esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Synthetic Hydrogels
#### 2.1.3 Polyvinyl Alcohol  
Poly(vinyl alcohol) (PVA) is a water-soluble, bio-degradable synthetic polymer that has a chemical formula of [CH\({}_{2}\)CH(OH)]\({}_{\text{n}}\) and is commercially available in a wide range of molecular weights from a few thousands to over a hundred thousand; in addition, it has been widely adapted in biomedical applications since the 1970s for cartilage repair and wound dressing [38,39,40]. PVA is conventionally synthesized from vinyl acetate via free radical polymerization followed by hydrolysis of an acetate moiety into a hydroxyl group [27,41]. Hydroxyl group side groups in PVA have major impacts on rheological properties and mechanical strength of the gels, and it also possesses the capability of forming hydrogen bonding with neighboring molecules [38,42,43].  
PVA hydrogels are formed via various mechanisms including hydrolysis, alcoholysis, or physically induced chain aggregation like the freeze-thaw method. PVA polymers exhibit excellent biocompatibility, especially against blood and tissue cells and high thermal stability. However, low bioactivity and weak mechanical stability limit the applications of PVA hydrogels in biomedical fields, especially for regenerative therapies [44, 45, 46, 47].
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Predictive Performance of the Models Based on Area Under the Curve (AUC)  
The AUC represents the predictive performance of ML models, which are summarized in Table 5. The highest AUC was reported for the Random Forest model at three-years follow-up (0.89). The lowest AUC was reported for the Decision Tree model at one-year follow-up (0.59).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Follow Up** & **ML Model** & **Sensitivity** & **Specificity** \\ \hline \multirow{8}{*}{Sheng 2020 [30]} & \multirow{8}{*}{1 year} & Adaptive Boosting & 72.33 & 93.45 \\ \cline{3-4}  & & Decision Tree & 63.52 & 97.45 \\ \cline{2-4}  & & Gradient Boosting & 67.92 & 97.97 \\ \cline{2-4}  & & k-Nearest Neighbor & 52.2 & 98.89 \\ \cline{2-4}  & & Linear Discriminant Analysis & 82.81 & 37.76 \\ \cline{2-4}  & & Light Gradient Boosting & 75.68 & 96.86 \\ \cline{2-4}  & & Logistic Regression & 81.76 & 37.58 \\ \cline{2-4}  & & Random Forest & 70.02 & 94.86 \\ \cline{2-4}  & & XGBoost & 78.62 & 96.92 \\ \hline \multirow{8}{*}{[PERSON] 2023 [32]} & \multirow{8}{*}{1 year} & Logistic Regression & 4.4 & 96.7 \\ \cline{2-4}  & & Decision Tree & 15.6 & 98.3 \\ \cline{2-4}  & & Random Forest & 0 & 100 \\ \cline{2-4}  & & Gradient Boosting & 2.2 & 99.9 \\ \cline{2-4}  & & XGBoost & 4.4 & 96.7 \\ \cline{2-4}  & & Logistic Regression & 24.3 & 91.6 \\ \cline{2-4}  & & Decision Tree & 28.6 & 86.5 \\ \cline{2-4}  & & Random Forest & 9.6 & 99.2 \\ \cline{2-4}  & & Gradient Boosting & 17.1 & 97 \\ \cline{2-4}  & & XGBoost & 17.5 & 97 \\ \hline \multirow{4}{*}{[PERSON] 2012 [34]} & \multirow{2}{*}{7–12 months} & Logistic Regression & 65 & 75 \\ \cline{2-4}  & & 13–18 months & Logistic Regression & 69 & 74 \\ \cline{2-4}  & & 19–24 months & Logistic Regression & 58 & 77 \\ \hline [PERSON] 2022 [35] & \multirow{2}{*}{3 months} & XGBoost & 70.3 & 79.1 \\ \cline{2-4}  & & Support Vector Machine & 66 & 70 \\ \cline{2-4}  & & Logistic Regression & 69 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity and specificity of ML models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Follow Up** & **ML Model** & **AUC** & \multicolumn{2}{c}{**95\% CI**} \\ \cline{3-5}  & & 6 months & 0.7 & 0.68 & 0.72 \\ \cline{2-5}  & 1 year & Random Forest & 0.73 & 0.72 & 0.75 \\ \cline{2-5}  & 2 years & & 0.73 & 0.71 & 0.74 \\ \cline{2-5}  & 6 months & & 0.69 & 0.67 & 0.71 \\ \cline{2-5}  & 1 year & Logistic Regression & 0.71 & 0.7 & 0.73 \\ \cline{2-5}  & 2 years & & 0.69 & 0.67 & 0.7 \\ \hline \multirow{2}{*}{[PERSON] 2023 [29]} & \multirow{2}{*}{3 years} & XGBoost & 0.8 & NR & NR \\ \cline{2-5}  & 3 years & Logistic Regression & 0.75 & NR & NR \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the predictive performance of ML levels based on the AUC and 95% CI.
