esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Matching User Preferences to Network Response  
Selecting the best communication solution given the application preferences is important for delivering the most efficient network for the application. This involves matching the preferences to the most suitable solution given the derived expected results from the model. Even with effective modelling, matching the response to application preferences is not a trivial task. The quality of a communications solution is defined over a number of unlike variables and creates difficulty when comparing these unlike variables for decision making. The solution quality is measured with metrics such as reliability, latency, efficiency and stability with each metric calculated with different units. In order to compare the communication solutions each solution must be given a single score which represents all the evaluation metrics. Creating this composite score requires some of the tenets of Multi Criteria Decision Analysis (MCDA) [36]. First a structure is put in place by which the evaluation metrics can be standardised to like variables for combination. These variables can then be scaled before adding a weight based on the application preference. When the variables are combined a clear winner can then be selected. Section 6.1 describes this process while Section 6.2 presents an example using the models created in Section 5.1.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 3 Modeling and Parameter Identification
### Model Uncertainty  
Although the error between model output and experiment data is little in Figure 7, it should be noted that the error could be obvious at different command velocities. As shown in Figure 8, when the command velocity is changed to 3.5 deg/s, the error between the output of the model identified in Section 3.3 and the experiment data is obvious. This means the problem of model uncertainty cannot be ignored, which is the main reason for applying \(\mathcal{H}_{\infty}\) loop shaping method, a kind of robust control, for the velocity controller design. In addition, note that a nonlinear model can represent the real plant with high accuracy, but our design techniques cannot effectively deal with it [27]. The model in Section 3.3 is used as the nominal model in robust controller design.  
Figure 8: Model uncertainty in different command velocity.  
Figure 7: Experimental data and model output.
## 4 Analysis of Numerical Results
### Graphite Resonator  
This subsection analyzes the numerical results when the patch antenna is combined with a graphite-made resonator when the chosen geometric shapes are considered. To support the analysis, \(|S_{11}|\) for the cap-based, rectangular prism-based, cone-based, half-cylinder-based, and trapezoidal prism-based geometries are shown in Figure 12a, Figure 12b, Figure 12c, Figure 13a, and Figure 13b, respectively. Furthermore, the radiation patterns for the cap-based, rectangular prism-based, cone-based, half-cylinder-based, and trapezoidal prism-based geometries are illustrated in Figure 14a, Figure 14b, Figure 14c, Figure 15a, and Figure 15b, respectively. Moreover, Table 6 summarizes key parameter values obtained at the resonant frequency. For the cap-based shape geometries, the plots of \(|S_{11}|\) in Figure 13a show that both the concave hemisphere and hollow concave hemisphere exhibit similar performance, which is characterized by a slight downshift of the resonant frequency. In contrast, the cylindrical hemisphere shows a right resonant frequency shift. However, the hemisphere presents a significant reduction in the resonant frequency value and obtains an unacceptable \(|S_{11}|\). Regarding BW, most of the evaluated geometries exhibit higher values than the patch antenna without a resonator, as summarized in Table 6. However, the cylindrical hemisphere stands out as the most prominent, achieving a BW value exceeding the patch antenna without a resonator. Considering the radiation pattern plots in Figure 14a and detailed in Table 6, we see that the concave and hollow concave hemispheres attain similar performance. Additionally, these two geometries achieve MLM values significantly higher compared to the patch antenna without a resonator, suggesting their potential for improving radiation efficiency. Regarding the HPBW, the concave and hollow concave hemispheres exhibit a reduction compared to the patch antenna without a resonator, signifying enhanced directionality. In contrast, the cylindrical hemisphere and hemisphere yield higher HPBW. Regarding SLL, only the concave and hollow concave hemispheres offer a reduction compared to the patch antenna without a resonator. Figure 12: Patch antenna with a graphite-made resonator: magnitude of the scattering parameter (\(|S_{11}|\)). (**a**) cap-based; (**b**) rectangular prism-based; and (**c**) cone-based. Figure 13: Patch antenna with a graphite-made resonator: magnitude of the scattering parameters (\(|S_{11}|\)). (**a**) half-cylinder-based; and (**b**) trapezoidal prism-based. Figure 14: Patch antenna with a graphite-made resonator: the radiation pattern plot of the 1D far-field realized gain (\(\phi=0\)). (**a**) cap-based; (**b**) rectangular prism-based; and (**c**) cone-based. Analysis of the rectangular prism-based geometries reveals similar performances in terms of \(|S_{11}|\), as illustrated in Figure 13b. The rectangular and hollow rectangular prisms show a slight shift in resonant frequency, with the hollow rectangular prism displaying
## 4 Construction and Operation of Haptic Equipment
### Design of the Haptic Equipment
#### 4.1.2 Equipment Design and Construction  
The force-feedback in the haptic device is induced by the torques generated by two gear motors on the shafts of the revolute joints G and E attached to links one and four. The whole assembly is mounted on a support at an ergonomically convenient height.  
