esa-sceva/satcom-chunk-collection huggingface dataset:
# Modification of Insulating Properties of Surfaces of Dielectric High-Voltage Devices Using Plasma
## 7 Conclusions  
The proposed methodology for dielectric/insulator plasma surface treatment was repeatedly verified on sets of test samples. The repeatability and desired increase in the surface strength during the plasma treatment was demonstrated, together with the change in the intensity of the electric field on the surface of the sample at the post-treatment times; the effects and quantities were graphically represented. A parametric analysis of the plasma treatment was carried out in order to determine the maximum strength of the surface of the sample, \(E_{\rm p}\). The tests were repeated on other sets of samples, indicating the actual repeatability of the plasma treatment without the use of precursors. The procedures showed that the first treatment increased the parameters of the dielectric surface resistance (and thus the flashover resistance) within tens of percent for both the thermoset and the thermoplastic dielectrics.  
The benefit of the research rests in a systematic, technologically manageable exposure of the insulator surface. In the process, the surface strength increased by an industrially usable value (up to 50% of the surface resistivity), and the gradually improved setting of the atmospheric plasma discharge allowed changing the surface parameters with a proven time stability.  
At the next stages, our research will prominently involve applying precursors in plasma and evaluating their influence on the described traceable parameters of the dielectric, the aim being to refine the parameters of the surface properties of insulators.  
Conceptualization, P.F. and R.P.; methodology, R.P.; software, R.P.; validation, M.K., R.P. and P.F.; formal analysis, P.L. and R.P.; investigation, P.F.; resources, P.F. and P.L.; data curation, P.L. and R.P.; writing--original draft preparation, R.P.; writing--review and editing, P.F.; visualization, R.P. and P.F.; supervision, P.F.; project administration, P.F. and M.K.; funding acquisition, P.F. and M.K. All authors have read and agreed to the published version of the manuscript.  
The research was funded within the National Sustainability Programme, from a Czech Science Foundation grant (GA 20-14105S) and grants BD 2020-2022, FEKT-S-20-6360, BD 2023-2025, FEKT-S-23-8425.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
For the actual analyses and experiments, the infrastructure of the SIX Centre was utilized.  
Figure 17: Evaluation of the time stability of the first series of treated samples (\(N=8\)) of both types of plastics (epoxy-based, polymer-based).
## 3 Materials and Methods
### Feature Extraction  
To enhance the model's accuracy in classifying error categories, new features were derived from the original dataset through time-domain (TD) and statistical analysis [4; 7; 25; 26; 27; 28; 29]. This study used 15 TD features extracted in different combinations, to evaluate the F1-scores of models classifying error categories. Statistical analysis helped to determine the most significant features, ensuring that only relevant data points were used for training. By testing various combinations of these features, the study improved classification accuracy and optimized model performance, as evidenced by higher F1-scores, which balance precision and recall. Table 3 contains the details of the TD features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Batch. No.**} & \multicolumn{2}{c}{**Discharge**} & \multicolumn{2}{c}{**Operating Conditions**} \\ \cline{2-5}  & **Constant** & **Cut-Off** & **Nominal** & **Data Length** \\  & **Current** & **Voltage** & **Capacity** & **Data Length** \\ \hline \#5 & 2.0 A & 2.7 V & 2.0 Ah & 168 cycles \\ \#6 & 2.0 A & 2.5 V & 2.0 Ah & 168 cycles \\ \#7 & 2.0 A & 2.2 V & 2.0 Ah & 168 cycles \\ \#18 & 2.0 A & 2.5 V & 2.0 Ah & 132 cycles \\ \hline \hline \end{tabular}
\end{table}
Table 2: The NASA battery dataset (room temperature).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & **Feature Name** & **Formula** & **Description** \\ \hline
1 & Minimum (Min) & \(\min(x_{i})\) & The lowest amplitude observed in the signal. \\ \hline
2 & Maximum (Max) & \(\max(x_{i})\) & The highest amplitude recorded in the signal. \\ \hline
3 & Absolute Max (AbsMax) & \(\max(|x_{i}|)\) & The maximum absolute amplitude attained by the signal. \\ \hline
4 & Mean & \(x_{m}=\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i})\) & The mean amplitude of the signal over its duration. \\ \hline
5 & Standard Deviation (Std) & \(x_{std}=\sqrt{\frac{1}{N-1}\sum\limits_{i=1}^{N}(x_{i}-x_{m})^{2}}\) & This represents a measure of the dataset’s distribution, providing insights into spread and central tendency.  
\end{table}
Table 3: Details for time-domain features.