# Coenzyme Q at the Hinge of Health and Metabolic Diseases
## 3 CoQ as Metabolic Integrative Factor for Cellular Homeostasis
### Mitochondrial Redox Reactions
#### 3.1.2 CoQ Role in Reactive Oxygen Species Balance  
Eleven sites in the ETC that leak electrons to oxygen producing ROS have been associated with substrate oxidation and oxidative phosphorylation in mitochondria [46]. These sites are located inside the oxoacid dehydrogenases that feed electrons to NAD\({}^{+}\), but higher production has been recorded in proper mitochondrial complexes III, I, and II in the sites where CoQ presents its redox activity [45]. In addition, the rate of ROS production can vary depending on the tissue, showing higher production in brown adipose tissue compared with skeletal muscle, and being heart the organ with lower rates [47]. The type of substrate is also crucial, succinate is the metabolite that causes higher production of ROS, and both fatty acids and amino acids the metabolites that produces the lower levels [46]. Most of the hydrogen peroxide production takes place in complex I, while glycerol-3-phosphate, fatty acids and glutamate/malate generate an equal amount in complexes I and III, and inside the proper glycerol-3-phosphate dehydrogenase. ROS is also produced by 2-oxoglutate dehydrogenase.  
Due to the topology of the mitochondria and the presence of a double membrane, these enzymes can generate superoxide either in the intermembrane space or in the matrix, but hydrogen peroxide exclusively in the matrix. The specific site of ROS production is important for redox signaling without generating catastrophic energetic effects [45]. Thus, we can distinguish intramitochondrial redox signaling, in which all targets are located inside mitochondria, and redox signaling from mitochondria to the rest of the cell (retrograde redox signaling). This ROS-dependent signaling process can even affect targets located outside cells. Among the targets affected by mitochondrial ROS are the initiation of hypoxia-inducible factor (HIF) signaling and its effect in gene expression modulation, insulin secretion stimulated by redox signaling due to the metabolism of branched-chain keto acids and fatty acids, retrograde redox signaling that modulates PGC1\(\alpha\) during exercise in skeletal muscle, and the redox signaling that affects immune cells [48; 49].
## 5 Outlook and Perspective  
In this research, the influence of materials (Al and brass) and bio-inspired sawtooth riblets on cavitation-induced erosion morphology, depth, and cross-sectional area has been investigated using experimental approaches. The main findings of this study reveal that erosion depth and cross-sectional area are reduced due to the effects of bio-inspired sawtooth riblets. Additionally, brass demonstrates greater erosion resistance compared to Al, with a 7.9% reduction in depth and a 27.4% reduction in erosion width, compared to a 3.8% reduction in depth and an 18.3% reduction in erosion width for Al. This is attributed to the greater mechanical stability of brass. Moreover, results show that the width of erosion perpendicular to the riblet lines decreases more than that for erosion parallel to the riblet lines. These findings are attributed to the physical properties of the materials and the passive erosion control method provided by the riblets. However, in seawater, which represents the primary application of anti-erosion materials for ship and submarine propellers and hulls, materials are exposed to a cathodic environment that leads to corrosion [43; 44; 45]. Therefore, it is necessary to determine the behavior of riblet direction in, as well as the impact of the cathodic environment on, propellers and hulls.  
