esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a New Bilayer Multipole Electromagnetic Brake System for a Haptic Interface
## 5 Experimental Evaluation
### Force and Torque Characteristics  
The BLMP brake was analyzed for its capability of generating a wide range of magnetic forces as a function of variable input current. For that, the experimental setup in Figure 10b was used; the spring in the armature body was removed to maintain a fixed air gap of 0.5 mm between the poles and armature. The force sensor measured the axial direction magnetic forces acting on the armature as a result of actuating the coils of the inner layer and the combination of the two layers, shown in Figure 11a,b, respectively. Because of the unavailability of analytical hysteresis model of the material, the analytical model of magnetic force was plotted with the increasing current; only, that was validated by the experiment results.  
For analyzing the braking capability as a function of variable input current, the same setup of Figure 10b was used with the inclusion of spring in the armature body. The armature body rotates freely, maintaining an initially fix air gap with the poles in the dis-engaged state. As the magnetic disc is attached with the armature through an unstretched-spring, an engaging force proportional to the spring restoring force \(T_{sp}\) is required to pull the magnetic disc all the way through air-gap. In the dis-engaged state, there is no contact between armature and friction pad; hence, no friction torque is available. During the actuation of brake, the magnetic force pulls the spring-connected magnetic disc against the friction pads, and the engaging force becomes constant due to no further displacement of the armature, as shown in the top graphs of Figure 12. In this engaged state, the resulting braking torque that is proportional to the net normal force \(F_{N}=F_{mag}-T_{sp}\) is measured by the torque sensor when user inputs rotary motion through the handle. Figure 12 shows three cases of torque measurement, before and after engaging. Firstly, an initial air gap of 0.5 mm was maintained and the coils of inner layer of brake were actuated with a 0.5 A current increments, as shown in Figure 12a. Secondly, with the same initial air gap, the coils of both inner and outer layers of brake were actuated with a 0.3 A current increment, as shown in Figure 12b. Lastly, the initial air gap was reduced to 0.35 mm and a 0.3 A current increment was provided to both inner and outer layer coils of the brake, as shown in Figure 12c. The experiment results also validate the analytical model derived in Equation (14).  
Figure 11: Magnetic pulling force on the armature as a function of coil currents with (**a**) inner layer actuation only; (**b**) combined layer actuation.
# Design and Experimental Study of Potato-Soil Separation Device for Sticky Soils Condition
## 4 Separation Device Performance Test
### Testing Operation-Efficacy of Soil-Crushing Mechanism  
#### 4.3.1 Test Method  
The soil-crushing mechanism is one of the key apparatuses for assisted crushing. To verify the effectiveness of the soil-crushing mechanism and assess the effect, a single factor test with or without a soil-crushing roller was performed. For a single-factor test, the potato-soil mixture with 23% moisture content was selected as test material, and the inclination angle of the lifting chain was set at 15\({}^{\circ}\), the linear speed of the second-stage lifting chain at 0.99 m/s, and rotational speed of the soil-crushing roller at 70 r/min. The respective trial in scenarios with or without a soil-crushing roller was repeated twice. Subsequently, small soil, soil adhered to potato tuber and damaged potato tuber, was picked out manually and weighed to calculate the percentage of small soil \(\lambda\), the percentage of soil adhering to potato tuber \(\gamma\), and damage rate of potato tuber \(K\).
## 4 Numerical Simulation
### Peak Signal-to-Noise Ratio (PSNR)  
PSNR refers to the ratio between the maximum possible power of original speech signal and the power of encrypted signal. A higher PSNR indicates that the encryption or reconstruction is of higher quality [40]. The higher the PSNR value, the better the quality of the encrypted signal. The PSNR is obtained based on the following equation:  
\[PSNR=10 log_{10}(\frac{MAX(x_{i}^{2})}{\sum(x_{i}-y_{i})^{2}}))\ \mathrm{dB} \tag{6}\]  
In Table 3, the maximum value of PSNR is opposite to the N = 3, and any increase in the levels leads to a decrease in the value of PSNR.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 3 CDR Products and Applications  
CDR products and derived CIRs have been widely used in various applications of environment and climate change research, including resource and risk management, decision support, and commercial impact assessment. Some of these CDR products and their important applications are briefly discussed in the following subsections.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Derivation of Fitness Function
#### 5.3.1 Coverage Rate  
Coverage rate is the ratio of the number of nodes in a circular area where candidate CH nodes are located to the total number of nodes. Obviously, the more nodes that are covered, the more candidate CH nodes can be obtained, so that local optimization can be avoided when selecting CH nodes. Moreover, it can avoid the situation where CH nodes are gathered in a certain corner. Therefore, we need to maximize the coverage rate.  
\[r_{Cov}=\frac{n_{Cov}}{n} \tag{13}\]  
where \(n_{Cov}\) is the number of nodes covered by the circular areas and \(n\) is the number of nodes in the network.
