esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 3 In-building System Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No**} & \multirow{2}{*}{**Load Type**} & **Energy (Quadrillion** & **Percentage** & **Energy (\%)** & **Energy (kWh)** & **Category (A, D, I)** \\  & & **British Thermal Units)** & **Energy (\%)** & **(kWh)** & **(A, D, I)** \\ \hline \multirow{2}{*}{1} & Heating & \multirow{2}{*}{0.42} & \multirow{2}{*}{8.78} & \multirow{2}{*}{2.64} & \multirow{2}{*}{I} \\  & (Space) & & & & \\ \hline \multirow{2}{*}{2} & Water & \multirow{2}{*}{0.48} & \multirow{2}{*}{9.93} & \multirow{2}{*}{2.98} & \multirow{2}{*}{I} \\  & Heater & & & & \\ \hline \multirow{2}{*}{3} & Cooling & \multirow{2}{*}{1.02} & \multirow{2}{*}{21.14} & \multirow{2}{*}{6.37} & \multirow{2}{*}{A} \\  & (Space) & & & & \\ \hline \multirow{2}{*}{4} & Lights & 0.53 & 11.04 & 3.31 & D \\ \hline \multirow{2}{*}{5} & Refrigerator & 0.45 & 9.45 & 2.84 & A \\ \hline \multirow{2}{*}{6} & Electronics & 0.33 & 6.86 & 2.06 & D \\ \hline \multirow{2}{*}{7} & Cleaning & 0.33 & 6.8 & 2.04 & A \\ \hline \multirow{2}{*}{8} & Cooking & 0.11 & 2.36 & 0.71 & I \\ \hline \multirow{2}{*}{9} & Computers & 0.19 & 3.95 & 1.19 & D \\ \hline \multirow{2}{*}{10} & Others & 0.94 & 19.69 & 5.91 & 0.33A + 0.33D + 0.33I \\ \hline \multirow{2}{*}{11} & Total & 4.795 & 100 & 30.03 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Residential Energy Splits.
###### Abstract  
The operational efficiency and reliability of the ship's lubrication oil system directly impact the vessel's safety. Traditional reliability analysis methods struggle to effectively handle the system's dynamic characteristics and multi-source data analysis. To address these issues, this study proposes an innovative method that integrates feature dimensionality reduction, a dynamic Bayesian network of gravity model to improve the accuracy of system reliability analysis. First, the proportional hazards model is used to evaluate the operational reliability of each component, providing a quantitative basis for assessing the system's health status through failure rate estimation. Then, a dynamic Bayesian network model is employed for overall system reliability analysis, fully considering the impact of multi-state devices and different maintenance strategies. The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). The reliability of the main lubrication oil system (GUB) increases from 0.169 to 0.261, representing a 9.2% improvement; under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after \(0.4\times 10^{5}\) h, compared to only 0.24 without maintenance. The proposed method effectively evaluates the reliability of the lubrication oil system, and the maintenance strategy using this method can greatly improve the reliability, providing strong support for scientifically guiding maintenance decisions.  
ship lubricating oil system; reliability analysis; proportional hazards model; dynamic Bayesian network +
Footnote †: journal: Applied Sciences
## 4 Discussion
It is worth noticing that for scale 4 images from the Lepton dataset were as small as 20 \(\times\) 15 pixels and still a similar RR accuracy was achieved as for the original resolution (80 \(\times\) 60) with color changes enhanced using EVM. Taking it into account, many applications that utilize very low resolution thermal images can potentially benefit from applying SR algorithms, leading to results similar to values estimated from data with much bigger spatial resolution. Examples of such applications include detecting of unusual behaviours at security checkpoint by analysis of vital signs patterns, monitoring of passengers' state of health in self-driving vehicles or contact-less estimation of respiratory rate in newborns. Considering the cost and availability of high-resolution thermal cameras, this finding can be crucial for enabling solutions that previously were very difficult to achieve, e.g., vital signs estimation at longer distances [54].
## 4 Simulation Results and Analyses
### Results and Analyses
#### 4.2.5 Simulation Results and Analyses for 22:00 to 24:00  
Figure 17 presents the results of the period 22:00-24:00 taken from Figure 7. One notes that the same results are shown above as the results of the period 0:00-6:00. At 22:00 the public grid power limit is 600 W and supports the load demand and also charges the BS until the \(soc_{BS}\) reaches \(SOC_{BS\_MAX}\); then, the public grid only supports the load demand.  
