esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 2 Intelligent Temperature Control System for Large Aqueduct  
### Architecture of Intelligent Temperature Control System  
In the early stage of the project, the simulation calculation module is used to simulate and calculate the actual boundary conditions of the project, concrete parameters and the temperature stress field of the aqueduct concrete under different water-cooling conditions. The temperature changes of the concrete at the characteristic point obtained under the working conditions where the stress value of the characteristic point is close to the design value of the tensile strength is taken as the initial temperature variation control curve and stored in the information processing and decision-making module to provide a reference for the intelligent regulation of the water flow rate; At the construction site, the temperature instrument records the machine inlet temperature, warehousing temperature and pouring temperature in real time and automatically transmits them to the information processing and decision-making module, providing necessary data preparation for temperature control effect evaluation, early warning and forecasting and intelligent regulation. During the construction process, the temperature sensor is fixed at the corresponding position of the concrete. The temperature and humidity meter, solar radiation meter, anemometer, flow meter and temperature sensor are used to monitor the temperature and humidity, solar radiation, wind speed, water flow rate, water temperature and other information on the surface of the pouring bin. Data are transmitted to the information processing and decision-making module for storage and management in real time through the signal transmission line and signal acquisition transmitter. The information processing and decision-making module determines the flow rate of cooling water and the valve switch according to the measured temperature variation and temperature variation control curve of concrete characteristic points. The simulation calculation module simulates and calculates the temperature stress field of the aqueduct based on the real boundary conditions and the adjusted cooling water parameters and then transmits back to the information processing and decision module the temperature stress data at the characteristic points. The total control box in the information collection and regulation module receives the regulation instructions from the information processing and decision-making modules, controls the water flow rate and water valve of the water pipe through the connected water pump and solenoid valve and realizes the ideal temperature control of the concrete. The system architecture is shown in Figure 1, and the closed-loop control block diagram is shown in Figure 2.
## 3 The OPTCON Algorithm  
### The OPTCON1 Algorithm  
The first version of the OPTCON algorithm, the OPTCON1 algorithm, was developed by [9]. It allows us to calculate an open-loop (OL) solution to a nonlinear stochastic dynamic optimal control problem with a quadratic objective function under additive and parameter uncertainties. Open-loop controls either do not take account of the effect of uncertainties in the system or assume the stochastics (expectation and covariance matrices of additive and multiplicative disturbances) to be given for all time periods at the beginning of the planning horizon. The basic idea behind this algorithm is that it extends linear-quadratic stochastic optimal control techniques (see, e.g., [1]) to nonlinear problems using an iterative method of linear approximations.  
The OPTCON algorithm solves the nonlinear optimal control problem iteratively by a sequence of linear approximations where the next approximation is derived using the optimal control solution of the previous one. The (optimal) solutions of the intermediate linear approximations are iterated from one time path to another until the algorithm converges. The criterion for convergence is that the difference in the values of the state and control variables of current and previous iterations is smaller than a pre-specified number. or the maximal number of iterations is reached. In each iteration the same procedure is conducted, namely linearizing and optimizing the linearized system. The system is linearized around the previous iteration's result and the problem is solved for the resulting time-varying linearized system. The solution of the linearized model is obtained via [PERSON]'s principle of optimality and is used as the tentative path for the next iteration, starting off the procedure all over again. If the OPTCON algorithm converges, the solution of the last iteration is taken as the approximately optimal solution to the nonlinear problem and the value of the objective function is calculated. Figure 1 shows the structure of the OPTCON1 algorithm.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 5 Conclusions  
The need to feed the population is prioritized as a result of population growth. Identification of high-consumption crops and their alternative varieties is one of the main issues in future planning to meet the nutritional needs of large-scale crop cultivation. Supplying the cultivated crop's fertilizer needs, awareness of water needs, and early detection of anomalies are all critical in the second stage. These issues were largely solved by new technologies such as remote sensing and the use of satellite imagery. When compared to that of traditional classification methods, the Copernicus program develops the potential for classification through the simultaneous use of multiple sensors. The classification accuracy was said to improve with the combination of radar data and optical signals in previous studies because one of the benefits of combining these data is that the cloud effect is reduced. We used 10-day Sentinel 2 smoothed NDVI mosaics and 12-day Sentinel 1 back mosaics to create a classification map for the 2020 growing season in Tarom. The random forest method was used for classification purposes. This study reported a kappa coefficient of 0.75 and a maximum accuracy of 85 percent. It is recommended to use a combination of radar and optical data to achieve maximum classification accuracy, as this combination will increase the chances of examining the details compared to a single-sensor classification method and provide more reliable information. This study found that combining optical and radar data was the most important factor in predicting the final classification, and that using optical data alone produced acceptable results. Finally, because the level of reliability is low in areas such as closed border areas, it is much more difficult to predict the classification than to give a general conclusion using such methods. It is also necessary to compare the results of various sensors to better assess their ability to classify crops and to assess the potential of various sensors for such research.  
