esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based Framework for Smart Water Supply Systems Management
## 4 Background
### Complex Event Processing  
Complex Event Processing (CEP) is a technology for capturing and analyzing event streams in order to identify patterns of behavior in real-time [20]. CEP plays an important role in many application areas, such as smart metering, energy management, and irrigation for agriculture [21]. In the context of water management systems, one could use CEP to measure pressure and volume of water flow in pipes, to identify patterns of water leakage, anticipate accidents. A set of CEP rules specifies each pattern. A rule defines filtering, aggregation, correlation, and transformation of data flows. Figure 6 illustrates the CEP architecture. The CEP engine receives the event streams and applies the CEP rules to identify predefined patterns. As soon as a pattern is recognized, the CEP engine sends an alert to notify human operators or other systems.  
CEP rules are queries to perform filtering, aggregation, correlation, and transformation of data flows. This work adopts Event Processing Language (EPL) to specify CEP rules. In this work, each CEP rule defines an existing water management policy. For instance, the rule in Figure 7 checks every 2 min if the average pressure in 12 seconds is higher than a critical limit. In addition to the water management rules, this work uses CEP rules to identify undesirable situations, such as rapid reduction of the water level in the water storage tank, or critical level of water (too high or too low), and send an alert to water managers to report the pattern.  
Figure 5: Water life cycle of a water supply system.
## 3 Methods
### Two-Stage Decomposition
\begin{matrix}\underset{\{u_{k}\},\{\omega_{k}\}}{\text{min}}\left\{ \sum\limits_{k}\left|\left|\partial_{t}\right|\left[\left(\delta(t)+\frac{j}{ \pi t}\right)\ast u_{k}(t)\right]e^{-j\omega_{k}t}\right|\right|_{2}^{2}\right\} \\ \sum\limits_{k}u_{k}=f\end{matrix} \right. \tag{2}\]  
where \(\{u_{k}\}\) and \(\{\omega_{k}\}\) are the respective modal functions and the corresponding central frequencies, and \(f\) is the sum of each modal function sequence. (2) Add a quadratic penalty factor \(\alpha\) and Lagrange operator \(\lambda\) to the original problem, so that the constrained variational problem becomes an unconstrained variational problem, which is as follows:  
\[L(\{u_{k}\},\{\omega_{k}\},\lambda):=\alpha\sum\limits_{k}\left|\left|\partial_ {t}\right|\left[\left(\delta(t)+\frac{j}{\pi t}\right)\ast u_{k}(t)e^{-j\omega _{k}t}\right]\right|\right|_{2}^{2}+\left|\left|f(t)-\sum\limits_{k}u_{k}(t) \right|\right|_{2}^{2}+\left\langle\lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\right\rangle \tag{3}\]  
Optimize the unconstrained variational problem, and using the alternating direction multiplier algorithm to obtain \(\omega_{k}\) and \(u_{k}\) in Equation (3), and iteratively update \(\omega_{k}\), \(u_{k}\) and \(\lambda\) in the frequency domain. (4) The iteration ends when the following conditions are satisfied. \[\frac{\sum\limits_{k}\left|\left|u_{k}^{n+1}-a_{k}^{n}\right|\right|_{2}^{2}} {\left|\left|\left|\dot{u}_{k}\right|\right|_{2}^{2}}<r \tag{4}\]  
where \(r\) is the accuracy threshold. The effectiveness of VMD for traffic flow sequence decomposition depends largely on the setting of the following two parameters: the number of BIMFs \(k\) and the quadratic penalty factor \(\alpha\). When \(k\) is larger or smaller, it will lead to over or under decomposition of the decomposition, and when it is not set properly, the modal mixing phenomenon will occur. It is difficult to set \(k\) and \(\alpha\) artificially make the VMD achieve the best decomposition, so the optimal combination \([k,\alpha]\) is solved by using GA with the envelope entropy as the fitness function, the workflow of GA is shown in Figure 1.  
Figure 1: Genetic algorithm flow chart.
## 3 Results
Figure 3: Hardness Vickers (HV) curves of the aged alloy at 150, 180, and 210 \({}^{\circ}\)C.  
