esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling and Methods
### Pump-Motor Hydraulic System Model
#### 2.1.1 Electro-Mechanical Conversion Unit  
The electro-mechanical conversion unit is an important part of the hydraulic control system [15]. Its main function is to convert the input electrical signal into a mechanical scalar (force and displacement). As with the previous stage of electro-hydraulic control components, it needs rich steady-state control accuracy, dynamic response performance, anti-interference ability and working reliability [16]. The electro-mechanical conversion unit model is expressed as follows:  
\[\left\{\begin{array}{l}F_{M}=K_{I}I-K_{Y}x_{y}\\ F_{M}=m_{t}\frac{d^{2}x_{x}}{dt^{2}}+\beta_{t}\frac{dx_{y}}{dt}+T\\ K_{y}=\frac{dF_{M}}{dy}+K_{yy}\\ F_{x}=T=m_{p}\frac{dF_{x}}{dt^{2}}+\beta_{p}\frac{dx_{y}}{dt}+K_{V}x_{y}+K_{fV}x _{y}\end{array}\right. \tag{1}\]  
where \(F_{M}\) represents the thrust force of the electromagnet; \(x_{v}\) represents the armature stroke; \(K_{I}\) represents the current force gain of the proportional electromagnet; \(K_{Y}\) represents the sum of the displacement force gain of the proportional electromagnet and the stiffness of the zero-adjusting spring; \(m_{t}\) represents the mass of the armature assembly; \(\beta_{t}\) represents the damping coefficient; \(m_{v}\) represents the quality of the spool; \(x_{v}\) represents displacement of the spool; \(F_{x}\) represents the force between armature and valve core; \(\beta_{p}\) represents the viscous damping coefficient of the spool; \(K_{V}\) represents the spring stiffness of the spool; \(K_{fV}\) represents steady-state hydrodynamic stiffness coefficient.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 3 Fuzzy Rule Generation Method According to Input Space Partitioning Method
### Scatter Partitioning Method
#### 3.2.2 Fuzzy C-Means (FCM) Clustering  
The fuzzy C-means (FCM) clustering method is based on a fuzzy set and LSE by improving the C-means clustering method proposed by [PERSON] [49; 50]. The FCM clustering method is a method of dividing a subdivided specific area by enumerating the values belonging to data of the cluster according to the degree of membership of each data item belonging to one cluster. The FCM clustering method divides a set of \(m\) vectors \(x_{i},\ i=1,\ 2,\ \ldots,\ m\) into \(c\) fuzzy clusters and finds the center within each cluster as the objective function of the non-similarity measure is minimized. In existing clustering methods, arbitrary data belong to one cluster, which has a 0 or 1 degree of affiliation. However, in the FCM clustering method, there is a difference in the degree of membership of arbitrary data between 0 and 1, and they belong to \(n\) clusters. The user sets the number of clusters, where the number of clusters is the number of fuzzy rules.  
Figure 5 shows the FCM clustering method in which clusters are generated according to the number of clusters. The red dot represents the data, and the black dot represents the center of the cluster. Next, the procedure of the FCM clustering method will be described.  
* **Step 1:** Initialize the parameter and the membership matrix to have a random value between 0 and 1 satisfying Equation (10). \[u_{ij}=\left[\sum_{k=1}^{cx}\frac{\|x_{j}-v_{i}\|^{\frac{2}{m-1}}}{\|x_{j}-v_{ i}\|}\right]^{-1}\] (10) Here, the distance calculation between the input data and the center of the cluster is obtained by the Euclidean norm. \[d_{ik}=d(x_{k}-v_{i})=\left[\sum_{j=1}^{n}(x_{ki}-v_{ij})^{2}\right]^{\frac{1}{2}}\] (11)
* **Step 2:** The center value of the new cluster is calculated by the value \(E=\{e_{1},\ e_{2},\ \ldots,\ e_{k}\}\) of the input data and the previously obtained membership function \(u_{ik}\). \[v_{ik}=\frac{\sum_{k=1}^{n}(u_{ik})^{m}x_{kj}}{\sum_{k=1}^{n}(u_{ik})^{m}}\] (12)  
Figure 4: Subtractive clustering method: (a) cluster radius = 0.1 and (b) cluster radius = 0.5.  
