esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulation
### Assumptions
* The energy consumption dynamics with the degree of fullness and allowed hysteresis band were assumed to be identical for industrial freezers as the ones proposed in [34; 35] and presented in Theorem 1. * The considered industrial freezers could automatically (or manually) adapt their hysteresis band according to the usage level, with a maximum allowed hysteresis of 2 \({}^{\circ}\)C--this means that according to Theorem 1, by changing the hysteresis band, the energy consumption could be kept the same with increasing occupation. * The maximum allowed hysteresis of 2 \({}^{\circ}\)C allowed a constant energy consumption up to a utilization of 40%--after that, the energy consumption started to increase from nominal values. This increase stagnated after a certain capacity was reached. * The increase in energy consumption was modelled as a sigmoid function, approximately constant at lower values, then increasing when the maximum hysteresis band was reached, and returning to constant values. * The warehouse energy consumption components of transportation of equipment, building heating and cooling, and ventilation were considered to be dependent on the degree of fullness of the warehouse. The remaining components of lighting, IT networks, and other energy consumptions were considered to be fixed values. * The energy consumption of the warehouse from the transportation of equipment was modelled linearly with the degree of fullness of the warehouse--more occupation required more movement of stock. * Considering that a warehouse is required to be kept at a specific temperature, and ignoring the transient period required to increase the warehouse contents' inner temperature up to the environment temperature, the greater the ratio of fullness of the warehouse, the lower its non-transient energy consumption, as proved in Theorem 3. This means that the warehouse's energy consumption elements of heating, cooling, and ventilation were modelled as inversely proportional to its degree of fullness.
## 2 Results and Discussion
### Phenolic Antioxidants and Structure-Activity Relationships in Goji Juice
#### 2.3.2 Extractable and Bound Individual Phenolics
All of the goji juice samples contained five types of flavonoids, comprising rutin, quercetin, naringenin, luteolin, and kaempferol. In the raw and fermented goji juice samples, the concentrations of the free forms of rutin and quercetin were higher than the bound forms. Compared with the raw goji juice, the concentrations of the free form of rutin were increased by 1.35, 1.29, and 3.10 times in the juice samples fermented with SLV, SZP, and SZVP, respectively. Similar results were obtained for other fermented juices in a previous study [35]. The increases in the concentrations of the free form of rutin might not have been due to the conversion of the bound form of rutin because the concentration of the bound form of rutin was low in the goji juice. Thus, the increases may have been due to the depolymerization of high molecular weight phenolics by (poly)phenoloxidases during fermentation [35]. The free form of naringenin was not detected in the raw goji juice, but it appeared after fermentation. In addition, the concentration of the bound form of naringenin decreased by 51.72% in the fermented juice, possibly due to the conversion of the bound form of naringenin. The free forms of luteolin and kaempferol were not detected in the raw and fermented goji juice samples. However, the content of the bound form of luteolin increased after fermentation. [PERSON] et al. [35] reported that the stability of phenols depended on the pH. The pH was reduced after fermentation (3.84-4.20), and this might have stabilized the bound forms of luteolin and kaempferol [35].
## 3 Results and Discussion
### Training and Testing of the HC-LSSVM Model  
According to the construction method of the HC-LSSVM model (Section 2.2) and the training samples of soft soil settlement, after many comparisons, the following HC-LSSVM  
\begin{table}
\begin{tabular}{c} **Cumulative** \\ **Settlement Time** \\ **\(C_{st}\) (Day)** \\ \end{tabular} \\ \hline
0 & 0 & 246 & 134 & 525 & 158 \\
29 & 8 & 254 & 134 & 555 & 163 \\
31 & 11 & 277 & 134 & 556 & 163 \\
46 & 41 & 308 & 134 & 586 & 176 \\
60 & 69 & 321 & 134 & 587 & 176 \\
63 & 7  
model solution process and parameters are set. (1) Set the predicted embedding dimension to 2, that is, the dimension of the input vector \(X_{i}\) is 2. (2) The sample data must satisfy the nonlinear mapping relationship between the autocorrelation \(X_{i}=\{x_{i}-1,x_{i}-2\}\) and the output \(Y_{i}=\{x_{i}\}\), where \(x_{i}\) is the soft soil settlement time of a group of observed data. (3) The radial basis function (RBF function) was taken as the kernel function, the grid search range was set \(C\in[1,2500]\) and \(sig2\in[1,1000]\), the initial regularization parameter \(C_{0}=100\) and the kernel parameter \(sig2_{0}=10\), and the grid search step was \(C\_step=30\) and \(sig2\_step=10\). (4) Set the homotopy parameter step \(t\_step=0.2\). After a multi-step grid search, the optimal LSSVM model parameters were obtained as \(C=209\) and \(sig2=24\). The HC-LSSVM model was established based on the optimized LSSVM model parameters, and settlement prediction was carried out for the test samples.  
