esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 3 Control Strategies of the DBI
### Non-Linear Controls
#### 3.3.2 Flatness-Based Control  
For the dual-boost inverter, the control strategy based on differential flatness theory was presented in [14]. A more detailed review of the main approaches can be found in [14; 29; 30].  
This control strategy for the DBI considers an individual control for each boost converter. In such a scenario, the control scheme is composed by two cascaded control loops, as seen in Figure 10. As explained above, the PV voltage error is regulated using a PI controller whose output is the active power reference \(P^{*}\), which is employed to calculate the output voltage reference of each boost converter. The same analysis, presented in the linear control strategy to calculate the output voltage reference, was used in this case. The next control loop (inner control loop), based on flatness theory, regulates the output voltage of each boost converter, which is indirectly accomplished through the regulation of the energy stored in each output capacitor.  
To analyze the flatness propriety of the DC-DC converter, the chosen candidate flat output, in this case, the stored energy through the converter [29; 31], is written as  
\[y_{1}=\frac{1}{2}L_{1}i_{\mathrm{L1}}^{2}+\frac{1}{2}C_{1}v_{\mathrm{c1}}^{2 }=\phi(i_{\mathrm{L1}},v_{\mathrm{c1}}) \tag{20}\]  
The flatness theory was verified through the following conditions:  
\[i_{\mathrm{L1}} =\varphi_{i_{\mathrm{L1}}}(y_{1},y_{1}) \tag{21}\] \[v_{\mathrm{c1}} =\varphi_{v_{\mathrm{c1}}}(y_{1},y_{1})\] (22) \[d_{1} =\psi_{1}(y_{1},y_{1},y_{1}) \tag{23}\]  
All variables can be expressed as a function of the flat output and its derivatives without solving any differential equations. Then, the system is flat, and it is possible to indirectly control all the variables through a control of \(y_{1}\).  
To ensure reference tracking, a third-order control law was proposed:  
\[k_{1}(y_{1}^{*}-y_{1})+k_{2}\int(y_{1}^{*}-y_{1})dt+k_{3}(y_{1}^{*}-y_{1})+k_{ 4}(y_{1}^{*}-y_{1})=0 \tag{24}\]  
Figure 10: Control scheme of the flatness-based control for DBI.  
The constants \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\) are calculated as follows:  
\[k_{1} =2\xi\omega_{n}p+\omega_{n}^{2}\] \[k_{2} =p\omega_{n}^{2}\] \[k_{3} =2\xi\omega_{n}+p\] \[k_{4} =1\]  
where \(\xi\) is the damping factor, \(\omega_{n}\) is the pulsation, and \(p\) is the value of one pole.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 6 Interfacial Activation of Lipase on a Hydrophobic Porous Support  
The success of lipase adsorption on a hydrophobic support depends on the structural domain called lid [57; 90]. The lid contributes to providing an open and closed configuration to the enzyme [83; 91]. In the closed conformation, hydrophobic residues face toward the catalytic site (inside the lid), thus preventing substrate binding [91]. The open conformation of lid occurs in the presence of a hydrophobic interface. The catalytic site is exposed outside the lid and bound to the substrate, which causes the phenomenon known as \"interfacial activation\" [91; 92].  
The interfacial activation of lipase occurs, when the enzyme is adsorbed on the hydrophobic surface and causes open conformation lipase [65; 93]. The reversible mechanism of immobilization permits reusing supports after enzyme inactivation [92; 93]. However, it should be considered, as already described in other publications, that this reduces the range of applicable cosolvent concentrations and even some substrates or products may produce the release of the enzyme [92]. Simple strategies to solve this problem have been proposed, enlarging the application range of this immobilized enzyme [82; 88; 93]. The interfacial activation of lipase on a hydrophobic support is shown in Figure 2.  
Figure 2: Scheme of immobilization by interfacial activation on a hydrophobic support.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis
### Optimization of Actuation Speed Parameters
#### 3.5.2 Performance Analysis  
Results shown in Figure 9 show that the crawling distances increased with actuation speed, peaking at the highest speed ratio. The 10/10 condition yielded 55 mm (5 cycles), averaging 11 mm per cycle. The 15/15 condition achieved 71 mm, averaging 14.2 mm per cycle. The 20/20 condition reached 91 mm, averaging 18.2 mm per cycle, while the 30/30 condition recorded 95 mm, averaging 19 mm per cycle. These results show that speed also impacts dynamic engagement rather than static friction anchoring force \(F_{\text{prop}}\). This corresponds to a predicted per-cycle distance of \(\sim\)19 mm (based on the model's scaling with \(F_{\text{prop}}\)), aligning with the observed 19 mm per cycle for 30/30.  
