selectivity of the process (S) (**B**). Cultivation conditions: initial glucose concentration 25.0 g/L; 1 g/L of yeast extract; 130 mL of post-crystallization erythroid liquid. Mean values with different letters (A, B, C) (a, b, c) differ significantly at \(p\leq 0.05\).
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 4 Experiments
### Analysis of Experimental Results
#### 4.2.2 Result2: TUM-VI  
Table 3 shows the results of different methods on the TUM-VI dataset. VINs-mono and ORB-SLAM3 are traditional visual SLAM methods, while DDPG-SLAM and SL-SLAM [46] are deep-learning based visual SLAM methods.  
Based on the data presented in Table 3, the proposed method demonstrates a reduced RMSE on both the Corridor and Room sequence datasets. The RMSE for 66.7% of the sequences is lower than that of alternative methods, with a notable reduction of approximately twofold in RMSE for the Corridor1 and Room6 sequences. Our method  
Figure 5: Comparison of pose trajectories in some sequences of the Euroc MAV dataset. (**a,d**) show the trajectory estimation results for the MH01 and MH02 sequences, where our method (green) exhibits reduced drift compared with the baseline method (blue). (**b,e**) highlight the details of trajectory alignment, demonstrating that our method achieves higher accuracy in key trajectory segments. (**c,f**) illustrate the distribution of absolute pose errors, indicating that our method maintains lower error levels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **VINS-Mono [47]** & **ORB-SLAM3** & **DDPG-SLAM [6]** & **SL-SLAM [46]** & **Ours** \\ \hline Corridor1 & 0.630 & 0.040 & 0.021 & 0.086 & **0.011** \\ Corridor2 & 0.950 & 0.020 & 0.019 & 0.062 & **0.014** \\ Corridor3 & 1.560 & 0.031 & 0.200 & 0.039 & **0.018** \\ Corridor5 & 0.170 & 0.030 & 0.010 & **0.009** & 0.040 \\ Room2 & 0.070 & **0.020** & 0.028 & - & 0.028 \\ Room3 & 0.110 & 0.040 & 0.030 & - & **0.025** \\ Room4 & 0.040 & **0.010** & 0.020 & - & 0.046 \\ Room5 & 0.200 & 0.020 & 0.010 & - & **0.009** \\ Room6 & 0.080 & 0.040 & - & - & **0.019** \\ \hline \end{tabular}
\end{table}
Table 3: Absolute trajectory error of TUM-VI dataset. \(RMSE/m\downarrow\).  
demonstrates improved experimental results on the Corridor5 sequence when compared with the VINS-mono method, yet it falls short of surpassing the performance of the remaining three methods. As shown in Figure 6, the experimental trajectory plot on the TUM-VI dataset.  
We conducted tests in more challenging scenarios of the TUM-VI dataset, including Outdoors, Slides, and Magistrale, as shown in Table 4. Taking \"magistrale1\" and \"slides1\" as examples, our method demonstrates lower RMSE values in these complex scenarios, 0.81 and 0.53, respectively, outperforming both VINS-mono and ORB-SLAM3. However, it is worth noting that in the \"magistrale3\" sequence, VINS-mono performs better, with an RMSE of 0.40, surpassing both ORB-SLAM3 and our method.
# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 3 Results
### Results of Kernel Regression Model Including All the Explanatory Variables  
Figure 5b shows the scatter plot of the actual ages and those estimated by the kernel regression model using all the gait parameters as well as height and weight as explanatory variables. The correlation coefficient between the estimated and actual ages was 0.65, and the mean absolute error of age was 17.7 \(\pm\) 0.63 years.  
Figure 5: Scatter plot of actual and estimated ages of walkers by different estimation models. (**a**) Multiple regression analysis. (**b**) Kernel regression analysis involving all the explanatory variables. (**c**) Kernel regression analysis with selected variables. (**d**) Kernel regression analysis without height and weight.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Explanatory Variables** & **Height** & **Weight** & **Maximum Mediolateral Velocity of CoM** & **Step Width** & **Stride Length** & **Intercept** \\ \hline Coefficient & \(-\)7.65 & 4.68 & \(-\)5.61 & 10.87 & \(-\)2.94 & 44.36 \\ \(t\)-value & \(-\)8.48 & 5.31 & \(-\)4.97 & 9.21 & \(-\)3.61 & 25.10 \\ \(p\)-value & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 3: Partial regression coefficients of multiple regression model.  