According to the optimization parameters obtained by the above HC-LSSVM model, the comparison between the predicted value and the monitored actual value of the training samples is shown in Figure 4. The predicted value is very close to the actual value in Figure 4, and the average error \(\varepsilon_{a}\) is only \(1.55\%\), which proves the reliability of the optimized parameters obtained by HC-LSSVM model.  
The observation data from 618 days to 742 days were taken as the test samples, and the test results are shown in Table 2 and Figure 4. It can be seen from Table 2 that the trained HC-LSSVM model is very close to the cumulative settlement of the embankment center. The error \(\varepsilon\) between predicted and actual value of settlement amount (\(P_{v}\) and \(A_{v}\)) is between 0.50% and 3.62%, and the average error \(\varepsilon_{a}\) is 1.87%. This shows that the prediction of soft soil settlement according to the optimized parameters obtained by HC-LSSVM model can get very close to the monitored actual value.  
Figure 4: Comparison between predicted and actual value of soft soil settlement of training samples and test samples.
## 2 Results
### C. hircus UK114 Is a Deiminase
As shown in Figure 3c, UK114 activity using the imino acid produced by DAAO from D-Leu was indistinguishable from that observed using LAAO and L-Leu. These results further demonstrate that UK114 is a RidA protein and also show that the substrate of UK114 is the \(\upalpha\)-imino acid, irrespective of the mode of its generation. Specificity of UK114 toward Different Imino Acids  
To extend the knowledge on the catalytic properties of goat UK114 in comparison with those of other RidA, we tested the enzyme activity on the \(\upalpha\)-imino acids that were generated in situ by LAAO and amino acids selected as representative of various classes (Figure 4, Table 1). UK114 was mostly active on the derivative of L-Ala, followed by L-Met and Leu. It was less active on L-Gln and poorly active on L-Phe and L-His (Figure 4, Table 1). The results obtained not only using D-Leu, but also d-Ala and DAAO are reported in Figure 4, and are indistinguishable from those obtained with the corresponding L-amino acids and LAAO. The curves that are shown in Figure 4 are the fit of the data to Equation (1), which allows the calculation of the concentration of UK114 that halves the initial velocity of semicarbazone formation measured in its absence (See Supplementary Materials). The values of the parameters and their standard errors are summarized in Table 1.  
In summary, UK114 can use multiple substrates, but exhibits little or no activity on L-Phe and L-His, while it is active on imino acids from Ala, Leu, L-Met, and, to a lower extent, L-Gln, qualitatively paralleling _S. enterica_ RidA specificity [6; 15]. That the highest activity is obtained with the product of  
Figure 4: Specificity of UK114. L-Ala (open squares, blue), L-Leu (open circles, red), L-Met (open triangles, green), L-Gln (open diamonds, orange), L-Phe (inverted open triangles, purple), and L-His (stars, fuchsia, 5 mM each) were incubated with 10 mM semicarbazide in 50 mM sodium pyrophosphate buffer, pH 8.7, in the presence of catalase (10 \(\upmu\)g/mL), LAAO (5–800 \(\upmu\)g/mL), and varying UK114 concentrations at 25 \({}^{\circ}\)C. The results obtained using D-Ala (closed blue squares) and d-Leu (closed red circles) and DAAO (2–20 \(\upmu\)g/mL) were indistinguishable from those that were obtained with the corresponding L-amino acid and LAAO. Thus, they were fitted together to Equation (1) obtaining the values of the concentration of UK114 that halves the velocity of semicarbazone formation shown in Table 1. The data obtained with L-Phe were fitted with a straight line. Due to the low effect of L-His, the analysis was limited and no data fitting was performed. the oxidation of L- and D-Ala by LAAO and DAAO, respectively, is particularly interesting. Indeed, the 2-iminopyruvate (2-iminopropionate) is the tautomer of 2-aminoacrylate produced by both serine and cysteine dehydration, which is believed to be the RidA physiological substrate [2; 3; 5; 35].
