esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 3 Polymer-Inorganic Coordinated Hybrids: Complexation-Induced Properties
### Nano-Templated Hybrids
#### 3.3.1 Nanomaterials as Sacrificial Templates  
Using a sacrificial template offers an efficient and straightforward method for creating patterned nanomaterials through vapor phase infiltration (VPI). While various templates and processes are available, they all follow a similar procedure. Initially, a polymer template is infiltrated with an inorganic material through VPI. During this process, the precursors react with the organic template, forming a hybrid organic-inorganic material. Since mineralization takes place within the template, its morphology is preserved post-hybridization. Finally, calcination, reactive ion etching, or UV or ozone cleaning of the hybrid material produces an inorganic replica of the original template, often resulting in a porous structure ([PERSON] et al., 2017). An example of this approach involves the infiltration of TiO\({}_{2}\) in PS-b-PMMA BCP films. Over multiple SIS cycles, the accumulation of TiO\({}_{2}\) within the PMMA domains gradually reduced the permeability of the polymer, ultimately restricting further infiltration. Once the desired level of infiltration was achieved, the BCP template was removed by calcination, leaving a structured inorganic TiO\({}_{2}\) framework that replicated the morphology of the original BCP nanodomains (Figure 8a) ([PERSON] et al., 2017). However, this step must also consider the template's shrinkage and temperature sensitivity to preserve the porous structures ([PERSON] et al., 2017).  
As mentioned above, liquid phase infiltration has also been vastly used to synthesize BCP-templated hybrid films and directly patterned inorganic nanostructures ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017). [PERSON] et al. (2018) used polystyrene-block-poly(2-vinylpyridine) (PS-b-P2 VP) BCP films with a self-assembled lamellar structure as templates. By soaking these films in a solution containing platinum ions (Na\({}_{2}\)PtCl\({}_{4}\)) at elevated temperatures, they achieved selective infiltration of Pt ions into the P2 VP domains, exploiting the pyridine groups' affinity for metal ions. The increase in temperature enhanced the infiltration rate and amount of Pt loading in the P2 VP domains, producing a denser metal-loaded structure. After sufficient infiltration, the polymer matrix was removed by oxygen plasma ashing,leaving behind a patterned Pt nanowire structure that replicated the original morphology of the BCP (Figure 8b).
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 2 Demand Management and Direct Control of Electric Water Heaters
### Problem Formulation  
The problem proposes determining an index for the quantitative evaluation of the discomfort generated by the application of load shifting using a direct control of electric water heaters. Two main challenges must be addressed to solve the problem: determining proper variables for the analysis and, since no-deterministic behavior is involved, proposing an effective methodology to estimate their values.  
The first step is evaluating relevant variables for the problem. In the case of electric water heaters, the main variables affecting the comfort of the users is the temperature of the water in the tank and variables that determine the utilization pattern of water heaters. Regarding user comfort, installing a remote device to the water heater that allows measuring its power and switching it off or on is reasonable, since it can be achieved via specific Internet of Things controllers without modifying the water heater structure [13; 14]. However, installing a remote thermometer to measure the temperature of the tank requires modifying the structure of the water heater, which implies a significant monetary investment. Thus, non-intrusive methods [15] are preferred for the analysis.  
The main goal of defining a proper direct control strategy is to determine the discomfort introduced by the same controlling action among several electric water heaters to decide which of them intervene and minimize the probability of generating discomfort. Therefore, it is not crucial to compute the temperature exactly, but a good approximation is enough for analyzing differences. Another relevant aspect for the analysis is that the user perceives thermal discomfort only when using water and the temperature is below a given threshold. At any other time, or if the water temperature is still above the threshold despite the intervention, users are not aware of the switching-off actions.  
Two variables must be estimated to define a discomfort index in the event of an intervention: the water utilization and the temperature of the water in the tank. By estimating these two variables, the proposed TDI takes into account the period of time  
Figure 1: (Normalized) total power and electric water heater demand in a representative workweek (11 November to 15 November 2019) in Uruguay. Data from the ECD-UY dataset [6].  
that the water temperature is below the comfort temperature for a person using the water. This way, the proposed TDI properly models the human discomfort associated with an intervention.