The equipment for haptic simulation, as well as its operation, is presented in the figure below (Figure 12).  
Figure 11: Angular correspondence of simultaneous operation of drive motors.  
Figure 10: Theoretical trajectory of saw handle.  
The operation of the equipment is carried out in the following sequence (see Figure 12): (i) the user pulls the handle (1); (ii) the rotation sensors-encoders (2 and 4) located on the motors' shafts (3 and 5) detect the time change in the angular position of the mechanism links and transmit the data to the calculation unit (6); (iii) the software application takes the value of the angular variation in the position of the links and introduces them into the calculation algorithm corresponding to the simulated articulated mechanism; (iv) depending on the current position, the value of the reaction force in this position is calculated; (v) depending on the dimensions of the components of the mechanism, the torque (Motor I) and position (Motor II) values are calculated and transmitted to the electric motor control to generate a force and position return; (vi) the user feels, at the handle, the force response of the system, watching, at the same time, the operation of the virtual mechanism on the subsystem of visualization, display, or virtual reality headset.  
The equipment is shown in Figure 13. The two gear motors are identical. The HD-1235 MG is a giant-scale (also called 1/4-scale or mega-scale) digital servo from Power HD that can deliver a stall torque up to 35-40 kg\(\cdot\)cm (@ 7.4 V or 6 V), at a speed of 0.18-0.20 s/60\({}^{\circ}\). The entire gear train is metal, and the output shaft is supported by two ball bearings for improved performance. This servo works with standard RC servo pulses at frequencies up to 333 Hz [35].  
Because the gearmotors do not have a control of the rotation angle, encoders (rotation sensors of type OMRON E6B2-CWZ6C) with a measuring accuracy of 2500 pulses/rotation were installed.  
This assembly meets the specific conditions imposed by the ability of a standard user (operator) to detect movement (power, speed, angular position, torque) and has the possibility of controlling these values through the input data (dimensions of mechanisms, values of forces).  
Figure 12: The structure of the haptic mechanism and the mode of operation.  
Figure 13: The construction and structure of the haptic mechanism.  
According to the specifications of the gear motors, the haptic device was designed with the linear stroke presented in Figure 10, and it delivers maximum values of the haptic force in the range 30-50 N, values perfectly distinguishable by an operator.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Energy Consumption for DRFTSA Using Regular Dataset by Using Fixed Number of VMs  
In this subsection, we precisely present the energy consumption generated by the proposed DRFTSA by using the regular dataset and using a fixed number of VMs for all tasks. The main reason to calculate the energy consumption in this research is that, from the point of view of the cloud provider, it is an important parameter as, when more tasks are executed with less energy consumption, the cloud provider will have more profit in terms of money and can provide high-quality services to its customers for a lower price. Therefore, this parameter has more impact on both the cloud provider and user but, in order to minimize consumption of energy, a proper scheduling mechanism with dynamic decision-making capacity to map tasks to appropriate VMs is needed. Therefore, in this research our proposed DRFTSA carefully considered priorities of all task sequences and priorities of VMs and then scheduled tasks appropriately to VMs in respective datacenters. To evaluate the efficacy of DRFTSA in terms of energy consumption, we used a real-time dataset which consists of heterogeneous tasks and then used a regular dataset to be given as input to our algorithm to calculate energy consumption by using fixed number of VMs and various benchmark approaches to evaluate our proposed DRFTSA. The table below shows energy consumption of DRFTSA.  