## 3 System Model and Problem Formulation
### Link Calculation Model  
The useful power received by the user terminal located under the beam can be calculated by the following formula:  
\[[P_{R}]=[P_{T}]+[G_{T}]+[G_{R}]-[L] \tag{1}\]  
in which \(P_{T}\) is the transmitting power of the satellite beam, \(G_{T}\) is the transmitting gain of the satellite antenna, \(G_{R}\) is the antenna receiving gain of the user terminal, \(L\) is the transmission loss, which can be calculated as follows:  
\[L=L_{fsl}+L_{rsl}+L_{aml}+L_{aa}+L_{pl} \tag{2}\]  
where \(L_{fsl}\) is the free space loss, \(L_{rsl}\) is the feeder loss of the receiver, \(L_{aml}\) is the antenna misalignment loss, which includes the antenna pointing loss and polarization loss, \(L_{aa}\) is  
Figure 2: Schematic diagram of strong coupling between illumination pattern and bandwidth allocation. (**a**) shows a situation where the lighting cells are scattered, and each cell can utilize the full bandwidth. (**b**) shows a situation where the lighting cells are concentrated, and each cell needs to isolate the bandwidth to eliminate interference.  
the fixed atmospheric loss, and \(L_{pl}\) is the ionospheric loss. The main component of \(L\) is the free space loss, and the specific calculation equation is  
\[[L_{fsl}]=20\lg(f)+20\lg(r)+32.4 \tag{3}\]  
in which the unit of frequency \(f\) is \(MHz\), and the unit of distance between transmitter and receiver \(r\) is km. The calculation of polarization loss can refer to the model in document ITU R P.618-13 [29], and the calculation of atmospheric loss and ionospheric loss can refer to document ITU R P.676-12 [30] and ITU R P.531-14 [31], respectively. The feeder loss of the receiver and antenna pointing loss are set as constant parameters, taking 0.5 dB and 2 dB, respectively, according to engineering experience.  
The noise power received by the user terminal can be calculated by Equation (4):  
\[P_{N}=kT_{S}B_{N} \tag{4}\]  
where \(k=-228.6012\) dBW/ (Hz\(\cdot\)K\({}^{-1}\)) is the Boltzmann constant, \(T_{S}\) is the equivalent system noise temperature, and \(B_{N}\) is the equivalent noise bandwidth. After determining the useful signal power and noise power at the receiver input, we can then obtain the input signal-to-noise ratio  
\[\frac{C}{N}=\frac{P_{R}}{P_{N}+I_{R}} \tag{5}\]  
It is worth noting that we regard the interference as a part of the noise. In Equation (5), \(I_{R}\) is the interference power received by the user terminal under the coverage of the current beam, which is mainly the co-channel interference from other beams that lit at the same time slot, and the specific calculation method will be given in the interference model.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation
### PMW-Based Precipitation Estimation
\[C_{1}(Tb_{IR})=\int_{Tb_{1}}^{Tb}f_{1}(Tb_{IR})dTb \tag{1}\]  
\[C_{2}(P_{PMW})=\int_{PMW_{1}}^{PMW}f_{2}(P_{PMW})dP_{PMW} \tag{2}\]  
\[C_{2}^{-1}[C_{1}(Tb_{IR})]\xrightarrow{f_{2}}Tb_{IR} \tag{3}\]  
where \(P_{PMW}\) is the precipitation rate retrieved based on PMW;\(f_{1}(Tb_{IR})\) and \(f_{2}(P_{PMW})\) are the probability density functions of IR \(Tb\) and \(P_{PMW}\), respectively; \(C_{1}(Tb_{IR})\) and \(C_{2}(P_{PMW})\) are the cumulative density functions of IR \(Tb\) and \(P_{PMW}\), respectively; and \(f_{3}\) represents the relationship between \(P_{PMW}\) and IR \(Tb\). The frequency histograms of \(P_{PMW}\) and IR \(Tb\) are generated and converted into cumulative histograms before being matched, so that the occurrence of the heaviest precipitation is associated with the \(Tb\) values linked to the heaviest rainfall. A \(Tb\)-\(P_{PMW}\) functional relationship between PMW precipitation rates and IR \(Tb\) is established for certain spatiotemporal ranges. Moreover, the rain-no-rain threshold is defined; this threshold greatly affects the final precipitation estimation. Finally, the \(Tb\) and \(P_{PMW}\) functions are applied to GEO IR \(Tb\) (below the rain-no-rain threshold), and the final precipitation rates are then obtained. The PMM has been successfully applied to derive global hourly satellite precipitation products. It has also found extensive application in various algorithms for integrating IR Tb and PMW estimates, including the algorithm proposed by [PERSON] et al. (2013), the Microwave/Infrared Rain Algorithm (MIRA) ([PERSON] and [PERSON], 2012), the MW-IR Combined Rainfall Algorithm (MICRA) ([PERSON] and [PERSON], 2012; [PERSON] and [PERSON], 2012), the PERSIANN Cloud Classification System (PERSIANN-CCS) ([PERSON] and [PERSON], 2012), the Naval Research Laboratory Blended (NRLB) algorithm ([PERSON] and [PERSON], 2012), and the CMORPH Climate Data Record (CMORPH CDR) ([PERSON] and [PERSON], 2012). Among these algorithms, there is significant variation in defining the rain-no-rain threshold. For example, [PERSON] et al. (2017) defined this threshold as the Tb value corresponding to the same cumulative frequency as that of non-rain events determined from PMW estimates. MIRA employs spatially and temporally varying optimal IR rain/no-rain thresholds derived through calibration against rain gauge observations ([PERSON] and [PERSON], 2012). Furthermore, in addition to its applications in precipitation product generation, PMM can also be utilized for downscaling purposes ([PERSON] and [PERSON], 2012).
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 2 Related Works
### Learning-Based Visual SLAM  
Recent research explores novel SLAM methods that exploit the advantages of both deep learning and multi-view geometry [25,26]. GCNv2-SLAM [10] integrates GraphConvolutional Networks (GCNs) [27] into SLAM, culminating in a comprehensive system with offline Bag-of-Words (BoW) training. It reduces absolute trajectory error (ATE) by 52% compared with ORB features. In another study [28], knowledge distillation from HFNet [29] enhances feature detection within a compact model. Additionally, ref. [30] enhances SuperPoint with edge and multi-scale information. In LIFT-SLAM [11], a LIFT [31] extractor serves as the SLAM front-end, implementing an adaptive feature matching strategy tailored to different datasets. LIFT-SLAM reduces absolute trajectory error by 45% compared with ORB-SLAM on KITTI sequence 03. In contrast, DK-SLAM adopts a meta-learning-based training strategy, obviating the need for separate hyperparameter designs for distinct datasets. Ref. [32] proposes a robust deep-learning visual SLAM system for weakly textured underwater environments, which integrates a feature generator UWNet into ORB-SLAM3. It achieves superior performance with an average reduction of 44.45% in absolute pose error. Moreover, ref. [33] proposes an online adaptive keypoint extraction framework for visual odometry, which adaptively adjusts keypoint selection policies based on real-time feedback. To address the challenges posed by dynamic objects in visual SLAM, ref. [34] introduces a semantic segmentation network that identifies dynamic objects in the scene. It achieving a 39.5% improvement in positioning accuracy over ORB-SLAM2 in sequences with multiple dynamic objects on the KITTI. The system then removes feature points associated with these objects during the front-end processing, leading to state-of-the-art performance in environments with multiple moving objects. Additionally, ref. [35] exploits geometric information from objects to enhance VSLAM performance, and ref. [36] applies learning-based semantic segmentation to correct a scale in monocular SLAM mapping and localization. Furthermore, ref. [37] incorporates learned depth labels into monocular visual SLAM, transforming it into a virtual stereo SLAM to overcome scale ambiguity. These depth labels are generated from unsupervised depth estimation networks, enabling the system to achieve more accurate mapping and localization.
## 4 Spatiotemporal Feature Extraction Techniques
### Feature Extraction Methods Future Research Directions
#### 4.5.7 Spatio-Temporal Graph Neural Networks (ST-GNNs)  
Future research could explore applying graph neural networks (GNNs) to spatio-temporal feature extraction better to capture complex spatial and temporal dependencies [85]. Traditional Convolutional Neural Networks (CNNs) struggle to handle the irregular relationships in graph-structured data. In contrast, GNNs can effectively capture the relationships and dependencies in irregular spatial layouts by aggregating and propagating node features over a graph structure. Extending GNNs to the temporal dimension to construct Spatio-Temporal Graph Neural Networks (ST-GNNs) allows for simultaneous consideration of spatial and temporal interactions in dynamically changing data [86,87].