## 5 Conclusions  
We explored the potential of fused high spatiotemporal remote sensing imagery for revealing cropping intensity dynamics. Using Shaanxi Province, China, as a case study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps at a 10 m resolution from 2019 to 2023. Subsequently, we investigated the spatiotemporal characteristics of cropping intensity and the distribution of abandoned cropland. Our findings revealed that single-cropping dominated crop production in Shaanxi (52.9% of cropland area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. While the transition in cropping intensity led to an upward trend in cropland utilization, large areas of cropland were abandoned, particularly in northern Shaanxi. These findings demonstrate the capability of high-resolution imagery to identify cropland utilization dynamics in fragmented agricultural regions and offer valuable guidance for effective cropland management practices.  
Conceptualization, Y.L. and Z.W.; data curation, H.L., B.C., and M.L.; formal analysis, Y.L.; methodology, Y.L. and L.Z.; project administration, Z.W.; supervision, Q.Y.; visualization, Y.L., H.H., and H.Z.; writing--original draft, Y.L.; writing--review and editing, Z.W. and Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant number 42101304), and the Key Research and Development Projects of Shaanxi Province (Grant number 2024 NC-YBXM-213).  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 4 Discussion
Although the MODIS NDVI data used were subject to strict quality control via Savitzky-Golay filtering and QA bands to eliminate the effects of clouds, snow, and atmospheric noise, residual noise or missing data may still introduce uncertainties in sparse vegetation areas of extremely arid regions, potentially affecting the accuracy of NDVI trend and correlation analyses. More importantly, this study did not quantitatively isolate the relative contributions of climate change and human activities (e.g., irrigation, shelterbelt construction) to NDVI variations. Additionally, key factors such as latitude, elevation, land use/cover changes, groundwater dynamics, and soil physical properties (e.g., water retention) were not incorporated into the analysis framework. These factors may play a crucial role in modulating vegetation responses to climate, particularly precipitation, in extremely arid regions [39; 40]. In subsequent research, we will focus on the following aspects: (1) integrating multi-source data, including high-resolution soil moisture data, detailed land use/cover change data, and groundwater dynamic monitoring data; (2) employing advanced statistical methods, such as residual trend analysis and machine learning techniques, to quantify the relative contributions of natural (climatic) and anthropogenic factors to vegetation changes; (3) introducing time-series deep analysis methods, such as least squares wavelet analysis [41], to reveal the coherence and phase lag relationships between NDVI and climatic variables (e.g., precipitation and land surface temperature) in the time-frequency domain, thereby deepening the understanding of the vegetation-climate coupling dynamics in the Turpan-Hami Basin; and (4) developing more comprehensive models by incorporating key anthropogenic factors (e.g., irrigation, land use changes, and groundwater dynamics) and non-climatic natural factors (e.g., soil and topography) into the vegetation-climate response model framework to enhance the predictive accuracy and applicability of models in arid regions. These improvements will provide a more robust scientific foundation for regional water resource management and ecological restoration strategies and contribute to the development of a globally applicable theoretical framework for ecological restoration in arid regions. Finally, this study offers methodological inspiration for constructing ecological models in arid regions. Traditional models often neglect the spatial heterogeneity of human activities [42], whereas this study emphasizes that in the arid regions, factors such as irrigation and land-use change should be incorporated into vegetation-climate response models to enhance predictive accuracy. By integrating medium-resolution remote-sensing and climate data, this study provides initial insights into the complexity of their interactions. The development direction of future models should be deepened closely around points 3 and 4 of the above future research directions.