Table 8 presents generated energy consumption for various benchmark approaches and DRFTSA. Generated energy consumption for PSO with 100, 200, 300, 400, 500, 600 tasks is 77.68, 82.45, 91.38, 103.67, 125.72, 134.12, respectively. Generated energy consumption for GA with 100, 200, 300, 400, 500, 600 tasks is 81.79, 85.35, 94.17, 113.57, 130.93, 142.47, respectively. Generated energy consumption for ACO with 100, 200, 300, 400, 500, 600 tasks is 91.85, 89.32, 97.65, 120.32, 134.16, 152.82, respectively. Generated energy consumption for DRFTSA with 100, 200, 300, 400, 500, 600 tasks is 53.37, 60.53, 79.27, 98.88, 118.53, 124.32, respectively. From Table 8 and Figure 8, it is evident that energy consumption of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.
# Add new tracks that are only present in the new frames of GraphB
## 3 Results and Discussion
This method, with linear time complexity, was shown to effectively handle low overlap scenarios using multi-view images and could seamlessly integrate with existing SfM and MVS pipelines. The process involved the separate reconstruction of aerial and ground images, co-registration using weak georeferencing data, rendering of aerial models to ground views, and feature matching with outlier removal. Finally, oriented 3D patches facilitated correspondence propagation to aerial views. The experimental results demonstrated superior performance, achieving successful matching in all ten challenging pairscompared to only three for the second-best method, while also enabling a more complete and stable reconstruction. The results of this study were compared with previous works based on depth maps, SfM, and MVS. This current research focuses on enhancing the accuracy of 3D reconstruction for cultural heritage preservation by refining depth estimation using SIFT features. This emphasis on improving depth estimation through feature matching distinguishes the present work from others. Depth map methods focus on a dense SLAM system for real-time reconstruction without a pose graph, using frame-to-model tracking, non-rigid surface deformation [44], and the robust reconstruction of indoor scenes from RGB-D videos using geometric registration and global optimization [45]. SfM methods [46; 47] propose single-shot 3D reconstruction using a grid pattern-based structured-light vision system, together with an efficient covisibility-based incremental SfM method for both unordered and sequential images. MVS methods [48; 49] involve viewpoint and trajectory optimization for aerial multi-view stereo 3D reconstruction, together with leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction. In contrast to these previous works, the proposed approach specifically targets cultural heritage preservation and utilizes a low-cost image acquisition setup, potentially making it more accessible for broader applications. However, its reliance on SIFT features may limit its robustness in certain scenarios, such as those involving repetitive patterns or low-texture regions. In such cases, incorporating complementary techniques from the cited works, such as dense frame-to-model tracking or leveraging photogrammetric mesh models, could enhance the robustness of the proposed approach.
## 2 Materials and Methods
### Monitoring of Glacier Termini Movement Speed from Sentinel-1  
The C-band Sentinel-1 satellite radar data released by the European Space Agency (ESA) have high quality, with a revisit period of 12 days and multiple imaging modes. Among them, Interferometric Wide (IW) is the main acquisition mode on land, with a resolution of 5 m \(\times\) 20 m. It is selected from the website of the Alaska Satellite Facility (ASF) of the National Aeronautics and Space Administration (NASA), covering the measurement area and time, including SAR images of two ascending orbits and two descending orbitsIn addition to providing rich data sources, the website has also developed HyP3 automation services for SAR image processing [45], which can be accessed directly through the website and includes three products: Radiometric Terrain Correction (RTC), Interferometric Synthetic Aperture Radar (InSAR), and Autonomous Repeat Image Feature Tracking (AutoRIFT). The AutoRIFT feature tracking algorithm can directly generate velocity maps from observed motion [46]. The coherence coefficient can be derived from the ancillary results of InSAR for further analysis.