To address this issue, the following subsections compare the results obtained in this study with prior available data to highlight the contributions of the current work. They also explain the effects of riblet morphology to propose potential new patterns for decreasing erosion and discuss the effects of cathodic environments to provide more accurate data concerning erosion in real seawater situations. It is worth mentioning that the effects of temperature or its limitations in this study have not addressed because previous research has extensively covered these aspects. Previous studies have thoroughly investigated the effects of temperature on bubble dynamics [18; 22], the collapse process [18; 22], cavitation [18; 46; 47; 48], and induced erosion [22].
## 4 Correlation Analysis
### Oxford Government Responses and Citizens' Mobility Features  
#### 4.1.1 Oxford Government Responses--New Confirmed Cases  
The correlation analyses were conducted on the new confirmed cases and the Oxford government responses (Appendix B, table (a)). The stringency index levels of correlation are notably high, particularly during the first wave, with a global value for the new confirmed  
Figure 4: Feature trend. Google COVID-19 Community Mobility (**a**) and Apple COVID-19 Community Mobility (**b**).  
cases at 2.524. The overall level quantified across the three time periods (from first to third wave) is 5.807. This generic global value has been denoted as the \"score\" of the selected feature. In the UK, the three levels of correlation quantified for the stringency index are close to 0 (\(-\)0.014 in the first wave, \(-\)0.126 in the second, and \(-\)0.093 in the third). The highest values are observed in the first wave (0.769 in Italy). The trend of the stringency index and its individual contributions are detailed in Figure 3. Other significant and high levels of correlation pertain to the following features:  
* Stay at home requirements (score 6.518, which is the sum of the three waves' values; 3.658 global level in the first wave; 0.878 for the USA in the first wave).
* Workplaces closing (score 5.22, which is the sum of the three waves' values; 3.251 global level in the first wave; 0.903 for the UK in the first wave).
* School closing (score 3.889, which is the sum of the three waves' values; 2.717 global level in the first wave; 0.637 for Germany in the first wave).
* Restriction on gatherings (2.925 global level in the first wave).  
Contact tracing correlation values generate the global score of \(-\)1.57, which refers to the first wave period. This score is one of the negative correlation values recorded in the correlation level table of the Oxford government responses.
## 2 System Model
### Transmitter  
The transmit structure of the proposed method is depicted in Figure 1. Two information bit streams \(\{a^{\alpha}(nT_{\mathrm{a}})\},\alpha=1,2\) with the time interval \(T_{\mathrm{a}}\) are converted into two groups of sub-streams \(\{a^{\alpha,m}(nT_{\mathrm{b}})\},\alpha=1,2,m=0,\cdots M-1\) through serial-to-parallel (SP) conversion, where \(T_{\mathrm{b}}=T_{\mathrm{a}}\cdot M\). Then, each bit sub-stream is encoded using convolutional encoders, and the coded bit sub-stream \(c^{\alpha,m}(nT_{\mathrm{b}})\) is permuted by interleavers to randomize the order of the code bits. Subsequently, the interleaved bit sub-streams \(\{d^{\alpha,m}(nT_{\mathrm{b}})\},\alpha=1,2,m=0,\cdots M-1\) are separately divided into Q-bit blocks and mapped to the symbol sub-sequences from the \(2^{\mathrm{Q}}\)-ary symbol alphabet \(\mathcal{S}\) with zero mean and unit energy. Finally, the mapped sub-sequences \(\{s^{\alpha,m}(nT)\},\alpha=1,2,m=0,\cdots M-1,T=Q\cdot T_{\mathrm{b}}\), are processed by FMT modulation and transmitted from two elements, respectively.  