* **Step 3:** Using the center value \(v_{ij}\) obtained in Step 2 and the input data \(E\), the membership matrix \(u_{ik}\) is continuously updated while increasing the number of iterations \(r\). \[u_{ik}^{(r+1)}=\frac{1}{\sum_{j=1}^{\zeta}\left[\frac{d_{ij}^{r}}{d_{jk}^{r}} \right]^{\frac{2}{m-1}}}\] (13)
* **Step 4:** The above procedure is repeated until the error of the repeated membership matrix \(U^{r}\) and \(U^{r+1}\) is smaller than the arbitrary threshold value \(\Delta\). \[\Delta=\|U^{r+1}-U^{r}\|=\max_{ik}\abs{u_{ik}^{r+1}-u_{ik}^{r}}\] (14)
## 2 Theory and Computational Methods
The spin polarization, \(P^{D}\), of a magnetic material is usually described in terms of the spin-decomposed densities of states (DOSs) at the Fermi level (\(E_{F}\)) as \(P^{D}=\big{(}N_{\uparrow}(E_{F})-N_{\downarrow}(E_{F})\big{)}/\big{(}N_{ \uparrow}(E_{F})+N_{\downarrow}(E_{F})\big{)}\), where \(N_{\uparrow}(E_{F})\) and \(N_{\downarrow}(E_{F})\) are the spin-up and spin-down DOSs at \(E_{F}\), respectively. This cannot usually be compared with the experimental transport current results, as previously pointed out by many researchers. One simple reason for this is that for the magnetic materials, \(d\)-electrons give rise to the largest contributions to \(E_{F}\). Nonetheless, \(s\)-electrons give rise to the greatest contributions to the transport current measured in the experiment. Indeed, in ferromagnets, the spin polarization of the AHE-induced Hall current, denoted as \(P^{H}\), is interesting, and there are, so far, few studies on this. It is also worth mentioning that \(P^{D}\) may correlate with the magnetic moment of a ferromagnetic material, but is obviously independent of the magnetization direction. In this present study, we calculated the Hall current spin polarization by using \(P^{H}=(\sigma_{xy}^{H\uparrow}-\sigma_{xy}^{H\downarrow})/(\sigma_{xy}^{H \uparrow}+\sigma_{xy}^{H\downarrow})\), where \(\sigma_{xy}^{H\uparrow}\) and \(\sigma_{xy}^{H\downarrow}\) are the spin-up and spin-down Hall conductivities, respectively. Note that by definition, the absolute value of \(P^{H}\) can be greater than 1, while \(P^{D}\) is between 0 and 1. Obviously, a high Hall current spin polarization \(P^{H}\) of a ferromagnet is also useful. Perhaps in some magnetic materials, the spin polarization \(P^{D}\) of the longitudinal current is small, but the spin polarization of the laterally induced Hall current is large. Such magnetic materials could, thus, be used in spintronic devices.
## 2 Geological Background  
The Dongying is a Cenozoic subbasin located in the Bohai Bay Basin (Figure 1A) [3,14]. The Dongying subdepression [15] comprises four subsags: the Lijin area, the Minfeng Sag, the Boxing Subsag, and the Nuzhuang Subsag [3,16,17] (Figure 1B). The structural evolution of the Dongying Depression comprises the syn-rift period (65 to 24.6 Ma) and post-rift stage (24.6 to the present) (Figure 1E) [16,18]. From north to south, the Basin is divided into five tectonic zones: the steep northern slope area, the Minfeng, the central anticline zone, the Nuzhuang zone, and the gentle southern slope area (Figure 1D) [3,15,18,19]. The Dongying Subsag is bounded by the Chenjiazhuang Uplift (North), the Qingtuozi Uplift (East), the Binxian and Qingcheng uplifts (West), and the Luxi Uplift (South) [3,18] (Figure 1B,C). The Dongying is one of China's most prominent hydrocarbon-rich basins, covering an area of 5700 km2 [3].  