Estimated ages were between 0-77 years, whereas the actual ages of the walkers ranged between 20-75 years. The maximum absolute error was 66.7 years.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 3 Results  
### Feature Engineering  
In the baseline model of this study, amino acid property (AAP) features, while contributing to epitope prediction, were not the sole critical factor. To comprehensively evaluate the impact of other features, we systematically analyzed the effects of spatial features and DSSP features on epitope prediction via the SAbDab_665 dataset and explored how these feature combinations could enhance model performance.  
First, we evaluated the impact of spatial features. These spatial features were utilized as edge features in the graph structure, effectively capturing spatial relationships between amino acid residues, including precise distance and directional information [26]. For the test set, the addition of spatial features alone improved the model's AUC from 0.735 to 0.802 and the AUPRC from 0.312 to 0.389, as shown in Table 1. This substantial performance improvement underscores the critical role of spatial relationships between residues, when represented as edge features, in enhancing the accuracy of epitope prediction.  
Next, we examined the contribution of DSSP features. DSSP features provide valuable information regarding protein secondary structure and solvent accessibility [27]. Upon integrating DSSP features into the model, the AUC increased to 0.785, and the AUPRC rose to 0.346. These results emphasize the importance of local structural information in epitope identification.  
More importantly, we assessed the model's performance under the combined influence of spatial and DSSP features. The results demonstrate that the multi-feature fusion model substantially outperformed the single-feature models, with the AUC increasing from the baseline value of 0.735 to 0.829 and the AUPRC improving from 0.312 to 0.433. This outcome validates the hypothesis that the synergistic effect of multidimensional features, particularly the incorporation of spatial information as edge features, can more comprehensively capture the complexity of epitopes, thereby enhancing the model's predictive ability [28].  
Based on these experimental results, our final model integrates a comprehensive combination of AAP, spatial, and DSSP features. We assert that this multidimensional feature engineering approach, especially the encoding of spatial information as edge features in the graph structure, not only captures the complexity of epitopes more holistically but also significantly improves the accuracy and robustness of the predictions. This integrated model provides a robust foundation for subsequent in-depth analyses and rigorous evaluations, offering a powerful tool for advancing the exploration of cutting-edge questions in the field of epitope prediction.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.5 Phasor Measurement Unit  
Phasor measurement unit (PMU) can be used in the context of self-healing to monitor and analyse the state of the power grid in real time. PMUs measure electrical quantities' magnitude and phase angle, such as voltage and current, at high speed and accuracy. PMUs can be used to implement self-healing mechanisms in several ways, such as:  
* Real-time monitoring: PMUs can be used to monitor the state of the power grid in real-time, providing high-resolution data on voltage, current, and frequency. This data can be used to detect and diagnose faults and other anomalies in the grid;
* Fault detection and isolation: PMUs can detect faults in the power grid, such as short circuits or equipment failures, by analysing electrical quantities' magnitude and phase angle. PMUs can identify the location and the extent of the fault;
* Restoration: PMUs can be used to facilitate the repair of power after a fault has occurred. By providing real-time data on the state of the grid, PMUs can help operators quickly identify the source of the fault and take steps to restore power;
* Protection: PMUs can be used to protect critical equipment and infrastructure. By monitoring the state of the power grid in real-time, PMUs can detect abnormal conditions and trigger protective measures, such as tripping circuit breakers or isolating faulty equipment.  
PMU for a self-healing feature on the power grid was implemented by [18] and created real-time monitoring and load balancing using three components that facilitate the self-adaptation and self-healing functionality of the network. The following list describes the three components of the PMU:  
1. Intrusion detection system (IDS): Relies on the PMU network logs and phasor measurements to detect different classes of abnormal events within the network;
2. Intrusion mitigation system (IMS): Once the IDS detects an anomaly, the generated alerts from AMS are delivered through a publisher-subscriber interface; for appropriate remedial action to be taken;
3. Alert management system (AMS): Generates alerts based on anomaly rules defined in IDS and forwards the alert to the IMS if abnormal events are detected for onward remedial actions by the IMS.  