From these simulation results, one can note the following:  
The full DC microgrid can operate normally during the day and night with the proposed algorithm.  
The microgrid power is well balanced with DC bus voltage fluctuation limited to achieve the microgrid power quality.  
The specific algorithm can operate the microgrid power balance control and follow the constrained power management strategy in grid-connected mode as well as in islanded mode.  
The constraints depicted in Section 2 of this paper can work.  
Load real-time optimization can give the optimal load combination according to the predefined load constraints.  
Figure 16: Curves of common DC bus voltage and SOC of storage (a) and curves of power (b) during the period 17:00–22:00.
## 3 Results  
Figure 1 represents the FE-SEM for CuCoNiOx@CNTs/GCE at (A) 600 s; (B) 900 s; and (C) 1200 s deposition time; as well as the (D) EDX analysis for Sample (C), and Figure 2 shows the XRD pattern of CuCoNiOx@CNTs/GCE. Figure 3 shows the CVs of different electrodes in 1 M NaOH solution at a scan rate of 100 mV/s: (a) CNTs/GCE; (b) CoOx@CNTs/GCE; (c) CuOx@CNTs/GCE; (d) NiOx@CNTs/GCE; and (e) CuCoNiOx@CNTs/GCE electrodes. Figure 4A,B reveals the CVs of CuCoNi@CNTs/GCE in 1 M NaOH solution at different scan rates and the variation of the anodic and cathodic peak currents with the scan rates, respectively. The effect of the scan rate in 1 M NaOH is shown in Figure 5. The electrocatalytic behavior towards methanol oxidation in alkaline medium for (a) CoOx@CNTs/GCE; (b) CuOx@CNTs/GCE; (c) NiOx@CNTs/GCE; and (d) CuCoNiOx@CNTs/GCE electrodes is shown in Figure 6. Figure 7 is a Schematic illustration of the methanol electrooxidation reaction mechanism and Figure 8 shows the variation of \(I_{\mathrm{p}}/\
u^{1/2}\) with \(\
u\) for the methanol electrooxidation obtained at CuCoNiOx@CNTs/GCE. Figure 9 shows the CVs for CuCoNi@CNTs/GCE at different deposition times in 1 M NaOH + 1 M methanol solution at a scan rate of 100 mV\(\cdot\)s\({}^{-1}\), and Figure 10 reveals the \(I\)-\(t\) transient for (a) CuCoNi@CNTs/GCE; (b) NiOx@CNTs/GC; (c) CoOx@CNTs/GC; and (d) CuOx@CNTs/GC in 1 M NaOH + 1 M methanol solution.  
Figure 1: FE-SEM (field emission scanning electron microscope) for CuCoNiOx@CNTs/GCE(carbon nanotubes/glassy carbon electrode) at different deposition times (**A**) 600 s; (**B**) 900 s; (**C**) 1200 s; (**D**) Mapping EDX (Energy Dispersive X-ray) for Sample (**C**).  
Figure 3: CVs (cyclic voltammetry) of different electrodes in 1 M NaOH solution at a scan rate of 100 mV/s: **(a)** CNTs/GCE; **(b)** CoOx@CNTs/GCE; **(c)** CuOx@CNTs/GCE; **(d)** NiOx@CNTs/GCE; and **(e)** CuCoNiOx@CNTs/GCE electrodes.  
Figure 2: XRD (X-ray diffraction) pattern of CuCoNiOx@CNTs/GCE.  
Figure 4: (**A**) CVs of CuCoNi@CNTs/GCE in 1 M NaOH solution at different scan rates; (**B**) Variation of the anodic and cathodic peak currents with the scan rates.  
Figure 5: CVs of CuCoNi@CNTs/GCE at different deposition times in 1 M NaOH at a scan rate of 100 mV\(\cdot\)s\({}^{-1}\).  
Figure 6: CVs of different electrodes in 1 M NaOH + 1 M methanol solution at a scan rate of 100 mV\(\cdot\)s\({}^{-1}\): (**a**) CoOx@CNTs/GCE; (**b**) CuOx@CNTs/GCE; (**c**) NiOx@CNTs/GCE; and (**d**) CuCoNiOx@CNTs/GCE electrodes.  