Referring to FMT modulation in Figure 1, the mapped sub-sequences \(\{s^{\alpha,m}(nT)\},\alpha=1,2,m=0,\cdots M-1,T=Q\cdot T_{\mathrm{b}}\) are modulated to \(M\) subcarriers after \(K\)-times sampling and transmit filtering, and the \(i\)-th modulated signal of the \(\alpha\)-th group is given by  
\[x^{\alpha,i}(kT_{c})=\sum_{n=-\infty}^{+\infty}s^{\alpha,i}(nT)g_{\mathrm{t}} (kT_{\mathrm{c}}-nT)e^{i2\pi f_{\mathrm{t}}kT_{c}} \tag{1}\]  
where \(T_{c}=T/K\) is the time interval after \(K\)-times up-sampling, \(f_{i}\) is the \(i\)-th subcarrier, and \(g_{\mathrm{t}}(kT_{\mathrm{c}})\) is the discrete time domain response of the transmit filter. To maintain a balance between implementation complexity and spectral containment, the root raised cosine (RRC)-shaping filter is selected as the transmit filter and the roll-off factor is set to \(K/M-1\).  
For total communication bandwidth \(B=1/T_{c}\), the intercarrier separation equals \(\Delta f=1/T_{c}M\), and then \(f_{i}=i\cdot\Delta f=i/T_{c}M\). Substituting \(f_{i}\) in Equation (1) with \(i/T_{c}M\), \(x^{a,i}(kT_{c})\) can be further expressed as  
\[x^{a,i}(kT_{c})=\sum_{n=-\infty}^{+\infty}s^{a,i}(nT)g_{\rm t}(kT_{\rm c}-nT)e^ {i\frac{2\pi}{M}ik} \tag{2}\]  
After combining all modulated signals on \(M\) subcarriers, the \(\alpha\)-th transmitted signal is given by  
\[x^{\alpha}(kT_{c})=\sum_{i=0}^{M-1}\sum_{n=-\infty}^{+\infty}s^{a,i}(nT)g_{\rm t }(kT_{\rm c}-nT)e^{i\frac{2\pi}{M}ik} \tag{3}\]
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 2 Graphene Optical Properties  
Graphene can be described by considering the surface conductivity (\(\sigma\)) and the complex permittivity (\(\varepsilon\)), from which the refractive index (\(n\)) and the extinction coefficient (\(k\)) can be calculated. Graphene optical properties require knowledge of the frequency-dependent surface conductivity \(\sigma_{G}\), a quantity which can be obtained from either a microscopic model or from measurements [56].
## 2 Materials and Methods
### The Overall Structure of CDFFNet  
The overall structure of CDFFNet is illustrated in Figure 1. It retains the structure of DeepLabv3+ for thoroughly extracting shallow and deep features in the spatial domain, utilizing the 1/4 low-level feature layer output by the MobileNetV2 backbone network as the shallow feature layer to preserve positional information in the spatial domain. The 1/16 feature map is passed into the Atrous Bottleneck Pyramid Module (ABPM) for multi-scale feature extraction, obtaining deep features that contain spatial domain category information. Subsequently, the output 1/8 and 1/16 feature maps are resized to the same scale and concatenated, then fed into the Frequency Band Feature Separator (FBFS). In FBFS, Haar wavelet transform is utilized to decompose spatial domain features into low-frequency and high-frequency features in the frequency domain. The Domain Fusion Module (DFM) module then selects and combines shallow features, low-frequency features, deep features, and high-frequency features to obtain richer hybrid features. Finally, the hybrid features from the two branches and the feature maps from three scales of the backbone network are concatenated, and the final road extraction result is obtained through the segmentation head.