Figure 1: (**A**) Localization map of the Bohai Bay Basin, East China. (**B**) Dongying Depression localization map exposing the study field and uplift. (**C**) Stratigraphy columns of the Dongying Depression revealing the tectonic evolution. (**D**) Map of the Lijin area showing the study wells. (**E**) Map of the Yanjia area showing the study wells. (**F**) Dongying Basin showing the tectonic units and the stratigraphy divisions.  
The formations in the Dongying Depression include the Kongdian (Ek), the Shahejie Formation (Es), Quaternary Pinyuan (Qp), the Minghuanzhen (Mm), Dongying (Ed), and the Neogene Guantao (Ng), (Figure 1E) [20,21]. The Es comprises four members, including the Es1, Es2, Es3, and Es4 (Figure 1E). The depositional strata in the north of the Dongying Depression are dominated by conglomerates, pebbly sandstones, and mudstone [22].
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 1 Introduction
[32] proposed a modified solution for earth pressure against the cantilever retaining wall. Considering the typical deformation mode of deep multi-strutted excavation, [PERSON] and [PERSON] [33] deduced the magnitude and distribution of the active earth pressure using the differential flat element method. [PERSON] et al. [34] simplified the soil behind the wall as the combination of nonlinear springs and a rigid plasticity object; in this way, earth pressure on the flexible retaining walls under any lateral deformation can be obtained. Although nonlimited earth pressure against flexible supports has attracted attention from researchers [35, 36, 37, 38, 39], these new methods are not friendly and put forward higher technical requirements for designers. Thus, a simple model should be proposed which can be achieved by conventional methods and be easy to be used widely. Underground excavation for construction is a typical lateral unloading process, in which the stress field and the displacement field of the soil around the foundation pit are disturbed, especially in soft soil. Therefore, it is necessary to explore the variation in earth pressure during the excavation process. In this study, reduced triaxial compression (RTC) tests, which simulate the lateral unloading stress path during excavation, were performed for the stress-strain relationship of soft clay in an active region. Combined with an assumption of soil strain distribution in the disturbed area, an active earth pressure model considering wall displacement was proposed and verified with experimental results from centrifuge model tests and full-scale tests. Figure 1: The earth pressure–displacement curve.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 3 Results
### Spatial Patterns  
Figure 8 shows the annual mean values of GPP, RECO, and NEE from 1999 to June 2020 in the GCFD. Generally, the spatial patterns of the three carbon fluxes were reasonable according to the physical knowledge about the process. As can be seen from Figure 8, GPP predicted by the CNN model showed relatively high values near the equator (mainly rainforest). There were also relatively high values in some places such as central America and the southern part of Asia. This pattern of GPP distribution is consistent with other studies [14; 40]. According to the Koppen classification, most areas with high GPP have an equatorial fully humid or monsoonal climate. These kinds of climate provide adequate conditions of temperature and humidity for photosynthesis, so the GPP values are higher  
Figure 6: Timeseries of GPP (gC m\({}^{-2}\) d\({}^{-1}\)) observed by FLUXNET and predicted by three models at 4 sites, including (**a**) ZA-Kru in subtropic, (**b**) NL-Loo in oceanic climate, (**c**) FI-Hyy in continental climate, and (**d**) CH-Dav in polar climate. Red circles indicate significant overestimation.  
than other areas out of the tropical climates [41]. There is usually a positive correlation between GPP and temperature and precipitation parameters [42]. Recent studies have also revealed that temperature is the dominant factor affecting GPP at a global scale, while water availability is more important at the local scale, especially at low latitudes [43]. Therefore, in some humid areas of lower latitudes such as the southeast part of China, eastern Australia, the southern part of South America, and the southern US, there were also high GPP values, as shown by Figure 8. The spatial pattern of RECO was generally consistent with that of GPP. That is, higher GPP corresponds to RECO, which is consistent with studies that indicated that high RECO values usually appear with high temperature [44; 45]. As recent studies have showed, RECO had the highest values in the tropical area, followed by southeast China, southeast US, and western Europe, and low values in cold and dry areas at high latitude [46; 47]. Regarding NEE, the high values (carbon source) appeared in the high latitudes, while low values (carbon sink) appeared in the tropical rainforest regions. This is consistent with the previous studies showing that NEE is strongly related to air temperature [48]. Recent studies also pointed to low NEE values in Asia and the tropics [49]. In cold and arid areas, previous studies reported that carbon usually releases into the atmosphere [50].  