Conceptualization, A.S.; methodology, A.S.; validation, S.F., A.G., K.M.; formal analysis, A.T., M.A.; writing--original draft preparation, A.S. and S.F.; writing--review and editing, A.M.; visualization, A.M.; supervision, A.S.; funding acquisition, N.Z. 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 42071374.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
The authors would like to thank [PERSON] for her helpful assistance.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Characteristics of the Shale Reservoirs and Their Influencing Factors  
#### 4.3.1 Physical Characteristics of the Shale Reservoirs  
Sample porosities ranged from 1.17-7.8%, averaging 3.13%; most (71.4%) had porosities of 2.0-4.0% (Figure 7a). The horizontal permeabilities of shale reservoir intervals ranged between 0.002 and 335.209 mD, with an average of 23.785 mD. All samples containing microfractures had permeabilities >1 mD, including the highest-permeability sample. In comparison, samples free of microfractures generally had permeabilities around 1 mD. The porosities and permeabilities of the Dalong shales in well ED-2 are positively correlated (Figure 7b). There is no apparent correlation between TOC and porosity (Figure 7c). Quartz content showed a weak negative correlation with porosity, whereas clay mineral content showed a weak positive correlation with porosity; carbonate mineral content showed no apparent correlation (Figure 7d).
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 3 Methodologies
### GA-SPO Hybrid Algorithm
#### 3.3.2 GA-PSO Operation  
Based on PSO, because the speed and position update operation of the defined particles is complex, the ICO is used to replace the update operation of PSO, and the parameter control algorithm of GA-PSO is used for execution. The operational improvements are as follows:  
1. Definition of particle position: According to the characteristics of TTSA solution space of the transmission task, the particle swarm coding is represented by the preceding text; that is, the \"position\" of the particle is represented by an orderly task sequence number. When the algorithm is started, a random sequence is used to initialize the particles. The goal of the algorithm is to continuously update the particle's position, so that the task allocation sequence represented by the position information can obtain the maximum comprehensive effect evaluation value through Formula (10)--that is, continuously optimize the Result sequence, and obtain the optimization result of the particle swarm optimization, which is recorded as: \[X_{i}=\big{(}x_{i1},x_{i2},\ldots,x_{ij},\ldots,x_{in}\big{)},i=1,2,\ldots,K,x_ {ij}=Tr_{j}.\textit{TaskNo}\] (17) The definition of particle best and global best is the same as the definition of particle position, which is recorded as: \[\textit{Pbest}_{i}=\big{(}p_{i1},p_{i2},\ldots,p_{ij},\ldots,p_{in}\big{)},i=1,2,\ldots,K\ \ \ \ \ p_{ij}=Tr_{j}.\textit{TaskNo}\] (18) \[\textit{Gbest}=(g_{1},g_{2},\ldots,g_{i},\ldots,g_{n}),\ \ \ g_{i}=Tr_{i}.\textit{TaskNo}\] (19)
2. Particle update operation based on probability selection and ICO: When the particle branch direction is calculated according to the parameters, and inertia operation isperformed according to the parameters, some particles move to global best, and some particles move to particle best. In order to avoid the problem whereby the motion direction and speed cannot be defined by a vector, the ICO is used to replace the particle motion operation for the move toward global best and particle best. One of the two generations is selected with a better evaluation value to replace the particle position to realize particle iteration. This achieves the goal of simplifying the PSO. The particles performing inertia operations adopt MO to increase the diversity of the particles.
3. Algorithm termination: When the defined number of iterations is reached, the algorithm terminates, and the global best is the output of the algorithm's optimal solution, ending the algorithm calculation.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 3 Conventional Architectures  
[PERSON] et al. have categorized solar plant monitoring as (1) manual inspection, (2) failure mode and effects analysis, (3) machine learning and forecasting, and (4) IoT-based monitoring [12; 25]. It was concluded that though IoT-based monitoring is the most expensive, it provides the highest accuracy [12; 26]. In this context, numerous solutions have been proposed in the existing literature; we have presented these literature reviews in Tables 2 and 3 in detail. Efforts are afoot to provide low-cost, scalable, reliable, and efficient IoT-based monitoring systems. The computing architectures of these IoT solutions can be broadly categorized as either centralized or cloud-based. The underlying architecture of these solutions generally has four layers for both centralized and cloud-based solutions. These layers range from perception, network, support, and application layers [22], and are shown in Figure 2.  