# Magnetic Properties of Gd-Doped Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
## 4 Conclusions  
Based on a microscopic model and the method of the Green's functions, this article presents analytical and numerical calculations of the magnetic and optical properties of a pure and Gd\({}^{3+}\)-doped magnetite NP with the chemical formula Fe\({}_{3}\)O\({}_{4}\). The investigations were carried out under the assumptions that the sample of [Fe\({}_{A}^{3+}\)][Fe\({}_{1-x}^{2+}\)Gd\({}_{x}^{3+}\)Fe\({}^{3+}\)]\({}_{B}\)O\({}_{4}\) NP is monodisperse and there is not interaction between the NPs.  
The magnetization \(M_{\rm s}\) and the Curie temperature \(T_{\rm C}\) are calculated depending on size, temperature, and Gd doping concentration. In qualitative agreement with the experimental data, we observe that \(M_{\rm s}\) and \(T_{\rm C}\) decrease with decreasing NP size and with increasing Gd doping concentration. The band gap energy \(E_{g}\) increases with decreasing NP size and Gd dopants.  
Methodology, I.A.; Software, I.A.; Formal analysis, A.A.; Investigation, A.A. and J.W.; Writing--original draft, A.A.; Supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Derived data supporting the findings of this study are available from the corresponding author upon reasonable request.  
One of us A.T.A. acknowledges financial support from the Center for Research and Design of the Sofia University of Architecture, Civil Engineering and Geodesy (contract number BN-289/23).  
The authors declare no conflict of interest.  
Figure 5: Gd doping concentration dependence of the band gap energy \(E_{g}\) of a Fe\({}_{3}\)O\({}_{4}\) NP for \(J_{d}=0.8J_{b}\).
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 2 Background
### Breadth First Search Pseudo-tree  
The _Breadth First Search_ (BFS) pseudo-tree is a commonly used communication structure for DCOPs and C-DCOPs [3; 26; 35; 36; 27]. The characteristics of the BFS pseudo-tree are multi-branch parallel computing, short communication path, and time. We briefly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Agent \(x_{1}\)** & **Agent \(x_{2}\)** & **\(\dots\)** & **Agent \(x_{n}\)** \\ \hline solution 1 & \(S_{1}.x_{1}\) & \(S_{1}.x_{2}\) & \(\dots\) & \(S_{1}.x_{n}\) \\ solution 2 & \(S_{2}.x_{1}\) & \(S_{2}.x_{2}\) & \(\dots\) & \(S_{2}.x_{n}\) \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ solution \(k\) & \(S_{k}.x_{1}\) & \(S_{k}.x_{2}\) & \(\dots\) & \(S_{k}.x_{n}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distributed population.  
Figure 1: An example of a C-DCOP.  
present the relevant definitions of the BFS pseudo-tree, and the details can be found in reference [41].  
Figure 2 shows an example of a BFS pseudo-tree using the constraint graph in Figure 0(a). Some definitions of the BFS pseudo-tree are given as follows:  
* \(E(x_{i},x_{j})\)--the tree edge, which connects \(x_{i}\) and \(x_{j}\) (e.g., \(E(x_{1},x_{2})\) is a tree edge in Figure 2).
* \(CE(x_{i},x_{j})\)--the cross-edge, which directly connects node \(x_{i}\) and \(x_{j}\) in two different branches (e.g., \(CE(x_{2},x_{3})\) is a cross-edge in Figure 2).
* \(P_{i}\)--the parent of node \(x_{i}\), the single higher node directly connecting \(x_{i}\) through a tree edge (e.g., \(P_{2}=x_{1}\) in Figure 2).
* \(C_{i}\)--the set of children of node \(x_{i}\), the lower nodes directly connecting \(x_{i}\) through tree edges (e.g., \(C_{1}=\{x_{2},x_{3},x_{4}\}\) in Figure 2).
* \(N_{i}\)--the set of neighbors of node \(x_{i}\), the neighboring nodes that directly connect \(x_{i}\) (e.g., \(N_{3}=\{x_{1},x_{2}\}\) in Figure 2).
* \(PN_{i}\)--the set of pseudo-neighbors of node \(x_{i}\), the neighboring nodes that directly connect \(x_{i}\) through cross-edge edges (e.g., \(PN_{3}=\{x_{2}\}\) in Figure 2).  