###### Abstract  
We report results on the electrochemical performance of flexible and binder-free \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fiber anodes for lithium-ion batteries (LIBs). The composite fibers were produced via centrifugal spinning and subsequent thermal processing. The fibers were prepared from a precursor solution containing PVP/iron (III) acetylacetonate/titanium (IV) butoxide/ethanol/acetic acid followed by oxidation at 200 \({}^{\circ}\)C in air and then carbonization at 550 \({}^{\circ}\)C under flowing argon. The morphology and structure of the composite fibers were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). These ternary composite fiber anodes showed an improved electrochemical performance compared to the pristine TiO\({}_{2}\)/C and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/C composite fiber electrodes. The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers also showed a superior cycling performance with a specific capacity of 340 mAh g\({}^{-1}\) after 100 cycles at a current density of 100 mA g\({}^{-1}\), compared to 61 mAh g\({}^{-1}\) and 121 mAh g\({}^{-1}\) for TiO\({}_{2}\)/C and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/C composite electrodes, respectively. The improved electrochemical performance and the simple processing of these metal oxide/carbon composite fibers make them promising candidates for the next generation and cost-effective flexible binder-free anodes for LIBs.  
centrifugal spinning; anode; lithium ion battery; \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\); TiO\({}_{2}\)
## 4 Case II: Active Eavesdropper
### Mean Square Error (MSE) Performance
#### 4.3.2 MSE at the Eavesdropper User E  
Next, Figures 17 and 18 show the MSEs of the SI channel \(\textbf{h}_{EE}\) and the wiretap channel \(\textbf{h}_{BE}\) versus \(SNR_{E}\) at the eavesdropper user E for various values of the self-jamming-to-noise ratio from user A, \(\rho_{AE}\). The self-interference-to-noise ratio at user E, \(\rho_{EE}\), is fixed at 30 dB. It can be clearly observed that user E cannot estimate the wiretap channel and the SI channel well, especially if there is a high self-jamming-to-noise ratio from user A, i.e., \(\rho_{AE}\) increases higher than 20 dB. So, the self-jamming signal provided by user A significantly influences the receiver side of user E, where user E cannot perform the wiretap channel estimation well in active mode, although user E also knows the pilot symbols. Moreover, the power of the combination of the self-jamming of user A and the SI component at user E is also higher than the power level of the intended message from user B. Therefore, the blind scheme without feedback and the semi-blind scheme without feedback, which are applied to user E, cannot estimate the channels well.
## 2 Related Works
The _integrated home automation system_ is the integration of personal wireless local area network (LAN) and Internet home automation system. In study [33] proposed an integration system with various communication technologies, including Internet, GSM, wireless radio frequency (RF), _etc._ In this system, the status of each device is monitored through wireless RF by a PC server, which links to the Internet, whereas users can also remotely control each device on/off by using the GSM protocol. However, the system architecture proposed in [33] requires high equipment cost, resulting in the low interest of buyers. In study [34] had proposed a home automation system based on wireless sensor networks. However, it requires a high-performance PC as a server and, hence, results in high equipment costs. [PERSON] _et al._[25] proposed a smart life application based on a low-power ZigBee wireless sensor network. This study built three types of devices that have ZigBee communication capabilities, such as lightning control, heating machine, and environmental security system. To build a gateway with Wi-Fi and ZigBee communication capabilities, this system uses a notebook to remotely access and control ZigBee devices, e.g., turning the lamp switch on/off, setting the heater temperature, and setting a feedback threshold to the temperature sensors for indoor safety. Users applying the application [25] are able to control ZigBee devices through the Internet, however, the ZigBee devices needs to be attached to a notebook or desktop computer for connecting to the Internet, increasing the construction cost. In this paper, the considered home appliances are certified by ZigBee Alliance. In fact, we suggest that all home appliances should be ZigBee-certified to guarantee all the ZigBee devices successfully communicating with each other, even the ZigBee devices are made by distinct manufacturers. In addition, the proposed IoT AP simultaneously supports ZigBee, Wi-Fi, and Internet communications because it has been integrated the firmware/hardware in a traditional Wi-Fi AP and the ZigBee protocol. It is a feasible solution upgrading an original Wi-Fi AP to an IoT AP. This paper also uses the IoT AP to construct a smart home application. For instance, the power meter and power switch can be connected to the proposed IoT AP, measured for the detailed power consumption, and turned on/off. This reduces the cost and helps developers prevent hardware revisions.