1. Perception Layer comprises actuating and sensing parameters at the panel or array level depending upon the proposed solution. 2. Network Layer is represented by wireless inter-networking (within a farm) or inter-networking (between solar farms and remote servers). 3. Support layer is where information is stored and processed and raw data are translated into valuable information. 4. Application layer provides an interface for clients, stakeholders, or researchers to offer insights into the viability of installed solar plants. However, depending on solar plants' size and monitoring objectives, different proposed solutions make some of the layers redundant, and these redundancies are observed and presented in Tables 2 and 3 in detail.
## 5 Results and Discussion
### Quantitative Results
#### 5.1.2 LSU Boundary Detection  
In this study, we also evaluated LSU boundary detection by comparing the results against two different algorithms for scene detection: [26], which uses a variety of visual and audio features that are integrated in a Shot Transition Graph (STG); and [27], which uses a spectral clustering algorithm and Deep Siamese network-based model to detect scenes. We used the same 10 videos from the RAI dataset for validation. Table 2 tabulates the coverage and overflow measures calculated based on the above methods. Our experimental results indicate that the model in [26] has the highest coverage value of 0.8--but it also has a very high overflow measure. Ref. [27] provides a comparatively better overflow result and overall performance than [26]. Although our approach achieved a lower coverage measure, it has obtained a very good overflow measure, which has resulted in a higher \(F_{score}\). Our approach, with an average \(F_{score}\) of 0.74, outperformed the other methods by more than 10%.
# A Charged Relativistic Engine Based on a Permanent Magnet
## 1 Introduction
It was also shown that the energy exchange between the engine's mechanical components and the electromagnetic field plays a crucial role [18]. One key finding was that the total electromagnetic energy consumed by the system is six times the kinetic energy gained by the relativistic motor. The studies also highlighted that improper coil configurations could result in some energy being radiated away, reducing the engine's efficiency. Earlier research assumed that the materials involved in the system were electrically neutral, with equal charges from electrons and nuclei across each volume element. However, this assumption was later revisited [19], allowing for the consideration of charged bodies. This shift enabled the exploration of how charge affects the functioning of an electric relativistic engine. The aim of this paper is to assess a relativistic engine that relies on a permanent magnet, specifically focusing on the interaction between a charged body (a capacitor) and the magnetic current generated by a permanent magnet. This configuration holds particular appeal for certain applications due to the self-sustaining nature of a permanent magnet, eliminating the need for an external power source. We mention that the macroscopic limitations of charge density due to dielectric breakdown can be somewhat mitigated on the atomic level as was shown in [20] and in more recent papers [21; 22]. We conclude the introductory section by specifying the list of contributions of the current paper:  
1. We introduce a theory of relativistic engine in matter (electromagnetic characteristics of macroscopic matter are considered). 2. The theory is applied to matter with permanent magnetization. 3. It is shown that a charged relativistic engine based on a permanent magnet can be constructed. 4. A calculation of the relevant characteristics and parameters of the engine is given.
# Study of the Effects of Plasma Pretreatment on the Microstructure of Peanuts
## 2 Materials and Methods
### Experimental Procedure  
#### 2.3.1 Plasma Pretreatment of Peanuts  
Spread an appropriate amount of peanuts on a tray and place them inside the sealed chamber of the plasma treatment equipment. Evacuate the vacuum chamber to 110 Pa using a vacuum pump. Set the plasma source frequency to 40 kHz and treatment durations to 30 s, 60 s, 90 s, 120 s, and 150 s. Adjust the cold plasma treatment power to 100 W, 200 W, 300 W, 400 W, and 500 W. Introduce helium gas (He), oxygen gas (O\({}_{2}\)), and argon gas (Ar) [26; 27; 28]. After treatment, temporarily store each group of peanuts in a 4 \({}^{\circ}\)C refrigerator. Untreated peanuts from the same batch serve as the control group.
## 2 Algorithm Theory and Method
### Operating State Identification  
During the whole service life of the equipment, when it traverses the evolution process from normal to abnormal to the fault state, the historical data collected must include extensive data in the normal state, some data in the abnormal state, and little data in the fault state. Therefore, it is necessary to identify the different operating states of the equipment.  
The _K-means_ algorithm adopts the distance as the judgement index to divide the data set into \(K\) categories, based on the largest data difference among different categories and the highest degree of data similarity within the same category.  