## 1 Introduction  
Solar cells based on perovskite structures (PSCs) have registered a significant power conversion efficiency (PCE) increase in recent years, achieving a record of 25.7% for non-stabilized PSCs, 29.8% for perovskite/Si tandem (monolithic) [1] and 17.9% for perovskite modules [2]. These efficiencies have demonstrated PSCs as a promising candidate capable to compete in a silicon dominated solar cells market.  
The degradation mechanisms for PSCs are mainly caused by exposure to moisture, oxygen, heat, UV light, light soaking, mechanical stress and reverse bias [3, 4]. From all these, moisture is considered the most determining factor in the degradation of the MAPb\({}_{3}\) layer [4], mainly due to the hygroscopic nature of the amine salt [3, 5]. Additionally, the composition of perovskite can be affected by defects, which can significantly change its properties depending on the radiation and temperature [6, 7].  
However, the stability of the achieved PCE remains a key issue to be solved in order to assure the lifetime and the durability of these kinds of technologies. Several research lines have been addressed to enhance stability, such as introducing variations on the structure composition of the perovskites [8, 9, 10, 11, 12], the inclusion of new layers, such as hydrophobic films [13; 14; 15; 16], the replacement of the metallic electrode by carbon or a transparent conductive oxide to avoid problems with the hole transporter layer [17; 18; 19; 20] or using encapsulation methods in order to isolate and protect the device from environmental agents [4; 21; 22; 23].
# MOCA: A Network Intrusion Monitoring and Classification System
## 4 MOCA Design
### Feature Selection  
Feature selection techniques are divided into filter-based, wrapper-based, and embedded-based. In filter-based methods, someone selects features involving no ML algorithm. Filter methods are useful with respect to computing time. Wrapper-based methods employ a supervised learning algorithm to choose feature subsets. The two fundamental wrapper-based approaches adopted are Step Forward feature Selection (SFS) and Sequential Backward Selection (SBS). Embedded techniques incorporate optimal feature selection into an ML-based classifier.  
In this paper, the integration of filter and embedded methods was necessary to select potential features, called _pre-selected features_, to improve the effectiveness of searching for the optimal feature subsets. Information gain and correlation coefficient were used as filter-based methods. Decision Tree, Random Forest, and XGBoost were used as embeddedmethods to calculate the importance of the pre-selected features. From each approach, the top-20 important features were derived. Then, a majority vote approach was taken to derive the pre-selected features.  
After feature pre-selection, a feature-searching Algorithm 1 was proposed to select optimal feature subsets (\(f_{1}\) and \(f_{2}\)) for the two stages of MOCA. In this algorithm, pre-selected features are used to train a binary classifier. A SHAP TreeExplainer is used to derive important features of the model. Features with top-20 SHAP values are used as the starting features for the SBS method to find the minimum set of features. Each feature is deleted one at a time; classification accuracy is computed for all subsets with the remaining features, and the worst feature is discarded. To train the optimum model, cross-validation and hyper-parameter optimization played a role in bias reduction.  