The BFS pseudo-tree is used for utility aggregation in distributed algorithms, each node \(x_{i}\) sends the sum of local utilities to the root node according to the parent node \(P_{i}\). Finally, the root node is aware of the aggregate utility.
## 4 The Methodology for the Proposition Validation
### The Case Studies Investigated for the Theory Validation
Therefore, we introduced two dispatching modes for those shipments: 1. In the first mode, we dispatch the whole shipment size to CRRT. This option constitutes the first shifting scenario, called \"All ILUs on Rail\" (ARA). In this scenario, the whole shipment, regardless of the shipment size, is dispatched to CRRT, meaning that one ILU is less utilized than 80%. This implies that some ILUs on rail are not well utilized, and the costs for renting, shipping, and handling the goods in this ILU are higher than for the better-utilized ones, but costs for direct road transport are obeyed. Before dispatching the ILUs via intermodal transportation, it is checked whether the ILU can reach the consignee on time with the available rail services. If this is not possible, the ILU is shipped directly by road transportation. 2. This implies more cost for a low-utilized road transport. In this scenario, a minimum utilization of 80% is necessary for each load unit to be shipped via CRRT. If this utilization is not reached, the ILU is scheduled for direct road transportation. We call this scenario \"Highly utilized ILUs on Rail\" (URA). Therefore, including the base case, three scenarios are presented for each case study, which are visualized in Figure 3.  
Table 2 presents a summary of the input data that was used for the scenarios. The difference between the total and considered shipments is based on constraints 1 and 2 of the enumeration. The emission data that is used is a combination of dynamic and static data. For the transportation legs, we use the EcoTransIT World (ETW) emission calculator as the shipments reflect real-world transports that have been conducted. ETW offers a comprehensive calculation methodology that accounts for real-world movements of ships and flights and integrates up-to-date emission factors, emission quantification standards, and traffic networks [72]. To address ILUs in the hubs, we use a static value of 30 kg CO\({}_{2}\)e per transshipment activity, as suggested by the GLEC framework [73]. Emission intensities of transshipment activities differ between hubs and equipment used, but no detailed data was available to us regarding terminal-specific emission factors.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 3 Analysis  
### Dynamic Analysis of the System with Pedestal Looseness  
The study aims to analyze the vibration response characteristics of the system at certain rotational speeds to further investigate the influence of the looseness failure on the vibration behaviors.  
Figure 13a,b show the shaft centerline orbit of the system before and after the looseness failure at a rotor speed of 1428 rad/s, where \(l\) denotes the distance of the node from the left end of the rotor shaft, and \(V,W\) denote the displacements of the node in the \(Y,Z\) directions, respectively. The shaft centerline orbit of the nodes located in the LP rotor shaft without loosening fault are regular patterns, which indicate that the nodes are in cyclic motion at this time. The shaft centerline orbit of LP compressor No. 3 is a circle, which indicates that the node is in simple harmonic motion and has only one dominant frequency, while the shaft centerline orbit of the rest of the nodes are regular and complex, which indicates that the vibration signals of the nodes contain multiple frequencies. After the looseness failure, the shaft centerline orbit of the fan and LP compressor become complex and irregular, and the shaft centerline orbit of the LP turbine is an approximate circle, which indicates that the loosening of the bearing housing located in the LP compressor mainly affects the vibration of the fan and the LP compressor, and the effect on the LP turbine is relatively small.  
Figure 14a,b show the shaft centerline orbit of the HP rotor to and subsequent to the act of loosening. Before loosening, the HP rotor is encountering simple harmonic motion at this speed. Once the pedestal in the LP rotor shaft is loosened, the intricate vibration response is transmitted to the HP rotor via the inter-shaft bearing. This causes the vibration response of the HP rotor to become intricate as well with an overall increase in amplitude. The proximity of the node to the inter-shaft bearing directly correlates with the magnitude of the impact caused by looseness failure.  
Figure 13: Shaft centerline orbit of LP rotor at 1428 rad/s. (**a**) Faultless LP rotor. (**b**) LP rotor with slight loose fault.  