The historical data set is recorded as \(X=[X_{1},X_{2},\ldots,X_{n}]\), wherein \(X_{1},X_{2},\ldots,X_{n}\) are the \(n\) data objects corresponding to \(n\) sensors; and the dimension of each group of data objects is \(N\), i.e., \(X_{i}=[x_{i1},x_{i2},\ldots,x_{iN}]\). Let \(K\) be the number of cluster centres, and select \(K\) initial points as centroids, and the set of corresponding cluster centers is \(C=[C_{1},C_{2},\ldots,C_{K}]\), for \(C_{j}=[c_{j1},c_{j2},\ldots,c_{jn}]\).  
\[J(C,X)=\min(\sum_{j=1}^{K}\sum_{i=1}^{n_{j}}d(C_{j},X_{i})) \tag{6}\]  
where \(n_{j}\) is the number of points of the data object of category \(C_{j}\), and \(d\big{(}C_{j},X_{i}\big{)}\) is the Euclidean distance between the centre of the category and the data object, which is used to measure the similarity between the data. The smaller the calculated value is, the greater the similarity is, which is defined as follows:  
\[d\big{(}C_{j},X_{i}\big{)}=\sqrt{\left(c_{j1}-x_{i1}\right)^{2}+\cdots+\left( c_{jn}-x_{in}\right)^{2}} \tag{7}\]
## 3 Results
### CatWalk XT(r) Automated Gait Analysis
#### 3.1.4 Base of Support (BOS)  
The base of support (BOS) is another well-established parameter in the CatWalk XT\({}^{\circledR}\) gait analysis, representing the distance between the forelimbs or the hindlimbs and measured perpendicular to the direction of walking. A lower BOS indicates a higher trunk stability and thus, BOS is typically increased after SCI [14].  
For the forelimbs of animals in the SCI group, the BOS significantly increased to 6.67 \(\pm\) 0.23 cm two weeks after the injury (N2; baseline vs. N2; \(p\) < 0.0001; Figure 2E), but then decreased, reaching a significant level of recovery already three weeks after SCI (N3) with 5.24 \(\pm\) 0.42 cm (N2 vs. N3; \(p\) = 0.0016). Spontaneous recovery continued and the lowest BOS was reached five weeks after SCI (N5) with 3.54 \(\pm\) 0.44 cm (N2 vs. N5; \(p\) < 0.0001).  
Similarly, the BOS of the hindlimbs showed a significant increase from 2.88 \(\pm\) 0.08 cm at baseline to 6.46 \(\pm\) 0.26 cm two weeks after the injury (N2; baseline vs. N2; \(p\) < 0.0001; Figure 2F), and then gradually decreased, indicating spontaneous recovery which became significant already three weeks after SCI (N3) with 5.48 \(\pm\) 0.35 cm (N2 vs. N3; \(p\) = 0.027). Five weeks after SCI (N5), the BOS of the hindlimbs had reached the lowest level with 4.05 \(\pm\) 0.38 cm (N2 vs. N5; \(p\) < 0.0001), while it slightly increased to 4.26 \(\pm\) 0.31 cm at the end of the experiment (N2 vs. N6; \(p\) < 0.0001).  
Figure 2: Regularity index (**A**), average speed (**B**), stride length of the forelimbs (**C**) and the hindlimbs (**D**) as well as base of support of the forelimbs (**E**) and the hindlimbs (**F**) for animals in the SCI and the Sham group from baseline (BL) until the end of the experiment (N6). In the SCI group, a similar pattern of a significant deterioration from the BL values after the injury followed by spontaneous recovery, eventually reaching a significant level compared to the first postinjury value (N2) could be observed for all presented CatWalk XT\({}^{\circledR}\) parameters (\({}^{\star}\)\(p\) < 0.05; \({}^{\star\star}\)\(p\) < 0.01; \({}^{\star\star\star}\)\(p\) < 0.0001).
# Fine-Grained Boundary Conditions in Field-Based Routing
## 2 Related Work  
Routing has been extensively studied as a fundamental issue in the field of networking from various perspectives, including different target network systems and metric designs. In this context, various routing protocols applicable to industrial WMNs have been also introduced in the past, each with different metrics, including shortest-path routing [8], geographic routing [9], back-pressure routing [10], and combination-type routing [11], which considers both the shortest path and congestion levels.  
In contrast to traditional approaches in designing routing strategies, several field-based routing protocols have been introduced for WMNs or wireless sensor networks. These protocols draw inspiration from fundamental physical principles. For example, in [12], a mechanism targeting WMNs in underground mines considers both the distance to the gateway and the energy factor, drawing inspiration from the principle of the potential field. Additionally, anycast routing schemes for WMNs were proposed. One was designed to consider the distance and path robustness based on [PERSON]'s law [13], while the other was devised to consider the proximity to gateways and congestion levels through inspiration from Poisson's Equation [14].  
This approach allows these protocols to inherit the properties of their parent principles. To fully leverage the parent principle as desired, it is naturally required to understand the main logic in the laws or equations. The boundary condition problem addressed in this paper is an important problem related to the main logic, which has been unexplored so far. This motivates us to study this problem.