Figure 2: Microstructure of the annealed alloy at 605 \({}^{\circ}\)C for (**a**) 1 h and (**b**) 3 h, and the distribution of alloying elements inside the white square on the SEM microstructure. The ingot of the investigated alloy was quenched at 605 \({}^{\circ}\)C after homogenization treatment for 3 h, rolled to a 1 mm thick sheet, and annealed at different temperatures. The HV vs. temperature and time curves of the annealed alloy after rolling are shown in Figure 5. Figure 5a demonstrates the evaluation of HV after 1 h of annealing at 100-550 \({}^{\circ}\)C in comparison with that of the Al-6 Cu-4.05 Er [19] and Al-4 Cu-2.7 Er-0.3 Zr [30] alloys. The investigated alloy has a high hardness due to the addition of manganese. Two processes can take place during the annealing of the as-rolled investigated alloy: softening and strengthening. Softening during low temperature after rolling proceeds due to a decrease of the dislocation density and formation of the substructure with low angular boundaries. The fine particles of the Al\({}_{3}\)(Zr,Er) and Al\({}_{20}\)Cu\({}_{2}\)Mn\({}_{3}\) phases retard softening. Strengthening is promoted due to aging processes and the nucleation of metastable \(\theta\)\"(Al\({}_{2}\)Cu) precipitates. A deformed structure with a high density of dislocations and vacancies accelerates strengthening. The HV peak was found after 1 h of annealing at 150 \({}^{\circ}\)C (Figure 5a) and after 2 h of annealing at 150 \({}^{\circ}\)C (Figure 5b). Strengthening by \(\theta\)\"(Al\({}_{2}\)Cu) precipitates has a larger effect on material hardness than do softening processes at 150 \({}^{\circ}\)C. The hardness decreases by increasing the annealing temperature above 150 \({}^{\circ}\)C (Figure 5a). The hardness peak shifted to 0.5-1 h by increasing the annealing temperature up to 180 \({}^{\circ}\)C (Figure 5b). Softening has the largest effect on hardness at temperatures higher than 180 \({}^{\circ}\)C (Figure 5b). The recrystallization temperature of the alloy is in the range of 350-400 \({}^{\circ}\)C. A non-recrystallized grain structure was found after 1 h of annealing at 350 \({}^{\circ}\)C (inserted image in Figure 5a). The average grain size increased from 7.3 to 10.6 \(\upmu\)m by increasing the annealing temperature from 400 to 550 \({}^{\circ}\)C (inserted images in Figure 5a). Tensile test samples were annealed at aging temperatures for different amounts of time. Results of the tensile tests are summarized in Table 2. The rolled yield strength (YS) is 344 MPa. Increasing annealing temperature from 150 to 210 \({}^{\circ}\)C leads to decreases in YS from 320-332 MPa to 290-298 MPa. The YS has the same value for up to 10 h of annealing. For example, the Al-4 Cu-2.7 Er-0.3 Zr alloy has a YS of 268-274 MPa after annealing at 150 \({}^{\circ}\)C [30]. Alloying with Mg and Ti can further improve the strength of the investigated alloy. Figure 5: HV vs. (**a**) temperature and (**b**) time curves of the annealed alloy after rolling.
## 4 Warm Core Structure Retrieval Experiment
### Bias Correction of MWTS and MWHS  
Before conducting the retrieval experiment, it is necessary to perform bias correction on the instruments. Some microwave instruments have asymmetric deviations, such as AMSU on the NOAA-15 satellite [31]. Recently, [PERSON] [32] and [PERSON] et al. [24] also mentioned this scan-angle-dependent bias on FY-3D and FY-3E. In order to maximize the elimination of this phenomenon, [PERSON] et al. [31] provided a correction algorithm, as follows:  
\[\mathrm{\Delta T_{b}}=\mathrm{A_{0}}\mathrm{exp}\Bigg{\{}-\frac{1}{2}\Bigg{[} \frac{(\mathrm{\theta_{s}}-\mathrm{A_{1}})}{\mathrm{A_{2}}}\Bigg{]}^{2} \Bigg{\}}+\mathrm{A_{3}}+\mathrm{A_{4}}\mathrm{\theta_{s}}+\mathrm{A_{5}} \mathrm{\theta_{s}}^{2} \tag{12}\]  
where \(\mathrm{\Delta T_{b}}\) represents the corrected brightness temperature, \(\mathrm{\theta_{s}}\) represents the satellite zenith angle, and \(\mathrm{A_{i}}\) (i = 0,..., 5) represents six fitting coefficients. In the fitting algorithm, O-B is \(\mathrm{\Delta T_{b}}\). By substituting \(\mathrm{\theta_{s}}\) and using the least squares method, the coefficients are fitted. The final corrected brightness temperature is defined as follows:  
\[\mathrm{T_{b}}=\mathrm{T_{b0}}-\mathrm{\Delta T_{b}} \tag{13}\]  
where \(\mathrm{T_{b}}\) and \(\mathrm{T_{b0}}\) represent corrected and original brightness temperature, respectively. In this experiment, each channel of the FY-3F MWTS and MWHS is individually fitted based on the coefficients \(\mathrm{A_{i}}\) (i = 0,..., 5).  