Figure 8: Variation of \(I_{\rm p}/\
u^{1/2}\) with \(\
u\) for methanol electrooxidation obtained at CuCoNiOx@CNTs/GCE.  
Figure 10: \(I\)-\(t\) transients for (**a**) CoOx@CNTs/GCE; (**b**) CuOx@CNTs/GC; (**c**) NiOx@CNTs/GC; and (**d**) CuCoNiOx@CNTs/GC in 1 M NaOH+1M methanol solution.  
Figure 7: Schematic illustration of the methanol electrooxidation reaction mechanism.  
Figure 9: CVs for CuCoNi@CNTs/GCE at different deposition times in 1 M NaOH + 1 M methanol solution at a scan rate of 100 mV\(\cdot\)s\({}^{-1}\).
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 3 Results and Discussion
### Spatial Distribution Analysis  
The spatial distribution processing steps were as follows:  
1. Count the number of UA events occurring at each airport;
2. Count the monthly throughput of the airport;
3. Calculate the frequency of UA using the number of UA events and the throughput;
4. Conduct spatial correlation analysis to match the segment of the UA and airports.  
The frequency of UA events was visually analyzed, and the results are presented in Figure 2. As shown in Figure 2, UA events were found to occur frequently in the eastern part of China, where airports are always busy. This pattern is consistent with the result presented in Figure 1. In particular, dramatic changes in terrain seem to be highly correlated with UA event frequencies, as in the northeastern and midwestern areas. Note that high frequencies appear in high-altitude areas, such as Yunnan. This observation indicates that the occurrence of UA is correlated with high-altitude terrain or its changes. In Xinjiang Province and most of the eastern regions, UA events were found to occur with different frequencies in different time periods, which could be related to the local meteorological conditions in January.  
Figure 1: Temporal heatmap of unstable approach (UA) events.  
From the distribution of the triggering causes of UA presented in Figure 3, the UA incidents that happened in the northeastern and southern regions of China were mainly caused by track deviations, while pitch overruns were found to be more prominent causes of UA in the eastern and northwestern regions of China.  
Figure 3: Different causes of UA events for different airports.  
Figure 2: Spatial pattern analysis of UA frequencies.
# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
## 2 Materials and Methods
### Optimization of Alloys with a Genetic Algorithm  
#### 2.2.1 Compositional Space Explored  
Austenitic steels contain iron and primarily rely on Ni, Mn and Co as key alloying elements to stabilize the FCC solid solution, along with Cr to ensure corrosion resistance. In this study, we focus on iron-rich alloys, requiring a minimum of 30 wt.% Fe. To ensure good corrosion resistance and intergranular corrosion resistance, we set a minimum Cr content at 15 wt.% and a C content of 200 wt. ppm, similar to low carbon stainless steels. Indeed, increasing chromium and limiting carbon content prevents intergranular precipitation of Cr carbides. Nickel plays a crucial role in stabilizing the austenitic phase, therefore the upper limit for this element is set to a high value of 35% wt. Manganese and cobalt, both serving as austenite stabilizers, have an upper limit of 25%. Adjustments to the maximum concentrations of the remaining elements were made based on data from commercial alloy compositions [65]. The compositional search space is indicated in Table 2.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Analysis of Our Proposed Model
#### 5.2.1 The Standard Deviation, and the Coefficient of Variation of the Number of CHs  
The current clustering schemes are using distributed methods for CHs selection which do not assure the optimal value of CHs according to our analysis. We also found that the optimum percentage of CHs is always less than 20% which does not affect the network lifetime. In worst case scenarios, when no CH is selected or the CHs are less than the optimal value the clustering structure will collapse or the network will drain its resources earlier than expected. The unevenness of CHs in the clustering structure badly affects the energy efficiency and the network lifetime. Figure 9a,b demonstrates the COV and the SD in the selection of CHs; the results are extracted from the proposed model in different  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Value** \\ \hline Size of network & 100 m \(\times\) 100 m \\ N & 100 \\ \(E_{\textit{{e}lec}}\) & 50 n/bit \\ \(\epsilon_{\textit{{amp}}}\) & 100 pJ/bit/m\({}^{2}\) \\ \(\varepsilon_{\textit{{fs}}}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{\textit{{o}}}\) & 0.25 J, 0.5 J, 0.75 J \\ \(E_{\textit{{DA}}}\) & 5 n/bit/message \\ \(P_{\textit{{opt}}}\) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters and their values used in simulation environment.  
scenarios. We can see that the larger the sensing field the greater the number of CHs, and the wider the distribution for the number of CHs.