AMS comprises three sub-components: the alert manager subscriber1, subscriber2, and subscriber3. The alert subscriber1 collects alert logs from anomalies detected and forwards them to the IMS. The subscriber2 sends the received alert messages to the namespace orchestrator, which triggers the orchestration process on a given substation namespace. The alert manager subscriber3 sends the received alert messages to the application programming interface (API) of the central management application [18].
## 2 Materials and Methods
### AAA Growth Rate Prediction Modelling  
A multi-layer perceptron (MLP)-based regression model was developed to predict AAA growth rate based on radiomic features within the AAA sac from the whole AAA and aortic arch blood pool segmentations. Ground truth growth rate values for AAAs were calculated from serial ultrasound measurements.  
Ratios of AAA to aortic arch blood pool values for radiomic features were calculated to compensate for blood pool uptake [30]. Normalising against the aortic arch blood pool, was conducted to reduce confounding factors and enhances the specificity of radiomic feature analysis. This approach is analogous to SUV normalisation commonly used in clinical PET to enable more reliable interindividual comparisons. A recursive feature elimination (RFE) algorithm based on Random Forest (RF) regression (available in Scikit-learn (Version 0.24) Python library) was used to select the most discriminative set of radiomic features for models predicting AAA growth rates [31; 32]. Table 2 details the radiomic features of highest importance. Subsequently, a MLP was developed for regressing AAA growth rates using full and reduced sets of radiomic features. The MLP was implemented using TensorFlow-Keras (Version 2.3) [33].  
The internal dataset was initially split into 70% training/validation and 30% testing. K-fold cross-validation (K = 10) was subsequently used to split the former into 80% for training and 20% for validating the machine learning (ML) model. This enabled assessment of the reproducibility and generalisation capacity of the proposed model. The generated models (from the K-fold cross-validation experiments) were output into a Hierarchical Data Format 5 (HD5) file and evaluated against the test data. Figure 2 illustrates the ML framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Class** & **Feature Count** & **Feature Names** \\ \hline First Order & 4 & 90 Percentile, Maximum, Uniformity, Variance \\ Gray Level Co-Occurrence Matrix & 2 & DifferenceAverage, Imc1 \\ Gray Level Run Length Matrix & 2 & ShortRunHighGrayLevelEmphasis, \\ Gray Level Run Length Matrix & 2 & ShortRunLowGrayLevelEmphasis \\ GrayLevelNonUniformity, & GrayLevelNonUniformity, \\  & & GrayLevelNonUniformityNormalised, \\  & & GrayLevelVariance, \\  & & HighGrayLevelZoneEmphasis, \\  & & LargeAreaHighGrayLevelEmphasis, \\ Gray Level Size Zone Matrix & 12 & LargeAreaLowGrayLevelEmphasis, \\  & & LowGrayLevelZoneEmphasis, \\  & & SizeZoneNonUniformity, \\  & & SizeZoneNonUniformityNormalised, \\  & & SmallAreaHighShargLevelEmphasis, \\  & & SmallAreaHighGrayLevelEmphasis, \\  & & SmallAreaLowGrayLevelEmphasis, \\  & & ZoneEntropy, ZonePercentage, ZoneVariance \\ Neighbouring Gray Tone & 5 & Busyness, Coarseness, Complexity, Contrast, \\ Difference Matrix & 5 & Strength \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features Included in the Reduced Dataset Derived from RF-RFE.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 5 Issues and Conclusions
### Conclusions  
In the development process of on-line Lamb wave detection equipment for microcracks in thin-walled tubes, the following conclusions were drawn.  
Figure 8: The peak of flaw signal with different depths ((**a**) The peak of inner flaw signal with different depths; (**b**) The peak of outer flaw signal with different depths).  
* Based on the calculation and simulation of phase velocity, incident angle, and particle amplitude displacement, the Lamb wave \(A_{0}\) mode with the highest sensitivity to the inner wall and outer wall cracks of thin-walled tubes was determined. This is consistent with the results of Ref. [37].
* A defect signal extraction circuit was designed, which greatly reduced the amount of data acquired and improved the data processing speed.
* Rapid on-line detection equipment for microcracks in thin-walled tubes was developed, and the engineering application of rapid detection of microcracks by Lamb waves was realized.