Figure 9 shows the variation of the O-B for two channels of FY-3F MWHS (channels 5 and 11) and two channels of FY-3F MWTS (channels 9 and 12) with respect to the FOV scan angle. The red line and black line represent the O-B of pre-correction and post-correction, respectively. The biases of some channels exhibit an asymmetric distribution with respect to the scan angle. For instance, at the first FOV, the average bias of MWHS channel 11 is approximately \(-2\) K, which decreases to around 0 K at the 98 th FOV. It can be found that the corrected O-B distribution is close to 0 K.  
Figure 9: Comparison of FY3F MWHS channel 5 (**a**) and 11 (**b**) and MWTS channel 9 (**c**) and 12 (**d**) O-B before correction and after correction (units: K).
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 4 Conclusions  
In this study, innovative CuW composites enhanced with graphene were prepared for use in high-voltage circuit breaker electrical contacts, employing an integrated vacuum infiltration technology. When compared to conventional CuW composites, the addition of graphene notably enhanced the comprehensive mechanical and electrical properties of  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Test Item**} & \multicolumn{2}{c}{**Size Changes before and after Ablation**} \\ \cline{3-4}  & & **CuW80** & **Gr-CuW80** \\ \hline \multirow{2}{*}{Stationary arc contact} & End diameter of contact & 0.33 & 0.46 \\ \cline{2-4}  & Contact length & \(-7.24\) & \(-9.57\) \\ \hline \multirow{4}{*}{Moveable arc contact} & External diameter of contacts & \(-1.63\) & \(-1.83\) \\ \cline{2-4}  & Internal diameter of contacts & \(-1.4\) & \(-1.64\) \\ \cline{1-1} \cline{2-4}  & Thickness of contacts & 0.17 & 0.17 \\ \cline{1-1} \cline{2-4}  & Contact height & \(-3.2\) & \(-4.5\) \\ \hline \end{tabular}
\end{table}
Table 2: Size changes of graphene-enhanced CuW80 electrical contacts and ordinary CuW80 electrical contacts before and after ablation.  
Figure 8: Ablation images of electrical contacts after full-capacity breaking. (**a**) CuW80; (**b**) graphene-enhanced CuW80.  
the materials. Graphene primarily resided within the microstructure defects and grain boundaries of copper and tungsten, effectively filling interfacial gaps and compensating for microstructural imperfections. As a result of this reinforcement, the bending strength, hardness, and electrical conductivity of the CuW composites increased by approximately 10%, 6%, and 20%, respectively.  
The innovative graphene-enhanced CuW electrical contacts exhibited superior ablation resistance, making them suitable for application in a 252 kV/63 kA high-voltage SF\({}_{6}\) circuit breaker. These innovative electrical contacts successfully achieved 20 times effective consecutive full-capacity short-circuit current breaking events during electrical durability testing. It opens a new avenue for the development and application of high-performance graphene-enhanced CuW electrical contacts. Furthermore, it is anticipated that these innovative contacts will find widespread application in high-voltage circuit breakers of even higher voltage grades in the future.
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 2 Materials and Methods  
### Image Sources  
This application has been developed from the following images, obtained on a plot of olive trees in Villena (Alicante) whose center is located approximately at the coordinates of longitude \(-0.879577\) and latitude \(38.596992\) (reference system EPSG: 4326):  
1. Orthophoto of 2019 of the Valencian Community, obtained from the catalog of Valencian Infrastructure of Spatial Data ([[https://idev.gva.es/](https://idev.gva.es/)]([https://idev.gva.es/](https://idev.gva.es/)) accessed on 28 July 2022), elaborated at 25 cm/pixel of resolution (Ground Sampling Distance, GSD), from RGBI digital photogrammetric flights made in the period 14 May 2019-30 June 2019 using the camera UltraCamEagle UC-E-1-50016095-f80 with a Qioptic Vexcel HR Digaron sensor. The Red, Green and Near Infrared (RGI) bands are used in this image. We call this image the original May image.