```
Input: pre-selected features \(f_{pre}\), training dataset \(D_{train}\), two classifiers \(m_{1}\) and \(m_{2}\), and the number of features \((k_{1},k_{2})\) Output: trained \(m_{1}\) and \(m_{2}\) with feature subsets \(f_{1},f_{2}\)
1. Train a model \(m\) using \(D_{train}\) and \(f_{pre}\)
2. Explain \(m\) using shap.treeExplainer()
3. Derive top-20 features \(f_{20}\) according to SHAP values \(f_{1}=f_{2}=f_{20}\)
4. Train \(m_{1}\) using \(D_{train}\) and \(f_{1}\)
5. Train \(m_{2}\) using \(D_{train}\) and \(f_{2}\)
6. Search for the worst feature \(x^{-}\) using \(SBC(m_{1})\) and \(SBC(m_{2})\)
7. Update \(f_{1}=f_{1}-x^{-}\) and \(f_{2}=f_{2}-x^{-}\)
8. if\(|f_{1}|==k_{1}\) and \(|f_{2}|==k_{2}\)then
9. return\(m_{1}\), \(m_{2}\), \(f_{1}\), and \(f_{2}\);
10. end if
11. Go to 5;
```  
**Algorithm 1** Selecting \((k_{1},k_{2})\) features in MOCA.
###### Abstract  
Our goal was to develop and characterize a Natural Language Processing (NLP) algorithm to extract Eastern Cooperative Oncology Group Performance Status (ECOG PS) from unstructured electronic health record (EHR) sources to enhance observational datasets. By scanning unstructured EHR-derived documents from a real-world database, the NLP algorithm assigned ECOG PS scores to patients diagnosed with one of 21 cancer types who lacked structured ECOG PS numerical scores, anchored to the initiation of treatment lines. Manually abstracted ECOG PS scores were used as a source of truth to both develop the algorithm and evaluate accuracy, sensitivity, and positive predictive value (PPV). Algorithm performance was further characterized by investigating the prognostic value of composite ECOG PS scores in patients with advanced non-small cell lung cancer receiving first line treatment. Of N = 480,825 patient-lines, structured ECOG PS scores were available for 290,343 (60.4%). After applying NLP-extraction, the availability increased to 73.2%. The algorithm's overall accuracy, sensitivity, and PPV were 93% (95% CI: 92-94%), 88% (95% CI: 87-89%), and 88% (95% CI: 87-89%), respectively across all cancer types. In a cohort of N = 51,948 aNSCLC patients receiving 1L therapy, the algorithm improved ECOG PS completeness from 61.5% to 75.6%. Stratification by ECOG PS showed worse real-world overall survival (rwOS) for patients with worse ECOG PS scores. We developed an NLP algorithm to extract ECOG PS scores from unstructured EHR documents with high accuracy, improving data completeness for EHR-derived oncology cohorts.  
EHR; machine learning; ECOG PS; RWD; RWE; NLP; cancer diagnosis; natural language processing 2023
## 2 Materials and Methods
### Design of the Integrated Probe
Multiple thermocouple temperature rise tests were conducted for each frequency of the focusing probe, and a stable group was selected as a representative after multiple experiments, as shown in Figure 4.  
Figure 4: Protein heating rate treatment experiment: (**a**) experimental platform; (**b**) protein heating rate measurement results. Figure 3: PAA phantom treatment experiment: (**a**) physical pictures of treatment results; (**b**) damage measurement results of the PAA phantom. Based on the damage results of the protein phantom and the temperature rise results obtained by thermocouple testing, it was found that the higher frequency transducer had a better temperature rise rate, as shown in Figure 4b. However, due to the attenuation effect of the high-frequency probes during propagation with the frequency increases, the shape of the focal area changed from a cone to a sphere gradually, and the treatment depth also decreased significantly, as shown in Figure 4a. Therefore, for superficial varicose veins, it is appropriate to choose a high-frequency treatment probe. In this study, a 4.5 MHz treatment probe was selected considering both the heating rate and the treatment depth, which has a balanced therapeutic effect in the experiment, as shown in Figure 3a,b. Therefore, this study chose the 4.5 MHz focusing transducer to realize the fusion transducer's therapeutic function. On the other hand, as noted in Table 1, the central hole in the therapeutic probe results in significant sound pressure attenuation when there is water in the backing; the sound power measurement platform is shown in Figure 5. Consequently, the connection between the imaging probe and the therapeutic probe should be waterproofed.