These trends suggest that higher actuation speeds promote more complete deployment of the kirigami structures during each stroke, improving vertical pop-up height and surface  
Figure 9: Crawling distance over time for different actuation speeds with corresponding actuation cycles shown as dashed lines.  
engagement. In contrast, lower speeds result in slower, incomplete deployment, limiting anisotropic friction and reducing overall locomotion performance. To model this effect, a deployment factor was introduced--defined as the fraction of the actuator's stroke effectively converted into forward crawling. At 10 mm/s, the deployment factor is \(\sim\)37%, consistent with the observed 11 mm stride. As speed increases, the factor improves to \(\sim\)47%, 61%, and 63% at 15, 20, and 30 mm/s, respectively, aligning closely with experimental results. At higher speeds (20-30 mm/s), the actuator imparts greater momentum, allowing the kirigami units to fully buckle and anchor. This enhances anisotropic friction, resulting in more effective energy transfer and improved mechanical efficiency. These findings confirm that faster actuation cycles enable more complete kirigami engagement, thereby maximizing traction and stride length.
# Two-Terminal Electronic Circuits with Controllable Linear NDR Region and Their Applications
## 5 Modeling the Negative Differential Resistance  
We find the NDR at the operating point as follows:  
\[R_{diff}=\bigg{(}\frac{dI_{1}}{dV_{1,2}}\bigg{)}^{-1}. \tag{18}\]  
Let us determine \(R_{diff}\) for the two-terminal NDR circuits shown in Figures 3-5. Substituting current \(I_{1}\) from (3), (5), and (7) into (18) and taking the first derivative of function \(I_{1}\) concerning variable \(V_{1,2}\), we obtain the following equations for the NDR:  
\[R_{diff}=\bigg{[}\frac{dI_{D}}{dV_{1,2}}\bigg{(}\frac{(m+1)}{1+4/h_{FE}+2/h_{FE }^{2}}+\frac{R_{d}}{R_{c}+R_{d}}\bigg{)}+\frac{1}{R_{c}+R_{d}}\bigg{]}^{-1}, \tag{19}\]  
for the two-terminal NDR circuit with an MWCM,  
\[R_{diff}=\bigg{\{}\frac{dI_{D}}{dV_{1,2}}\bigg{[}(m+1)\bigg{(}1-\frac{2}{h_{ FE}^{2}+2h_{FE}+2}\bigg{)}\bigg{(}1+\frac{V_{CE2}-V_{CE1}}{V_{A}}\bigg{)}+ \frac{R_{d}}{R_{c}+R_{d}}\bigg{]}+\frac{1}{R_{c}+R_{d}}\bigg{\}}^{-1}, \tag{20}\]  
and, for the two-terminal NDR circuit with a MIWCM,  
\[R_{diff}=\bigg{\{}\frac{dI_{D}}{dV_{1,2}}\bigg{[}(m+1)\bigg{(}1-\frac{2}{h_{ FE}^{2}+2h_{FE}+2}\bigg{)}+\frac{R_{d}}{R_{c}+R_{d}}\bigg{]}+\frac{1}{R_{c}+R_{d} }\bigg{\}}^{-1}. \tag{21}\]  
The derivative \(dI_{D}/dV_{1,2}\) in (19)-(21) cannot be derived analytically. Therefore, we can replace this derivative with the ratio of \(\Delta I_{D}/\Delta V_{1,2}\) in the vicinity of the operating point. Then, calculate the increment of current \(\Delta I_{D}\) by (12) for a JFET and by (17) for a MESFET, HEMT, or PHEMT.