## 2 Traffic Characteristics and Weather in Slovenia
In winter conditions, we filtered the data from the database that correspond to slippery, icy, or snowy road conditions. Thus, on a corresponding homogeneous short carriageway section, we calculated traffic flow density \(k_{i,c}\) in traffic lane \(i=\) [1,2] under weather conditions \(c=\) {_d_, _w1_, _w2_, _w3_, _lv_, _s_} and analysed the \"classic\" relationships of density-speed and flow-speed. An example of the analysis at test site 0178 on highway A1 near Ljubljana is presented in Figures 1 and 2. Figure 1: Example of scatter graph of time mean speed vs. traffic density on the carriageway near Ljubljana in different traffic lanes: (**a**) lane 2—left (overtaking) lane; (**b**) lane 1—right lane. In any case, we found out from the literature review and experience with known events in unstable traffic flow that, at an increased total traffic flow rate \(q_{tot,c}\), the flow rate in the left lane \(q_{2,c}\) is higher than in the right lane \(q_{1,c}\). This confirms the finding about the unused capacity of lane 1 (\(LFR_{2}>LFR_{1}\)), where the speeds are lower [12; 14; 18]. It is also interesting to note that this difference varies depending on weather conditions [19], which means that it makes sense to consider it as a decision-making criterion in active traffic lane control. Differences in the traffic flow density \(\Delta k_{c}\) occurred at the maximum measured flows. In good weather conditions, the density was lower in lane 1 (\(k_{1}<k_{2}\)), while it was lower in the overtaking lane (\(k_{2}<k_{1}\)) in adverse weather conditions. In addition to the above-mentioned traffic flow characteristics, we empirically analysed the remaining characteristics on the basis of the findings [15; 43], which allowed us to perform a microsimulation in the next step to substantiate the appropriateness of the decision to not permit HGV to change traffic lanes.
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Fabrication Process
#### 4.2.5 Stereolithography (SLA)  
Scaffolds can be synthesized using the Stereolithography (SLA) process as described in previous research [169; 170]. CAD software is used to design the architecture of the substrate to be fabricated. Firstly, a pre-polymer solution is prepared. Then, the digital light processing chip is used to create dynamic photomasks that create cross-sectional images of the 3D microstructure, which are then projected onto the prepolymer solution using a UV light source. Polymerization occurs when the liquid pre-polymer solution is exposed to the light source. A layer-by-layer 3D microstructure is obtained with controlled pore size and porosity [169]. For applications in bone tissue engineering, a poly(propylene fumarate) (PPF) scaffold was fabricated using SLA [171]. [PERSON] et al. developed a PPF scaffold using the salt-leaching technique for bone healing. Although the scaffold demonstrated good mechanical and biological properties, the lack of appropriate internal architecture led to minimal cell proliferation and attachment [172]. The former process showcased superiority over the latter. Previously, this RP technique was widely used in utilizing and analyzing data obtained from CT and MRI images. A combination of medical imaging and SLA has bought forth the possibility of producing anatomically shaped implants like vascular grafts. [PERSON] et al. fabricated a life-size anatomical model of an abdominal aortic aneurysm with the aid of images obtained from the patient's CT scan [173]. In vivo implantation of such devices poses great difficulties, but these models help surgeons pre-plan for life-threatening circumstances. Micro-tissue models fabricated using SLA were assembled digitally using bubble-propelled microbots [174]. The 3D stereolithographic technique was used to fabricate multi-material cantilevers that can mimic the native myocardium, with potential applications in sensor development and biohybrid actuators [175]. For superior speed in grabbing and cargo carrier, a hydrogel that could be manipulated using near-infrared light was fabricated using N-isopropylacrylamide [176].