Figure 8: Global distribution of average GPP (**a**), RECO (**b**), and NEE (**c**) (gC m\({}^{-2}\) d\({}^{-1}\)) from 1999 to June 2020 in the GCFD.  
Figure 7: Annual timeseries of GPP, RECO and NEE for all sites in the global and in seven climate zones. The number of sites in each area is given in brackets.
# Local Attention Sequence Model for Video Object Detection
## 3 Kinematic Control of CDHRM
### Spatial Attention  
The spatial attention module obtains the distribution of spatial attention by modeling the correlation of features at various positions in space and instructs the model to pay more attention to the areas of the feature that can perform subsequent tasks more effectively. As shown in Figure 2, in order to obtain the spatial attention distribution, firstly, the maximum feature response and the average feature response in each grid of the feature map are obtained through max pooling and average pooling operations. Then, these two features are stitched together, and a small convolutional network is used to model the feature correlation in its local area. Following this, the attention distribution of each area is obtained on this basis. Finally, the attention distribution and the original feature map are multiplied in the spatial dimension to strengthen the more noteworthy regional features, and the remaining unimportant regional features are suppressed.  
Formally, let \(\mathbf{F}\) and \(\mathbf{F}^{\prime}\) be the original feature map and spatial attention feature map, respectively. After obtaining the maximum and average feature response, we use a small convolutional network to compute the distribution of spatial attention as  
\[\begin{array}{l}\mathbf{F}_{avg}=\text{AvgPool}(\mathbf{F})\\ \mathbf{F}_{\text{max}}=\text{MaxPool}(\mathbf{F})\\ \mathbf{F}^{\prime}=\mathbf{F}\otimes\sigma\big{(}\mathbf{W}_{2}\text{g} \big{(}\mathbf{W}_{1}\big{[}\mathbf{F}_{avg};\mathbf{F}_{\text{max}}\big{]} \big{)}\big{)}\end{array} \tag{1}\]  
where \(\mathbf{W}_{1}\) and \(\mathbf{W}_{2}\) are the parameters of the two-layer convolutional network with \(3\times 3\) kernel size. Function g represents the ReLU (rectified linear unit) activation function, and \(\sigma\) represents the sigmoid activation function.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 3 Simulation and Experimental Results
### Performance of the Pressure Control Agent  
The conditions are as follows: the initial combustion chamber pressure is set to 1 standard atmosphere, then 1 for 1 s, 0.22 for 1 s, and 1.33 for 1 s, continuously cycling seven times. The control effect is shown in Figure 9.  
Figure 8: Control signal transmission flowchart.  
Figure 9 shows that closed-loop pressure regulation using reinforcement learning (RL) is significantly faster and more precise than with PID control. RL adapts its strategy over time as the engine's combustion chamber volume changes, while PID control uses fixed parameters and cannot quickly adjust to the time-varying system. Excluding the initial ignition phase, Table 1 shows the cumulative deviation and average deviation between the pressure command and the actual pressure.
# Leaky Wave Generation Through a Phased-Patch Array
## 1 Introduction
### Phased Array  
The properties of a phased array are well known and will not be analysed in detail here. The interested reader may refer to [4; 16].  
It is worth recalling here that the active electronically scanning array (AESA) can produce a beam that scans the space by simply modifying the phase of each element. In particular, the scanning is achieved by satisfying a condition on the phase shift to allow constructive interference between the fields produced by the radiating elements along the direction of interest.  
For active linear uniform phased arrays, this condition is  
\[\Delta\phi=kd[sin(\theta)-sin(\theta_{0})] \tag{3}\]  
where \(k\) is the wave number and \(d\) is the fixed distance between elements. The amplitude of the excitation phasor of each radiating element impacts the pattern shape. Typically, this property is used to reduce side lobes by applying proper tapering [16], i.e., reducing the amplitude of elements close to the edges. Intuitively, this happens because by reducing the currents on the edges the antenna results are electrically smaller, leading to a wider main beam and lower side lobes.  