## 4 Case Study for SU Allocation in 0.4 kV PTLs According to the Developed Methodology
### Payback of Capital Investment in SU Installation  
According to the technical reports of power grid company, the estimated time of emergency power supply outages during a year is 3.59 h; however, as it has been explained in the particular case, the actual power supply outage time is 24% higher than the estimated, since the methodology does not take into consideration the time to obtain information on a failure. Thus, the actual power supply outage time will increase up to 4.44 h per year, whereby it will be 1.78 h/year for stable failures and 2.66 h/year for unstable failures. The damage caused by electricity undersupply for the entire transmission line will be USD 936.98 per year. From the calculations described above, it is shown that the installation of SU with automatic reclosing function will enable us to reduce PS restoration time by 1.44 times in case of stable failures and by 221.7 times in case of unstable failures. Thus, the installation of sectionalizing unit in Mezenka electrical network allows reducing power supply outage time for stable failures from 1.78 h/year to 1.24 h/year and, for unstable failures, from 2.66 h/year to almost zero (0.01 h/year). In total, power supply outage time during a year after SU installation will amount to 1.25 h. The damage caused by electricity undersupply in such a case will be USD 263.79 per year, that is, 3.6 times less than in the original case (without sectionalizing units), and the effect of sectionalization (\(E_{s}\)) will equal to USD 673.19 USD/year. Since the capital cost of SU implementation is 1467 USD/year and operating cost of servicing SU is 90.64 USD/year, annual economic effect from SU installation (Equation (13)) will be:  
\[G\ =\ 673.19-(90.64+0.125\cdot 1467)\cdot 1\ =\ 399.1\ \text{USD/year}\]  
As \(G\geq 0\), SU installation in Mezenka electrical network will pay off.  
The payback of capital investment in SU installation within the Mezenka electrical network will be:  
\[P_{SU.}\ =\ C_{cap}/(E_{S}-C_{ep})\ =\ 1467.00/(673.19\ -\ 90.6464)\ =\ 2.52\ \text{years}\]  
A payback period of 2.52 years in the considered case demonstrates remarkable effectiveness of SU installation with automatic reclosing function in the Mezenka electrical network.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 3 Results
### LM Patterning with a UV-Curable Polymeric Membrane  
The sprayed LM thin film can be patterned with UV-curable polymeric masks. The polymeric mask was fabricated by replication from PDMS molds, as introduced in a previous report [22]. The process is shown in Figure 6. A PDMS master mold was prepared, and UV-curable PUA polymer was dropped on the mold. A flat PDMS piece was placed over the PUA and pressed into contact with the lower PDMS mold. The UV-curable PUA polymer was then exposed under a UV lamp for 3 min. Finally, the patterned PUA membrane can be peeled from the mold.  
This UV-curable polymeric mask enables us to fabricate minute perforated patterns on the film. In this work, a 2 cm \(\times\) 2 cm PUA membrane with 150-\(\upmu\)m-diameter holes was fabricated (Figure 7a). The PUA mask is slightly sticky, allowing it to conformally contact a smooth glass or plastic substrate. In Figure 7b, the PUA polymeric stencil is placed on a glass microscope slide and the inset is the magnified image of the holes. LM was then uniformly sprayed on the substrate (Figure 7c), and the stencil was carefully peeled off, leaving LM droplets on the glass substrate (Figure 7d). The magnified image of Figure 7d is shown in Figure 8a. LM has a high surface tension, which can make it difficult to separate the shadow mask from the substrate. Thus, there is a limit on the resolution of the LM (\(\sim\)100 \(\upmu\)m) when it is patterned using the shadow mask method. Improving the contact of the LM to the substrates could help increase the resolution.  
Figure 6: Illustration of the fabrication process for UV-curable polymeric membrane.  
Figure 7: (**a**) UV-curable polymeric membrane (2 cm \(\times\) 2 cm) and (**b**) the membrane attached to a glass substrate. (**c**) Galinstan was sprayed on the polymer membrane and (**d**) the LM dots pattern remained on the glass substrate after peeling off the membrane.  
The sprayed LM patterns are shown in Figure 8. In Figure 8a, LM dots with a diameter of 150 \(\upmu\)m were transferred to the glass substrate. Each LM pattern has a hemispherical feature because of the high surface tension of Galinstan. Using the PUA mask, it is difficult to fabricate LM patterns smaller than 150 \(\upmu\)m. The oxidized surface of the LM makes it difficult to separate it from the polymeric membrane. If the thickness of the LM film is smaller than the feature size, LM can be left on the glass substrate. However, when the size of the pattern is too small, the polymeric membrane can lift all the LM from the surface as the oxidized LM surface sticks to the mask. However, it is easy to create larger patterns, such as a 2-mm letter \"C\" (Figure 8b) and a simple electrode pattern (Figure 8c). The LM in the letter \"C\" is 400 \(\upmu\)m wide, and the width of the electrode in Figure 8c is 500 \(\upmu\)m.