# Brewing with 10% and 20% Malted Lentils--Trials on Laboratory and Pilot Scales
## Results and Discussion
### Laboratory-Scale Brewing Trial
#### 3.1.2 Saccharification Time  
Saccharification occurred within the expected time range of 10-15 min for pale barley malt [39]. With the addition of 10% and 20% lentil malt, the saccharification time increased to 20-25 and 25-30 min, respectively. As the lentil malt was killed at a higher temperature (120 \({}^{\circ}\)C) compared to Pilsner-type barley malt (80 \({}^{\circ}\)C), increased saccharification time was expected, as higher temperatures decrease the overall enzymatic activity, including that of starch-degrading enzymes [40]. Moreover, enzymatic activity may decrease with the addition of lentil malt due to the low enzyme activity in lentils and the presence of amylase inhibitors [16; 41; 42]. As shown in previous research, word production with 100% lentil malt is only possible with exogenous enzymes when a product containing \(\alpha\)-amylase, \(\beta\)-glucanases and endopeptidase is used [34]. However, when using 10% and 20% lentil malt as an adjunct, the barley malt enzymes are sufficient to saccharify the mash in under 30 min. [PERSON] et al. [43] reported a saccharification time of 10 min with the addition of 30% lentil malt, but they included a pre-gelatinization step. After gelatinization, starch is already accessible for enzymatic breakdown and saccharification occurs faster.
# Boosting the Learning for Ranking Patterns
## 7 Discussion
### Results Analysis: Pros and Cons of the Proposed Approach  
According to our experimental study given in Section 6, the results highlight that AHPRank has superior scalability compared to the state-of-the-art RankingSVM in passive learning mode, particularly when the number of data points is high. Although RankingSVM has a high level of accuracy, it requires more training time, which makes it impractical for an interactive learning context. In active mode, our sensitivity-based heuristic was shown to be effective in selecting informative data points, and AHPRank was shown to be more robust than RankingSVM with respect to incorrect rankings.  
In summary, our research findings indicate that AHPRank offers an interesting trade-off between ranking accuracy and running time, making it a practical and efficient solution for various real-world applications. Its time complexity scales linearly with the training data size, and it can achieve high ranking accuracy in just a few seconds. Overall, our results suggest that AHPRank is a promising approach for ranking tasks.  
As with any ranking methodology, AHPRank has certain limitations that should be taken into consideration for further improvements:  
1. **Expert judgment bias:** AHPRank relies heavily on subjective expert judgment, which can introduce bias into the ranking process.
2. **Limited applicability:** AHPRank may not be applicable in all scenarios, such as cases where there are no clear criteria or when the criteria are too subjective.
3. **Data availability:** The accuracy of AHPRank depends on the availability and quality of data. If the data are incomplete or inaccurate, they may affect the ranking results.
4. **Complexity:** The AHP methodology can be complex to implement and interpret, which may make it difficult to use by non-experts.  
Figure 6: Learning under swaps (20 queries).  
Sensitivity to input parameters:AHPRank requires input parameters, such as criteria, which can be sensitive to changes and may require careful tuning for optimal performance.
## 6 Conclusions
### Managerial Implications  
The findings offer valuable insights for managers and decision-makers. Firstly, the results suggest that organizations can improve their performance by encouraging innovation, proactivity, and a willingness to take risks. In other words, companies should adopt an EO. It is worth mentioning that our research took the Miller/Covin and Slevin approach, so we argue that top management should encourage innovation, and incentive programs that recognize and encourage ideas could be an option. Likewise, promoting intrapreneurship initiatives stemming from these ideas is recommended. In terms of proactivity, actively seeking financing from traditional (such as bank loans or issuing shares) and non-traditional sources (like crowdfunding, forming strategic alliances with other companies, and angel investor networks, among others) is advisable. Regarding risk-taking, companies may explore investing in cutting-edge solutions offered by promising startups that leverage emerging technologies. Such investments could be a catalyst for ITC development.  
In line with the above, companies must develop a robust ITC. This process involves the synergistic interaction of three fundamental pillars. Firstly, strengthening IT management capabilities is achievable through the effective integration of emerging technologies in strategic planning and business decision-making processes. Secondly, the consolidation of IT technical capability requires continuous investment and the maintenance and periodic updating of advanced technological infrastructure. Finally, support for human capital entails hiring highly skilled personnel in the field of IT and continuously training and developing such personnel, both at the technical and managerial levels. These elements, collectively, form a comprehensive strategy that enhances the company's innovative and competitive capacity in the technological realm. Applying these strategies would enable companies to establish a synergistic relationship between EO and ITC, leading to improved firm performance.
## 6 Algorithms and Frameworks in Federated Learning
### Adaptive Learning Rates in Federated Contexts
#### 6.4.6 Explanation of Asynchronous Algorithms  
Asynchronous approaches in federated learning focus on flexibility and adaptability.  