So, to achieve the desired pattern, i.e., to impose the desired set of coefficients for the radiating elements, it is necessary to modulate the amplitude and phase of each element. A typical solution that allows for achieving this employs a Digital Variable Attenuator to control the amplitudes and Digital Phase Shifters to modify the phases. These are components with a given number of bits (that ranges typically from 6 to 8) that introduce either an attenuation or a phase shift. Introducing a phase shift between radiating elements assures the beam pointing for a specific frequency. This means that for higher or lower frequencies, the relation (3) is no longer valid, resulting in a beam that squints around the direction of interest. This topic will be further developed in the Results section of this paper, where the behaviour of the phased array is compared to that of the LWA.  
This article aims to establish an alternative and improved design of the antenna proposed in [7], which utilizes inhomogeneous waves for hyperthermia applications, as they may enable deeper tissue penetration. In this study, we focused on a numerically feasible scenario by considering an antenna operating at 12 GHz as a feasibility study. Then, we explored its application to hyperthermia by designing a prototype operating at 2.4 GHz, paving the way for more advanced antenna designs and experimental measurements.
## 4 Results
### Key Technologies of the Platform  
#### 4.4.1 Edge Computing Technology: Community Information Monitoring  
Edge computing technology plays a crucial role in community information monitoring, dynamically reflecting various data changes within the community, such as the environment, carbon emissions, and disaster monitoring. The introduction of edge computing technology optimizes the workflow of traditional IoT sensors, including the full-process management of data acquisition, transmission, processing, and analysis [44]. Edge computing technology enables real-time monitoring of community information and the acquisition of accurate data, facilitating the assessment and optimization of community low-carbon emission reduction strategies. Edge computing nodes possess certain computational capabilities, allowing local data preprocessing, filtering, and compression. They can perform real-time calculations of carbon emissions and mark abnormal data, contributing to the scientific formulation and optimization of low-carbon emission reduction strategies. Additionally, with the integration of mobile applications, community residents can view monitoring data in real-time through mobile apps and participate in community low-carbon emission reduction activities. The application of edge computing technology enhances the timeliness and accuracy of data, allowing users to conveniently view and verify relevant data, thereby improving the participation experience in low-carbon emission reduction activities.
## 4 Detailed Design and Implementation
### The Architecture of the Database  
The proposed application uses a database created in MySQL using the SQL programming language. After its organization, it contains 10 tables.  
During the design process of the internal MySQL database, normalization was aimed at avoiding redundant fields, synthetic keys, and circular references, while applying the following principles:  
* A clear structure of the tables was established, using descriptive column and table names.
* Each table contains information strictly related to a single defined object.
* Each table has a distinct primary key.
* The data types of the fields have been optimized by applying appropriate functions.
* In Figure 9, we will illustrate the database diagram before deriving the tables.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 1 Introduction
### Interpolation Scheme: The SIMP  
The SIMP is based on the relation of the stress state of every element with its relative density. The formulation is included in Equation (1):  
\[\mathrm{E_{i}}=\mathrm{E(x_{i})}=\mathrm{E_{min}}+\mathrm{x_{i}^{P}(E_{0}-E_{ min})} \tag{1}\]  
where \(\mathrm{E_{i}}\) is the element Young Modulus, \(\mathrm{E_{min}}\) is the almost zero elastic modulus of void material for avoiding mathematical incompatibility issues, \(\mathrm{E_{0}}\) is the elastic modulus of the original material, \(\mathrm{x_{i}}\) is the relative density of the element \(\mathrm{i}\), and \(\mathrm{p}\) is the penalization factor. This penalization factor is one of the parameters to use in the sensitivity analysis of this manuscript. According to Equation (1), it is evident that a higher \(\mathrm{p}\) will result in a more binary outcome [16; 17; 18], corresponding to the graph of a power rule function presented in Figure 2. The penalization raises the degree of the SIMP polynomial when it increases, resulting in a more discrete relation between the tensional state of the element and its density.  
Figure 1: Simplified flowchart of the density-based topology optimization process.  
SIMP represents a robust, highly configurable, and computationally efficient scheme. It does not require homogenization and is adaptive to various design conditions and constraints. Due to these advantages, SIMP is chosen as the initial interpolation scheme for the research conducted in this work. However, using SIMP presents challenges such as dealing with intermediate densities, mesh dependency, and potential non-convergence [19].