# Numerical Study of Wave Effect on Aircraft Water-Landing Performance
## 4 Results and Discussions
### Calm Water vs. Wave Water
#### 4.1.3 Pitch-Up Stage  
After the front-fuselage on-flight stage, the vertical velocity of the aircraft of wave-landing case is slightly larger than the initial vertical velocity as depicted in Figure 8b. Comparing with the fuselage impacting stage of the calm-water-landing case, the hydrodynamic load of second impacting and pitch-up stage of the wave-landing case is significantly larger as plotted in Figure 8a,c, which is caused by the larger impacting velocity and wave shape. A more comprehensive insight into the impacting load is obtained by comparing the impacting position and pressure distribution between Figures 11a and 13. At \(t=0.156\) s, the moment of maximum load for calm-water-landing case, the pressure peak is located in the step region and the corresponding positive pressure area is small. In contrast, at \(t=0.54\) s, the time moment of maximum load in wave-landing case, the impacting load is mostly generated by the front-fuselage, as well as the entire second impacting stage. As a consequence, the pitch angle shown in Figure 8d is rising up. We also note that there are two acceleration peaks in Figure 8a,c at \(t=0.54\) s and \(t=0.74\) s, respectively. The mechanism behind these two peaks is probably contributed to the negative pressure on the front-fuselage. From \(t=0.45\)-\(0.54\) s, the front-fuselage heavily slams on the wave surface due to the large pitch angular acceleration generated by the rear-fuselage, and the impacting area gradually increases so that the accelerations quickly reach to the maximum. After that, during the time period of \(t=0.54\)-\(0.63\) s, the negative pressure takes place in the front-fuselage region and then disappears at the moment of the second peak. As indicated by the air-water phase contour at \(t=0.74\) s and \(t=0.85\) s in Figure 13, the area of the fuselage contacting with water shrinks when the aircraft moving forward and pitching up. Such a feature is relevant in the second decreasing of acceleration time-history, as well as the reduction in the growth rate of pitch angle.  
Figure 12: Time-snapshots of flowfield of aircraft landing in wave field at crest position: (**a**) air-water phase contour during the rear-fuselage impacting stage; (**b**) air-water phase contour and pressure distribution during the front-fuselage on-flight stage.
# Review of Functionally Graded Thermal Sprayed Coatings
## 1 Introduction  
Thermally sprayed coatings can be obtained using a number of methods enabling different type of materials such as metals and their alloys and ceramics (mainly oxides and carbides) to be deposited. Also the composites of metal and alloys with ceramics are also very frequently sprayed [1]. Another group of materials, namely the polymer is also possible to spray and the deposits of thermoplastic polymers as polymethylmetacrylate and fluoropolymers are described in the literature [2, 3].  
The coatings can be applied to protect the substrates of all type of materials. Metals and alloys are used as substrates very frequently. However, ceramics or polymer substrates are also used. That is why there are many new industrial applications of coatings. The entire thermal spraying (TS) market was estimated in the beginning of XXI century to be 2 billion \(\epsilon\)[1] and it grew up in 2018 up to 7.4 billion US$ (i.e., about 6.6 billion \(\epsilon\)) and the projection for 2026 is 13.5 billion US$ (12 billion \(\epsilon\)) [4].  
The main function of coatings is to protect the substrates of all types of harmful agents. The latter may act destructively starting from coatings surface in the following ways, such as e.g.,:  
* Mechanical, by action of abrasion, erosion, or friction of external bodies;
* Physical, and in particular, thermophysical, by submitting the substrate to high temperature and to intensive heat flux;
* Chemical or biochemical, by reaction of external compounds with substrate.  
The protection of substrate is realized by the coatings that belong, generally, to another group of materials than substrate. For example, ceramics protect metals and alloys against wet corrosion or high temperature. Knowing that many properties of such coating are very different from that of substrate, their direct application may lead to generation of mechanical or thermal stresses at their interface. A reasonable way leading to reduce them is the design of multi-coatings with the gradient of composition. The design of multi-coatings requires that the coating being in contact with substrate should be reasonably similar to substrate properties. Inversely, the one being in contact with outer environment has to have the properties enabling to fulfill the protective function of the deposit. This is a fundamental concept of functionally graded coatings presented in this paper.  
An important issue in the design of functionally graded coatings is the prediction in which properties are essential to fulfill the functions in service. That is why the properties of the coatings are shortly described. The properties depend strongly on the coatings' microstructure resulting from the method of thermal spraying, and in particular, on the feedstock used to spray. Some of the methods as well as solid (powders) and liquid feedstock (solution and suspension) are shortly described.  
Finally, some practical applications of functionally graded coatings are shown and the perspective of their future development are briefly depicted.