## 4 Experimental Research
### Experimental Results and Analysis
#### 4.3.2 Comparative Experiments of Image Segmentation Modules  
The proposed image segmentation module was compared with several widely used semantic segmentation networks, including DeepLab [25], PSPNet, U-Net, and SegNet [26], using the same training dataset. All models were evaluated on a semantic segmentation task targeting coal-rock images. Due to space constraints, six representative samples are selected for qualitative comparison, as shown in Figure 12. From left to right, the figure displays: the original input image, the ground truth label, the segmentation result of the proposed model, and the results from DeepLab, PSPNet, U-Net, and SegNet, respectively. The proposed segmentation module demonstrates strong performance in handling low-quality coal-rock images from underground mining environments, accurately delineating the boundary between coal and rock seams. In contrast, the baseline models exhibit various deficiencies such as segmentation failures, misclassification, over-segmentation, and significant boundary inaccuracies, indicating limited robustness under challenging conditions. In Figure 12, the red regions denote coal seam areas, while black regions correspond to rock and background areas.  
The improvements in PSNR and SSIM achieved by the multi-scale luminance-enhancement module are particularly evident in low-illumination and high-reflectance scenarios, which are common in underground mining environments. In these cases, traditional enhancement methods (e.g., RetinexNet, RRDNet) tend to either over-enhance bright regions or suppress details in darker areas, leading to degraded visual quality. In contrast, our module maintains brightness uniformity and texture clarity, resulting in higher PSNR and SSIM scores.  
Similarly, the segmentation module yields notably higher MIoU and MPA scores, especially in cases with fuzzy coal-rock boundaries or partial occlusion by dust and water mist. These improvements indicate that the proposed model is more robust to visual degradation and better at preserving fine-grained spatial semantics. This translates to improved operational reliability in downstream tasks such as automatic shearer control or real-time coal-rock interface monitoring.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Framework** & **Retinexnet** & **RRDnet** & **Infrastructure** & **EnlightenGAN** & **Paper Model** \\ \hline PSNR & 15.010 & 17.056 & 16.087 & 20.078 & **21.288** \\ SSIM & 0.678 & 0.757 & 0.714 & 0.775 & **0.783** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results of enhanced images of coal rock.
## 3 Time-Efficient Method to Determine an Optimal Scaling Factor and the Encoder Resolution
### Dynamic Adjustment of the Number of Bits for the Encoder Resolution  
[PERSON] et al. [12] proposed a method to adjust the encoder resolution of the ECAP-L algorithm for acoustic echo cancellation applications. Nevertheless, this variant has not been applied for active noise control, for which the adaptive configuration is very different. To adjust the number of bits for the encoder in each iteration, the instantaneous value of the steady-state mean square error (see [6]) was used. Thus, the number of bits is adjusted as follows:  
\[b_{ij}(n)=\left\{\begin{array}{ll}min\{b_{ij}(n-1)+1,b_{max}\},&\eta_{ij}(n) <\epsilon_{1}^{2}(n),\\ b_{ij}(n-1),&\theta_{ij}(n)<\epsilon_{2}^{2}(n)\leq\eta_{ij}(n),\\ max\{b_{ij}(n-1)-1,1\},&\epsilon_{2}^{2}(n)\leq\theta_{ij}(n),\end{array}\right\} \tag{10}\]  
where \(b_{ij}\) is the number of bits for each adaptive filter and \(b_{max}\) is the maximum number of bits. In this way, at the beginning of the process, a large number of bits is used to encode the error, and thus the algorithm provides a fast convergence speed. After that, the number of bits is reduced as long as the residual error power is less than \(\theta_{ij}(n)\). Therefore, this mechanism guarantees a low computational cost since the FXECAP-L algorithm is not updated during all the iterations. The resolution of the encoder must be calculated in each iteration because the number of bits changes over the time, yielding a resolution \(Res_{ij}(n)\) for each adaptive filter.