* For the 0.045-mm depth of the artificial defect, whether the inner wall, or outer wall of the longitudinal defect, or the transverse defect, there was almost no difference in the shape and amplitude of the defect waveform. However, for the inner wall of the longitudinal defect and the transverse defect, or the outer wall of the longitudinal defect and the transverse defect, waveform amplitude and contour had certain differences.
* For the artificial defects with depths of 0.073 and 0.10 mm, the longitudinal defects of the inner and outer walls were quite different in terms of waveform profile and amplitude.
* This study not only solved the practical issue of the rapid detection of microcracks in thin-walled tubes but also provided a reference for the application of ultrasonic technology to detect other components.  
Conceptualization, S.Y. and M.W.; Methodology, S.Y.; Software, S.Y. and L.Y.; Validation, L.Y., and S.Y.; Formal Analysis, M.W.; Investigation, L.Y.; Resources, M.W.; Data Curation, L.Y.; Writing--Original Draft Preparation, S.Y.; Writing--Review and Editing, S.Y. and M.W.; Visualization, L.Y.; Supervision, M.W.; Project Administration, S.Y. and M.W.; Funding Acquisition, M.W.  
This work was supported by the National Key Scientific Instrument and Equipment Development Projects of China (Grant No. 2013 YQ240803) and the International Science and Technology Cooperation Program of Shanxi, China (Grant No. 201803D421032).  
The authors declare no conflict of interest.
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 2 Materials and Methods
### Model Fitting  
After obtaining the collection of estimated signature vectors, the _relative_ SpO\({}_{2}\) contrast, i.e., the change in pulse amplitude as a function of the SpO\({}_{2}\) value, between the wavelengths has to be determined for the update vector \(\bar{P_{bp}^{u}}\). We accomplished this by sorting the estimated relative amplitudes from the wavelength with the largest SpO\({}_{2}\) contrast in ascending order, as visualized in  
Figure 3: A visualization of the exhaustive search for the wavelengths [675,800,905] nm shows that the optimum, pulse quality, shifts as function of SpO\({}_{2}\) level. Here, \(\alpha\) and \(\beta\) are the relative pulse amplitudes for 675 and 905 nm, respectively, and the amplitude for 800 nm is set to 1.  
Figure 4, where the corresponding values from the other wavelengths are displayed at the same location on the horizontal axis. In order to prevent overfitting on the few samples corresponding to the lowest and highest SpO\({}_{2}\) values, first-order polynomials were fitted on the sorted samples to estimate the relative SpO\({}_{2}\) sensitivity using iteratively re-weighted least squares with a bisquare weighting function. Here, the weights are the values of the quality metric of each estimated signature vector. The slopes of the \(N-1\) fits are a scaled version of the update vector \(P_{bv}^{\mu}\), indicated with \(P_{bv}^{\mu*}\) in Figure 4. In the next paragraph, we explain how the vector set for the APBV calibration model can be derived from the vectors \(P_{bv}^{*\ast}\) and \(P_{bv}^{\mu*}\).  
Determining the APBV Calibration Model  
In order to determine the APBV calibration model, the range of SpO\({}_{2}\) values present in the dataset has to be incorporated. Contrary to most calibration methodologies, we did not link each data-point (pulse signature vector) to an SpO\({}_{2}\) value from the reference. Rather, we looked at the range and distribution of SpO\({}_{2}\) values to reduce the effects of outliers and to prevent overfitting on short-term desaturations with unknown physiological delays between the SpO\({}_{2}\) traces from the face (camera) and fingers (reference). The procedure is illustrated in Figure 5. Similar to the collection of estimated pulse signatures, we fitted a first-order polynomial to the sorted SpO\({}_{2}\) of the reference data. The \(\Delta\)SpO\({}_{2}\) of this fit is used to determine \(P_{bv}^{\mu}\), by scaling \(P_{bv}^{\mu*}\). The signature vector for 100% SpO\({}_{2}\), \(P_{bv}^{s}\), is calculated by linear regression using the maximum value of the fit, \(SpO_{2}^{max}\), and the update vector \(P_{bv}^{\mu}\).  
Figure 4: First-order polynomials are fitted on the sorted pulse signatures (by the amplitude of the wavelength with the largest SpO\({}_{2}\) contrast, from high to low SpO\({}_{2}\)) to estimate the relative SpO\({}_{2}\) sensitivity using iteratively re-weighted least squares with a bisquare weighting function.