2. Images were obtained by drone flights made by the company Asdron Spain using a Parrot Sequoia multispectral camera. The images were processed with the PIX4 DMapper package. Of these, the Red, Green and Near Infrared (RGI) bands are used exclusively. The resolution (average GSD) of these images is 6.2 cm/pixel. The flights were made near solar noon, between 13:00 and 14:00 (official local time), on 24 July 2019 (hereinafter referred to as the 'July' image), on 14 August 2019 (hereinafter referred to as the 'original August' image, used for the validation process) and on 18 September 2019 (hereinafter referred to as the 'original September' image). The camera has a sunlight sensor, which performs real-time correction of lighting differences. Reflectance conversion to image grey levels was performed with Pix4D software, which uses previously acquired images of a calibration panel and automatically calculates the appropriate reflectance factor. Image resolution was reduced to 25 cm/pixel by averaging neighboring values and reprojecting.
3. Images of tile 30 SXH from the period 1 May 2019-31 October 2019 of the Sentinel 2 satellite constellation, through the MSI sensor (Multi Spectral Instrument) with the processing level 2A. In total, 29 images are obtained with a percentage of clouds lower than 35%. Of each image, only the bands B4, B3 and B8 are considered, whose resolution (GSD) is 10 m/pixel. Each image was subjected to masking both the clouds and their shadows.  
All images were reprojected from their original coordinate reference system to EPSG:32630 and clipped to the geometry of the plot. Table 2 summarizes main technical specifications.
# Chinese Character Component Deformation Based on AHP
## 2 Related Works  
### Feature Point Detection and Matching  
Currently, there are many highly respected feature matching methods in the field of image processing, such as Harris corners, Shi-Tomas corners, SIFT, SURF, ORB, and GMS.  
Harris [10] determines the feature points according to the change in gray value in the sliding window and uses the idea of the autocorrelation function. Shi-Tomas [11] is an improvement of Harris. It differs in the selection of the final discriminant and enhances the stability of corner detection. The methods in [12; 13] have strong stability and are not disturbed by image rotation and scale scaling, but the calculation process is complex and the running time is long. ORB [14] improves the calculation speed, but there are often many matching errors in practical applications. GMS [15] obtains high-quality matches by eliminating false matching pairs. However, these methods are usually suitable for image with obvious color texture, and the effect is not ideal in Chinese character feature point matching.
## 2 Materials and Methods
### Coverage Path Planning
#### 2.3.4 Genetic Operator Improvement  
Genetic operation is divided into three parts: selection, crossover and variation, which is the core of the Genetic Algorithm. In order to determine the algorithm converge quickly and improve the planning speed, we optimize the selection operation. Before the roulette selection method selects individuals from the population for genetic operation, we sort each individual according to the fitness from high to low, select the good individuals with high fitness as the excellent population according to a certain proportion, and then use the roulette method to select individuals from the excellent population for genetic manipulation. However, this step will reduce the diversity of the population and may cause the algorithm to fall into a local optimum. Therefore, we use adaptive adjustment of the crossover operator and mutation operator and introduce a selection operator to jump out of the local optimum, so that the population as a whole maintains high fitness and evolves to higher fitness, as follows in Formulas (6)-(8).  
\[P_{r} =P_{r\max}-\frac{P_{r\max}-P_{\min}}{t_{\max}}\,t \tag{6}\] \[P_{c} =P_{c\max}-\frac{P_{c\max}-P_{c\min}}{t_{\max}}\,t\] (7) \[P_{m} =P_{m\min}+\frac{P_{m\max}-P_{m\min}}{t_{\max}}t \tag{8}\]  
In the formula: \(P_{r}\) is the selection retention ratio of excellent individuals, \(P_{r\max}\) and \(P_{r\min}\) are the maximum and minimum selection retention ratios of excellent individuals, respectively; \(P_{c}\) is the crossover probability, \(P_{c\max}\) and \(P_{c\min}\) are the maximum and minimum crossover probability, respectively; \(P_{m}\) is the mutation probability, \(P_{m\max}\) and \(P_{m\min}\) are the maximum and minimum mutation probability, respectively; \(t_{\max}\) is the maximum number of iterations; and t is the current iteration algebra.  
In the early stage of algorithm iteration, the population is quite different, rich in species and has good diversity. Selecting a larger retention ratio of excellent individuals and crossover probability can retain a richer variety of individual types and evolve new high-quality individuals at the same time. A lower mutation probability can reduce the possibility of good individuals being destroyed and increase the speed of convergence. In the later stage of iteration, the fitness of individuals in the population is concentrated, and the degree of difference is small. Reducing the retention ratio of excellent individuals and crossover probability and increasing the mutation probability can prevent the algorithm from falling into local extreme values and can search for the global optimal solution.