The spectrum of the steady-state responses at the fan and LP compressor before and after the looseness failure can be derived by applying a rapid Fourier transform to the vibration data, as shown in Figure 15. The fan oscillates at 227 Hz, which is equal to the LP rotor frequency \(f_{L}\). After the failure, the frequency spectrum shows \(\frac{1}{2}\) times the HP rotor frequency \(\frac{1}{2}f_{H}\), and the components of the frequency are close to \(f_{L}-\frac{1}{2}f_{H}\), i.e., 147.5 Hz and 80 Hz. This characteristic frequency is caused by the failure, and thus it is more obvious in the node where the failure occurs. The spectrum of the LP compressor before the breakup contains \(f_{L}\) and \(f_{H}\); the spectrum after the breakup shows a significant \(\frac{1}{2}f_{H}\) and an approximate \(f_{L}-\frac{1}{2}f_{H}\) frequency component.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 4 Image Compression
Particularly in the field of remote sensing information, lossless compression and lossy compression are often used for comprehensive processing, so as to make operational processing more convenient. (3). Measurability  
Measurability refers to gradual transmission by image quality, resolution, component, or spatial locality. Specifically, it can realize the transmission of the overall outline, and then gradually insert pixels into it to improve the image resolution. This makes it easy to quickly scan the image without considering the details of the image. (4). Support area-of-interest coding  
We can freely choose the region of interest, arbitrarily access and process this region, and realize the priority display of this region. It is possible to compress the selected area of interest and other areas in different proportions to achieve a low pressure ratio and even lossless compression in some areas to ensure the appropriate roles of image details and other information. (5). Good error code robustness  
Robust coding technique refers to preserving some redundant information in the process of encoding the original image information. This redundant information is used to resist errors in transmission [19].
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 2 Experimental Examination of the SOFC Stack
### Experimental Results  
The relations between the current, voltage and power for the stack, which works under the different furnace temperature--which determines the stack operating temperature--and under the different fuel composition (i.e., concentration of the hydrogen and nitrogen at the stack inlet) have been examined. The experimental results are presented as the current-voltage and current-power characteristics, since many parametric studies on the SOFCs focus on these relations. The results are shown in Figure 3.  
As shown in Figure 3a, increasing furnace temperature leads to the higher voltage at the same load. The difference between the cell potential at the temperature 650 \({}^{\circ}\)C and 800 \({}^{\circ}\)C and at the load 10 A is almost 0.2 V. The power in the further case is around 0.05 W cm\({}^{-2}\) higher. This effect is caused by the faster electrochemical kinetics and increased ionic conductivity of the YSZ in the higher temperature. The performance of the stack experiences a relatively large increase between 650 \({}^{\circ}\)C and 700 \({}^{\circ}\)C, whereas the performance is only slightly higher between 700 \({}^{\circ}\)C and 800 \({}^{\circ}\)C (see Figure 3a).  
\begin{table}
\begin{tabular}{l l} \hline fuel flow rate & 2.6 L min\({}^{-1}\) \\ fuel pressure & 3 bar \\ fuel inlet heater temperature & 350 \({}^{\circ}\)C \\ \hline air flow rate & 28.8 L min\({}^{-1}\) \\ air pressure & 5 bar \\ air inlet heater temperature & 550 \({}^{\circ}\)C \\ \hline afterburner temperature & 400 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 3: Modular stack test bench operating conditions.  
Figure 2: Configuration of the modular stack test bench.  
Figure 3b presents the results for the different inlet fuel composition. As can be observed, the higher fraction of hydrogen in the fuel leads to slightly better performance and higher power. Nevertheless, the effect of the hydrogen concentration in the fuel is not as significant as the effect of temperature. Please note that the lower the molar fraction of hydrogen is in the fuel mixture, the higher is the utilization factor; therefore, for the fuel with \(\chi_{\text{H}_{2}}=0.4\), the maximum achieved power density is lower than for the case with the fuel containing \(\chi_{\text{H}_{2}}=0.7\).