## 2 Problem Formulation
The geometrical structure of the problem is given in Figure 1.  
The problem considered here is based on the following suitable similarity transformations [13; 17; 25]. \[ \begin{array}{c}\eta=\sqrt{\frac{U_{0}}{2 vt}}e^{(x/2l)}y,\,u=U_{0}e^{(x/l)} f^{\prime}(\eta),\\ v=-\sqrt{\frac{vtU_{0}}{2l}}e^{(x/2l)}[\eta f^{\prime}+f],\,\theta(\eta)= \frac{T-T_{\infty}}{T_{w}-T_{0}}.\end{array}  \tag{9}\]  
Equation (1) is equivalently satisfied and Equations (2)-(8) are transformed into the following form  
\[\frac{1}{\left(1-\phi\right)^{2}\left(1-\phi+\phi\big{(}\rho_{s}/\rho_{f} \big{)}\right)}f^{\prime\prime}\,+ff^{\prime\prime}\,-2\left(f^{\prime}\right) ^{2}-\frac{2P_{m}}{\left(1-\phi+\phi\big{(}\rho_{s}/\rho_{f}\big{)}\right)}f^{ \prime}=0, \tag{10}\]  
Figure 1: Physical diagram of the present flow model. \[\frac{1}{\Pr}\frac{k_{n_{f}}/k_{f}}{\left(1-\phi+\phi\big{(}\rho c_{p}\big{)}_{s} \big{/}\big{(}\rho c_{p}\big{)}_{f}\right)}\theta^{\prime\prime}+\theta^{\prime }f-\frac{1}{2}\lambda_{t}\Big{(}f^{2}\theta^{\prime\prime}-ff^{\prime}\theta^{ \prime}\Big{)}=0, \tag{11}\]  
\[ \begin{array}{c}f^{\prime}(\eta)=1,f(\eta)=0,\theta(\eta)=1-S\text{ at }\eta=0,\\ f^{\prime}(\eta)\to 0,\ \theta(\eta)\to 0\text{ as }\eta\rightarrow\infty.\end{array}  \tag{12}\]  
In the above equations, the embedded parameters thermal stratification (TS) is denoted by \(S\), the porous medium parameter (PMP) is designated by \(P_{m}\), the Prandtl number (PN) is symbolized by Pr and the non-dimensional thermal relaxation time (TRT) is represented by \(\lambda_{t}\). These physical parameters are defined as  
\[P_{m}=\frac{\
u\varepsilon}{U_{0}K},\ \Pr=\
u/\alpha,\ \ \lambda_{t}=\lambda_{2}U_{0},\ \ S=b/a. \tag{13}\]  
Note that for \(\lambda_{t}=0\) leads to the classical [PERSON]'s heat conduction law. **3. Variables of Engineering Interest**  
The physical quantity such as the shear stress rate (local coefficient of skin friction) is given by  
\[C_{f}=\frac{\tau_{w}}{\rho_{f}U_{\infty}^{2}},\tau_{w}=\mu_{n}\bigg{(}\frac{ \partial u}{\partial y}\bigg{)}_{y=0}. \tag{14}\]  
Consuming the similarity transformation (9) the above equation becomes  
\[\sqrt{\text{Re}_{x}}C_{f}=\frac{1}{\left(1-\phi\right)^{2.5}}f^{\prime\prime} \left(0\right). \tag{15}\]  
By making use of Equation (15), extensive data results are outputted in Table 1 for reduced skin friction coefficient \(\sqrt{\text{Re}_{x}}C_{f}\). However the values specified to the parameters \(\phi\), \(P_{m}\) and \(\lambda_{t}\), it is found that the nanoparticles TiO\({}_{2}\) exhibit higher frictional effect that the other nanoparticles.
## 4 Applications
### Broadband Access and Network Coverage  
In remote and extreme environments, such as mountainous regions, deep valleys, or isolated islands, conventional communication infrastructure often struggles to provide adequate coverage [151]. This results in weak network signals and low data transmission efficiency, which significantly restricts both production operations and everyday communications. In scenarios involving field activities like mining, forestry monitoring, or scientific research in these areas, drones can serve as temporary airborne base stations or communication relay nodes, supported by SAGINs. This arrangement enables broadband access and network coverage, effectively filling the coverage gaps of ground networks. Compared to the high costs and long construction timelines associated with setting up terrestrial base stations, drones offer a more flexible deployment option, swiftly covering designated areas and ensuring stable communication services, thereby greatly enhancing operational efficiency in fieldwork settings. The literature [152] discusses extending cell tower coverage using UAVs, with encouraging results.