## 3 Result and Discussion
### CO Emission According to Vessel's Operation
\[DN_{RPM}=\frac{RPM}{Max.RPM} \tag{3}\]  
CO\({}_{2}\) emissions according to the six stages of rpm are shown in Figure 6. Because the study was conducted on two SSD and three HSD vessels, it is difficult to conclude that the results are highly reliable. However, there is a tendency to be divided into HSD and SSD groups in general. For ships corresponding to SSD, the average CO\({}_{2}\) emissions according to each rpm stage are between 1.91 and 2.90, and for HSD, it is between 1.27 and 1.93. The difference in CO\({}_{2}\) emissions according to rpm is up to 16% for HSD and 19% for SSD, which is predicted by differences in the ship's shape and tonnage. CO\({}_{2}\) emissions appear to decrease in the 50-55 section of SSD 1, but this is judged to be an error due to the small number of data in that section. In addition, as rpm increases, CO\({}_{2}\) emissions also increase linearly. Therefore, if various data on emissions are collected for each engine speed, it is expected that a statistical basis for emissions data for each engine rpm can be prepared. Figure 7 shows the CO\({}_{2}\) emissions according to the operating routes of the SSD 1 vessel, which primarily sails the ocean, and the HSD 2 vessel, which sails to fisheries near land. Normally, an engine on each vessel is operated in a steady rpm range at high sea, but the difference in the CO\({}_{2}\) emission quantity is approximately 5 times. Figure 6: CO\({}_{2}\) emission tendency according to rpm variation. Figure 7: CO\({}_{2}\) emission tendency according to rpm variations. (a) SSD 1; (b) HSD 2. There are two vessels that have the most significant difference in their sailing patterns. SSD1, with a slow-speed large engine, fluctuates its rpm frequently to berth. Geographical conditions may affect the differences, but it is suspected that slow-speed large engines, unlike high-speed engines, have a large change in speed according to rpm variations, so they steer the ship using momentary thrust. Even in the high sea, where the rpm is relatively constant, CO2 emissions form a peak, which is expected to be under the influence of instantaneous changes in tides, wind direction, etc. In other words, CO2 emissions are expected to be higher than in normal conditions when ships are sailing in reverse tide or headwind conditions. The CO2 emission characteristics in SSD and HSD show the same pattern where emissions increase as rpm increases, but there is a difference in quantities. Therefore, a standard is necessary to quantitatively evaluate CO2 emissions for various ships, and fuel-based emission factors and power-based emission factors are commonly used. However, unlike cars, it is almost impossible to obtain real-time output data from ship engines because on-board diagnostics (OBD) cannot be connected. Therefore, in this study, a fuel flow sensor was installed to measure the fuel consumption to calculate the fuel-based CO2 emission factor.
## 2 Related Work
### Flow Scheduling  
Existing algorithms can be roughly divided into static scheduling and dynamic scheduling algorithms, according to whether they can feedback the network status in real time and perform corresponding adjustments. Static algorithms, such as the classic FCFS algorithm, have been developed and applied in different scenarios. [PERSON] et al. studied the distribution of AOI in two-hop WNCS in Ref. [18] and devised a problem of minimizing the tail of the AoI distribution with respect to the frequency of generating information updates, i.e., the sampling rate of monitoring a process, under first-come-first-serve (FCFS) queuing discipline. The Baraat system designed by [PERSON] et al. [19], although based on the static scheduling algorithm FIFO, combined with the task-aware network, is a typical dynamic scheduling scheme.  
With the diversification of transmission tasks and requirements, more and more researchers turn their attention to achieving superior performance through the innovation of transmission units. The author puts forward PRISM in Ref. [20], a fine-grained perception of the MapReduce scheduler, and divides it into stages to improve the resource utilization of Hadoop when the job is running. [PERSON] et al. [21] considered the budget and deadline of the MapReduce job and designed a task-level scheduling algorithm based on this constraint. Having successfully optimized a cost-effective budget through the greedy strategy, [PERSON] et al. proposed a new load-balancing solution: DiffFlow [22]. This is a mechanism to forward packets by distinguishing long and short streams and using random packet spraying (RPS) with the help of SDN. However, this situation will potentially lead to a disorderly arrival of data packets.  
Moreover, researchers can also design algorithms with better performance by designing different underlying frameworks. [PERSON] and [PERSON] [23] classified the load-balancing strategies in SDN based on different architectures. In the centralized architecture, a controller manages the connected network devices to concentrate the load-balancing work on the data plane. In the distributed architecture, multiple controllers are used to exchange and connect to each device, or studies such as Refs [24; 25] improve their goals by using MPTCP from the perspective of network protocol.  
In a word, researchers have explored different angles and methods to improve the performance of network transmission, and most of the mainstream research is based on OpenFlow.