## 3 Off-board Charger Control
### Double Closed-Loop Control  
The DC/DC converter in the off-board charger uses a full-bridge resonant LLC converter that is frequently used in practical applications. This converter not only actualizes the electrical isolation between the power battery and the grid, but also has the characteristics of high power transmission and high efficiency, which can meet the fast charging requirement of high-capacity electric vehicles [17; 18]. The double closed-loop control which is shown in Figure 4 will be mainly relevant for ensuring the stability of the DC bus voltage \(U_{\text{dc}}\). The post-stage DC/DC converter does not directly regulate the charging power \(P_{\text{bat}}\) of the power battery, which is processed by pre-stage AC/DC converter.  
It can be noticed from Figure 4 that the inner-loop current reference value \(I_{\text{ref}}\) which is obtained by outer-loop voltage \(U_{\text{dc}}\) control and the effective value \(I_{\text{r}}\) of resonant current emit the PWM modulated signals \(D\) through PI control. When the battery is charged, the full-bridge resonant LLC converter is in the step-down mode. Under such circumstances, the direction of the current flowing into the battery is positive, i.e., \(I_{\text{bat}}>0\), but \(U_{\text{dc-ref}}-U_{\text{dc}}<0\). Accordingly, the reference value of the inner-loop current is engendered by using \(U_{\text{dc}}-U_{\text{dc-ref}}\) for PI control.
## 5 GaoFen-3 Test Data Processing Results
### Test Data Processing and Analysis  
In order to improve the processing efficiency, the pulse signal data is first taken out from the complex sampling data, and the invalid noise data between pulses is eliminated; then, process the data as described in Section 3. Figure 8 shows the spectrum of one of the pulse signals and the spectrum of the matched filter. Figure 9 shows the extracted range migration curve and the pulse compression peak envelope.  
According to the pulse signal, acquisition time length is 0.25 s, and according to the satellite flight speed, the range from satellite to receiver and other parameters and supposing that the ground receiver collected the data of the peak time, the maximum possible range migration is calculated to be 2.02827 m. The one-way range resolution calculated from the above parameters is 5 m, and the sampling interval of the ground receiver corresponds to 1 m, so that the change of the entire range  
Figure 8: Spectrum of one of the pulse signals and the constructed matched filter.  
Figure 7: Complex sampling data recorded by the ground receiver.  
migration curve does not exceed 0.4 range gates and the corresponding sampling interval is 2 units. In this case, it is impossible to measure the extreme position at all. Figure 10 shows the comparison of the effects before and after interpolation. It can be seen that after 32 multiples interpolation, the range migration changes reach 12-13 units. There is also better symmetr so that the extreme point of the range migration curve can be accurately obtained.  
Figure 11 shows the comparison of the pulse compression results before and after interpolation, and shows that only the peak value of the pulse compression can be accurately extracted after interpolation.  
Figure 10: The comparison of the range migration curves before and after interpolation.  
Figure 9: Extracted range migration curve and peak envelope after pulse compression.  
It can also be seen from Figure 9 that the pulse signal amplitude envelope center is also obtained and is not at the edge position, thus the method of Section 3 can also be used to solve the center position of the main lobe of the pattern. However, the M value cannot be obtained too large; this is the limitation of the data acquired by this receiver.  