## 2 Related Works
Their study implemented several models and reported that the Support Vector Machine (SVM) achieved a coefficient of determination (R\({}^{2}\)) of 0.9000, indicating high predictive accuracy for remaining useful life (RUL) estimation. The work demonstrated the potential of SVM in capturing nonlinear degradation patterns and emphasized the importance of feature selection and preprocessing in enhancing model performance. [PERSON] et al. [10] developed a hybrid machine learning framework for predicting the remaining useful life (RUL) of lithium-ion batteries by integrating Harris Hawks Optimization (HHO) with traditional ensemble models. Their method focused on optimizing feature selection for improved model performance. When optimized with HHO, the Random Forest model achieved a high R\({}^{2}\) score of 0.979 and a Mean Squared Error (MSE) of 2148.86, demonstrating strong predictive capability and robustness. The study highlights the benefits of combining nature-inspired optimization algorithms with ensemble learning for battery health prognostics. [PERSON] et al. [11] explored the use of multiple machine learning models for predicting the remaining useful life (RUL) of lithium-ion batteries, using real-world data from the Hawaii Natural Energy Institute (HNEI). Among the models evaluated, the Random Forest algorithm delivered the most accurate results, achieving an R\({}^{2}\) score of 0.98 and a Mean Squared Error (MSE) of 14.1186. Their study emphasized the value of battery parameter analysis, such as voltage, current, and time, and highlighted Random Forest's robustness in handling complex degradation behavior across different batteries. Electrified transportation systems are rapidly developing across the globe, thereby assisting in the reduction of carbon emissions and facilitating efforts to mitigate the impacts of global warming. Practical applications are increasingly realizing the forecast of battery remaining usable life (RUL) as a strategy to reduce maintenance costs and improve system dependability and efficiency [12]. Optimizing performance and guaranteeing sustainable energy solutions depend on an awareness of and prediction of lithium-ion batteries' life cycles, given their significance in many technological uses [13]. Because lithium-ion batteries are so widely used as energy sources in numerous industrial equipment, including automated guided mobile cars or vehicles, satellite-powered drones, and battery electric vehicles (EVs), predictive maintenance (PdM) of these batteries has attracted major interest over the years [14].
## 2 Materials and Methods  
The study was designed to be cross-sectional and observational. The procedures were retrospective and agreed with the Declaration of Helsinki. CT images and data of ccRCC patients were acquired at multiple centers and retrieved from The Cancer Imaging Archive (TCIA) [15; 16; 17]. The TCIA project received the approval of the Institutional Review Board. This subsequent retrospective analysis was on the publicly available, anonymized data and did not require further review due to previous protections implemented by TCIA. All the subjects enrolled signed a written informed consent.  
Between November 2019 and February 2020, a total of 267 consecutive patients were assessed and selected, through medical history and CT images, following the exclusion criteria. All the patients received a histologically proven diagnosis of ccRCC.  
We decided to use CT in this study over other imaging modalities because it enables the highest spatial resolution making it the best modality for the evaluation of tiny peritumoral collateral vessels.  
The selection of ccRCC patients was carried out using the following exclusion criteria: patients with non-Caucasian ethnicity, female patients, patients who had undergone magnetic resonance examination only, patients who had undergone CT without administration of contrast medium, patients who had undergone chest CT only, patients with previous renal ablation, nephrectomized and heminephrectomized patients, cirrhotic patients with collateral vessels, patients with congenital solitary kidney, patients with narrow field of view (FOV) images that did not allow SAT to be quantified, and patients who had undergone upper abdomen CT in which collateral vessels were not entirely visible.  
Patients with ccRCC were classified into two groups: absence of collateral vessels (ccRCCa) and presence of collateral vessels (ccRCCp). Furthermore, two subanalyses were performed with patients grouped according to their history of previous malignancy or previous neoadjuvant treatment.
## 7 Discussion
Analysis of Figures 17-19 shows that noise intensity significantly affects the surrogate model's prediction accuracy, with a clear decline as the noise standard deviation increases. Notably, even when the noise standard deviation reaches 3% of the true amplitude and the number of target domains is reduced to just four, the surrogate model developed in this study maintains high prediction accuracy. The prediction MAE and RMSE are both maintained at \(10^{-5}\) across the whole parameter space, while \(R^{2}\) achieves values of 0.927 or higher across the parameter plane (Figure 19g-i). These results emphasize the model's robustness and reliability, demonstrating its ability to provide accurate predictions even with significant noise and limited target domain data. This performance demonstrates the model's practical applicability in real-world scenarios where data quality and quantity may be limited. Figure 19: The impact of different migration paths on the prediction accuracy of the proposed surrogate model illustrated through three evaluation metrics, MAE, RMSE, and \(R^{2}\). The noise standard deviation is 3% of the true value’s amplitude. In Section 2, our theoretical analysis identifies a demarcation curve on the \(c-v\) parameter plane, where the beam's vibration responses vary in form across this boundary. When all target domains lie below this curve (Figure 16c), the surrogate model's prediction accuracy exhibits marked differences above and below the boundary, particularly at low noise levels. This suggests that classifying the solutions to the governing equations can inform the selection of optimal migration paths, thereby boosting the surrogate model's overall prediction accuracy.