# Influence of Cryogenic Grinding on the Nutritional and Antinutritional Components of Rapeseed Cake
## 2 Materials and Methods
### Content and Composition of Sterols  
The sterol composition was determined according to the standard ISO 12228-1:2014 method for animal and vegetable fats and oils [24] with \(\alpha\)-cholesterol as the internal standard. After saponification of the oily extract of rapeseed cakes, followed by extraction and purification of the sterol fraction, the sterols were silylated with 100 \(\upmu\)L of pyridine-hexamethyl-disilazane-chlorotrimethylsilane solution (5/2/1, _v/v/v_). The prepared silylated sterol fraction (1 \(\upmu\)L) was injected into an Agilent Technologies 6890N Network GC system (Santa Clara, CA, USA) equipped with an Agilent Technologies 5973 Inert Mass Selective Detector and an Agilent DB-17 MS capillary column (30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m). The split ratio was 13.3:1. Helium was used as the carrier gas at a constant flow rate of 1.5 mL/min. The temperature of the injector was set at 290 \({}^{\circ}\)C. The temperature of the oven was programmed to increase by 6 \({}^{\circ}\)C/min from 180 to 270 \({}^{\circ}\)C and remained at the maximum temperature for 30 min. The transfer line temperature was set at 280 \({}^{\circ}\)C. Sterols were identified from an internal mass spectral library created using the \(\beta\)-sitosterol, campesterol, and stigmasterol standards and the NIST 2 and NIST 5 online libraries. All sterols were quantified using an internal standard method. The concentration of total sterols was calculated based on the mass of the cake, taking into account the amount of extracted oil from each sample.
###### Abstract  
The combination of graphene and metamaterials is the ideal route to achieve active control of the electromagnetic wave in the terahertz (THz) regime. Here, the tunable plasmon-induced transparency (PIT) metamaterial, integrating metal resonators with tunable graphene, is numerically investigated at THz frequencies. By varying the Fermi energy of graphene, the reconfigurable coupling condition is actively modulated and continuous manipulation of the metamaterial resonance intensity is achieved. In this device structure, monolayer graphene operates as a tunable conductive film which yields actively controlled PIT behavior and the accompanied group delay. This device concept provides theoretical guidance to design compact terahertz modulation devices.  
plasmon-induced transparency; metamaterial; graphene; bright mode +
Footnote †: journal: Applied Physics Letters
## 2 Data  
### COSMIC-2 Data Coverage  
COSMIC-2 occultations mainly distribute from 45\({}^{\circ}\) N and 45\({}^{\circ}\) S. Figure 2a shows the daily average of each 500 \(\times\) 500 km\({}^{2}\) box of COSMIC-2 occultations using the data for October 2019. The colors represent the average number of occultations per day in each box. Figure 2a shows that most (more than 75%) COSMIC-2 data are within 30\({}^{\circ}\) N and 30\({}^{\circ}\) S, where it is also the region that contains a uniform local time coverage (not shown).  
Figure 2b depicts the daily average of COSMIC-2 and COSMIC density distribution, made by constructing a 500 \(\times\) 500 km\({}^{2}\) box in the same latitude band. The plot shows that COSMIC-2 provides an average of about three to five soundings per day in each 500 \(\times\) 500 km\({}^{2}\) box latitude box in subtropics from 30\({}^{\circ}\) N to 30\({}^{\circ}\) S. [1] (see Figure 18 in Ref. [1]) shows that COSMIC-1 provides one sounding per day in each 500 \(\times\) 500 km\({}^{2}\) box over the mid-latitudes and about two soundings per day over higher-latitudes. Currently, COSMIC-2 tracks only GPS and GLONASS signals, which results in approximately 4000 occultation profiles on average from June 2019 till August 2020.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 6 Conclusions
This progressive arrangement leads all terminals to the same ratio value and at this point the branch can be considered balanced. If there is more than one branch, the users belonging to each one can be balanced between them following the same procedure, then all the branches can be balanced in the same way by acting on the branch-valves and following the same pair procedure. At the end of the balancing step, the whole network is characterized by the same rate ratio within admitted tolerances. 4. Adjusting. The pump velocity is varied, and all the flow rates will increase (or decrease) at the same time until the shared rate ratio reaches the unit value and the total flow rate reaches the nominal value. The balancing procedure involves iterations because after any action on a valve the flow rates of all other paths change as well. This makes the procedure expensive in operations and time. Furthermore, results and time consumption strongly depend on the system complexity, the required tolerance, and the operator experience.