Using the method of Section 3, the center position of the measured main lobe of the antenna pattern is the 85 th pulse in the measured data and the 86 th pulse in the fitting curve of the measurement data. The extreme position of the range migration curve is the 164 th pulse in the measured data and the 167 th pulse in the fitting curve of the measurement data. According to the spatial relationship between the satellite and the ground receiver, the range between the satellite and the ground receiver is calculated as \(R_{0}\) = 882,300.41 m. Calculating the antenna beam squint angle deviation according to formula (8) is:  
\[\Delta\alpha=0.0285^{\circ}\]  
According to Equation (12), the estimation accuracy is approximately:  
\[\delta\alpha=0.00157^{\circ}\]
# Design and Experiment of Dry-Ice Cleaning Mechanical Arm for Insulators in Substation
## 7 Experiments
### High-Voltage Endurance Insulation Test of Dry-Ice Cleaning Mechanical Arm  
In order to ensure the operation safety of the electrified dry-ice cleaning mechanical arm in the substation and adapt to the high-voltage environment in the station (shown in Figure 13a), the 750 kV high-voltage test vehicle (shown in Figure 13b) is used for the voltage-withstanding insulation test. The test system is shown in Figure 12. After the high-voltage test vehicle is ready, the high-voltage wires from the high-voltage test transformer is directly twined on the nozzle at the end of the mechanical arm. In the process of test for the experiment, the mechanical arm is controlled by hand-held controller at a distance of 100 m to move the mechanical arm up and down, forward and backward, and rotate. At the same time, the experiment voltage is constantly raised. When the voltage is raised to 470 kV, the mechanical arm can still work stably and reliably without any phenomenon of discharge or breakdown. Through the high-voltage-insulation test, it is proved that the dry-ice cleaning mechanical arm can meet the high-voltage-insulation requirements of 220 kV substation and it can carry out electrified cleaning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Parameters** & **Item** & **Parameters** \\ \hline Azimuth angle range & \(\pm\) 120\({}^{\circ}\) & Pressure of dry-ice gas & 0.5\(\sim\)0.6 MPa \\ Azimuth velocity & \(\leq\) 5 \({}^{\circ}\)/s & Angle control accuracy & \(\leq\pm\) 0.5\({}^{\circ}\) \\ Working distance & 1.2\(\sim\)1.5 m & Terminal control accuracy & \(\leq\pm\) 5 mm \\ Working height range & 2 m & Wireless transmission distance & \(\geq\) 100 m \\ Up, down/front, rear velocity & \(\leq\) 100 mm/s & Wireless transmission delay & \(\leq\) 30 ms \\ Diameter of cleaning insulator & \(\leq\) 250 mm & Weight & \(\approx\) 150 kg \\ Arm material & MC nylon 66 & \multicolumn{2}{c}{Closing size \(\left(\text{L}\times\text{W}\times\text{H}\right)\)} & \(2000\times 800\times 2900\) mm \\  & & \multicolumn{2}{c}{Expanded maximum envelope} & \\ Pedestal material & LY12 & \multicolumn{2}{c}{size} & \(\Phi\) 3400 \(\times\) 3400 mm \\  & & \multicolumn{2}{c}{(Diameter \(\times\) Height)} & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Index and parameters of mechanical arm.  
Figure 12: Dry-ice cleaning mechanical arm system.
# Discovering Trends in the Digitalization of Shipping:
## 2 Current State of the Maritime Industry: An Industry 4.0 Perspective
### Towards Revival and Adaptation Strategies--Collaborations and Prospects  
In response to global market fluctuations and environmental challenges, the maritime industry is actively exploring revival strategies centered on technological innovation and sustainability [2]. Embracing digital technologies, particularly AI, is seen as a key driver for enhancing competitiveness and operational efficiency. Moreover, the industry is increasingly focused on adopting environmentally sustainable practices, aligning with global efforts to reduce greenhouse gas emissions and minimize the ecological footprint of maritime activities.  
The latter is often facilitated by the synergistic collaboration between academic institutions and the maritime industry. This collaboration is also evident in joint research initiatives aimed at advancing AI applications, improving data analytics capabilities, and strengthening cybersecurity frameworks [4; 5]. As the industry continues to evolve, ongoing research and innovation in these areas are expected to remain central to its growth and adaptation to future challenges.  
The current state of the maritime industry is therefore characterized by a dynamic transition towards greater digitalization and a commitment to sustainable operations. The integration of cutting-edge technologies like AI and big data analytics, coupled with a strong emphasis on cybersecurity and strategic revival initiatives, is redefining the future landscape of maritime activities.