## 3 Results and Discussion
### Comparison of Volatile Aroma Compounds during AAO and ABA Production  
Gas chromatography-mass spectrometry was performed to further understand the molecular basis of our sensory results. Flavor is composed of taste and aroma, with volatile aroma compounds contributing to the aroma, thereby ultimately contributing to the flavor. The experiment identified a total of 9 volatile compounds that were produced during the fermentation from rice to amazake (Figure 3C). These included 2 alcohols (2-methyl-furan and 2,3-butanediol), 2 aldehydes (3-methyl-butanal and 2-methyl-butyraldehyde), 1 ketone (acetoin), 1 carboxylic acid (isobutyric acid), and 1 carboxylic ester (diallyl ester oxalic acid).  
The most abundant compounds in the AAO fermentation compared to ABA were 2-methyl-butyraldehyde (which has a cocoa, musty, green, and malty aroma), 3-methyl-butanal (chocolaty, peachy aroma), and diallyl ester oxalic acid (flavoral aroma). The greater abundance of these specific compounds aligns with earlier research [42], which showed that during koji fermentation on wheat and soybean, _A. oryzae_ produces a greater abundance of aldehyde compounds compared to _B. amyloliquefaciens_.  
Conversely, the most abundant compounds in the ABA fermentation compared with AAO were isobutyric acid (which has a sour, cheesy, dairy-like, buttery, and rancid aroma and contributes to the characteristic smell of many dairy products), 2,3-butanediol (fruity, creamy, buttery; used as a flavoring agent, common in other fermented foods and beverages), and acetoin (sweet, buttery, creamy, and dairy-like, used as a flavor enhancer in butter, cream, and dairy-flavored products). The greater abundance of these specific ketones and alcohols in the ABA fermentation aligns with earlier research [42], which demonstrated that during koji fermentation on wheat and soybean, _B. amyloliquefaciens_ exhibits a greater abundance of these compounds. However, our study diverged by detecting isobutyric acid, a compound not found in the earlier research.  
Overall, the volatile aroma profile helps explain our sensory results from the CATA test, which described the AAO to be tarter and fruitier and ABA to have more nutty and yeast-like aromas. It should be noted, however, that aroma is a complex concept determined by combinations of many compounds. Additionally, this experiment measured volatiles that developed during the fermentation only, and did not include volatiles that were already present in the rice or koji at the start of the fermentation at 0 h. This could help explain any other aromas detected by the sensory panel, such as the nutty flavors associated with ABA.
## 3 Materials and Methods
### Data Preprocessing  
This research utilizes the CSE-CIC-IDS2018 dataset, a comprehensive collection comprising 16,232,943 instances and 80 features, including seven attack families, fifteen attack types, and diverse network traffic patterns. This dataset is ideal for evaluating IDS models as it accurately mirrors real-world network activities and potential attack situations, offering a solid basis for model development and evaluation. The dataset undergoes four carefully executed preprocessing steps to prepare it for analysis. Data integration is achieved by combining ten separate CSV files with the pd.concat() function to form a single dataset, enabling smooth analysis. Feature encoding is used to standardize attributes such as IP addresses and timestamps, ensuring consistency and uniformity throughout the dataset. Next, normalization is applied using Min-Max scaling to bring all features within the [0, 1] range, ensuring a consistent scale and improving the performance of machine learning algorithms by reducing the effects of varying feature magnitudes. The dataset is split into training and testing sets using the split_data() function, with a 70:30 ratio, to assess the model's performance on unseen data effectively. To address the issue of class imbalance, a balanced subset of the original dataset was constructed by applying stratified random sampling, ensuring an equal number of normal and attack traffic instances. This approach facilitates fairer model training and evaluation, avoiding bias towards the majority class. These preprocessing steps improve the dataset's quality, ensuring its effectiveness for intrusion detection tasks, optimizing the accuracy and efficiency of the IDS framework in detecting malicious activities, and enhancing its scalability to handle larger and more diverse datasets.