* Staleness: Updates from slower clients may become outdated by the time they reach the central server, raising questions about how to weigh these older updates against newer ones.  
* Decentralized Aggregation: Unlike traditional FL, which aggregates client updates in batches after receiving all or most updates, asynchronous methods continuously update the model as new data arrive.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 4 Temperature Compensation  
Based on the echo compensation Formulas (11) and (12) from Section 2 and the hardware design of the calibration equipment from Section 3, we further discuss the acquisition of temperature compensation data.  
To compensate for the drift and offset of the in-orbit radar instrument caused by ambient temperature changes, ground temperature tests need to be performed to obtain temperature compensation data. Combining with the satellite temperature telemetry values, temperature compensation is completed during the data processing process. The calibration accuracy after compensation is mainly determined by the temperature telemetry accuracy and test repeatability error. During the ground temperature test, the temperature characteristics of the internal calibrator, antenna calibration networks, and through-cabin coaxial cables are calibrated separately using a temperature test chamber to obtain their respective phase-temperature curves and amplitude-temperature curves, further realizing the compensation of temperature drift and offset of the radar instrument.
## Results and Discussion
### Oxygen Sensing Properties  
Typical oxygen sensing using micelle **M3** as a representative example was given in Figure 5. With the increase of oxygen concentration, the emission of PtTFPP at 650 nm decreased, showing the oxygen sensitivity of the sensors (Figure 5a). The intensity ratio (I\({}_{0}\)/I) curves showed in Figure 5b followed the Stern-Volmer equation:  
\[\frac{I_{0}}{I}=\ 1\ +\ \text{K}_{SV}\ [O_{2}] \tag{2}\]  
where K\({}_{\text{SV}}\) is the Stern-Volmer quenching constant, and [O\({}_{2}\)] is the dissolved oxygen concentration adjusted through the nitrogen and oxygen gas mixer. Linear Stern-Volmer curves were observed, and the curves were not affected much by the polymer structures (Figure 5b). The limits of detection (LOD) for oxygen were found to be 0.07, 0.09, and 0.04 mg/L, for **M1**, **M2**, and **M3**, respectively by using three folds of standard errors of K\({}_{\text{SV}}\) divided by K\({}_{\text{SV}}\). The temperature has some influences on sensitivity. Figure 5c gave the Stern-Volmer curves of **M3** at 37 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, respectively. Slightly faster KSV at 37 \({}^{\circ}\)C than that at 25 \({}^{\circ}\)C was observed due to the higher interaction activity of oxygen molecules with PtTFPP at higher temperatures.  
Figure 4: Photophysical properties of PtTFPP in micelles, tetrahydrofuran (THF), and dichloromethane (DCM) solutions.  
The changes in the lifetimes of PtTFPP in micelles were also measured at room temperature under nitrogen and air conditions (Figure 6). Under air condition, the three micelles' lifetimes are 7.64 to 7.93 \(\upmu\)s for **M1**, **M2**, and **M3**, which were slightly affected by the micellar structures. Under nitrogen conditions, the differences of lifetimes among the three micelles are much more obvious; those are 23.65, 28.02 and 35.98 \(\upmu\)s for **M1**, **M2**, and **M3**, respectively.  
Figure 5: (**A**): Oxygen responses of the micelles excited at 405 nm; (**B**): Stern–Volmer plots of micelles **M1–M3** by using the oxygen probe’s emission intensities at 650 nm; (**C**): Stern–Volmer plots of micelles **M3** at 25 \({}^{\circ}\)C and 37 \({}^{\circ}\)C. I\({}_{0}\) is the intensity under nitrogen; I is the intensity under oxygen.
###### Abstract  
Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and \(F_{1}\)-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs.  
Keywords:feature engineering; intensive care unit; mortality prediction; hard-margin support vector machines +
Footnote †: journal: Applied Soft. 2019, 9, 3525; doi:10.3390/app9173525
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Tracking Jitter
#### 4.2.3 Boc(2,1)  
Figure 44 shows the tracking jitters of simulated S-curves for the BOC(2,1) signal of infinite bandwidth, which is similar to the BPSK signal case. The results of the 8.184-MHz bandwidth case are shown in Figure 45. The code tracking error variance of the proposed S-curve is much better than the HEU S-curve, because of the closely-spaced correlator positions and large correlator weights in the [14] discriminator. By contrast, the amplitudes of the local reference waveform in our manuscript are relatively low, and it has a narrower distribution.  
Figure 44: The tracking jitters for the S-curves of the BOC(2,1) signal (infinite bandwidth).  
Figure 43: The tracking jitters for the S-curves of the BOC(1,1) signal (6.138-MHz bandwidth).