## 6 Results and Discussion  
### Simulation Setup  
In this section, numerical results are provided to evaluate the performance of proposed spatial-temporal spectral hole-sensing model and UoS scheme for FD-SU TXs with different temporal states and spatial positions. In our simulation setup, the transmission range of the PU node and sensing range of the FD-SU TXs were varied to allow different values of probabilities and sensing parameters. To obtain the numerical results, the key parameters were chosen as follows: \(kPS=1000\) bits for \(k=1\) and BER = 0.00025. The power of the primary and secondary transmissions was set to be 20 dB and 10 dB, respectively. The variance of the sensing channel was set to \(\sigma_{h_{1}}^{2}=0.001\), and hence, the instantaneous sensing SNR was \(-10\) dB. The values of the SIS factor (\(X\)) were considered to be in the range 0.001-0.3. To provide the desired protection level to the PU, the probabilities of finding the non-availability of spatial-temporal spectral holes during HD and FD sensing slots (\(P_{F\_{nS}T}^{0}\), and \(P_{F\_{nS}T}^{1}\)) were considered to be in the range 0.7-0.9. Unless otherwise stated, a fair model of PU the status, i.e., \(P_{ON}=P_{OFF}=0.5\) was considered. The values of these parameters were set accordingly to validate the channels characteristics and network behavior.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results  
### Spatial and Temporal Changes in Land Use  
Calculation of the integrated LU dynamics for the four time periods from 1985 to 2020 revealed that it was 0.04% from 1985 to 1995, 2.21% from 1995 to 2005, 18.51% from 2005 to 2015, and 34.62% from 2015 to 2020. LUCs in the region were faster after 2005, which may be related to rapid socioeconomic development and accelerated urban transformation. Figure 3 shows the Sankey diagram produced by the LU transfer matrix of the study area from 1985 to 2020, indicating the transfer in and out of each LU type in different periods. From the figure: (1) From 2005 to 2015, transfers in and out between LU types were more frequent. Farmland was largely reduced and 82% of the transferred out area was transferred into construction land. The water area decreased by 12.06 km\({}^{2}\) and the largest area was transferred to farmland and construction land. Overall, the area of construction land increased by 51.45 km\({}^{2}\) in this period, of which 67.45 km\({}^{2}\) of farmland was transferred to construction land, 4.19 times more than the previous time. (2) From 2015 to 2020, there are different degrees of area transfer between each LU type; the change is 164.29 km\({}^{2}\) and the farmland continues to decrease by 27.35 km\({}^{2}\). Additionally, the water area increased by 15.10 km\({}^{2}\), mainly from farmland and construction land. The construction land increased by 5.88 km\({}^{2}\) and the growth was mainly from grassland, which was transferred into 6.06 km\({}^{2}\). In a comprehensive view of the four time periods, the area of farmland has been decreasing and the ratio of the area transferred into construction land to the total area transferred out from farmland was 86%, 83%, 82%, and 60%. The areas of other LU types transferred into water in the four time periods were 0.30 km\({}^{2}\), 2.51 km\({}^{2}\), 6.66 km\({}^{2}\), and 21.50 km\({}^{2}\), in order.
# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 2 Problems of the Existing Cloud Computing Architecture
### Cloud Computing Resource Management Problem  
Multimedia services require not only many computationally intensive tasks (i.e., encoding, decoding, and transcoding, etc.) but a large storage capacity. Moreover, computing-related services that require robust memory and computing performance (i.e., machine learning, image analysis, etc.) lead to high energy consumption. Because of their compact size, mobile and IoT devices have limited energy capacity; therefore, computing power and high energy consumption are key factors. There is a possibility for unnecessary energy consumption, resulting from wasted computing resources, since the provision of services suited to the characteristics of the cloud and MEC server and the efficient server arrangement structure are not considered [16; 17]. So, excessive energy consumption and inefficient processing overhead happen when a large number of user requests is received, which leads to resource waste, because existing schemes adopt a fixed resource allocation policy for user requests.
# Comparative Study to Evaluate Mixing Efficiency of Very Fine Particles
## 2 Numerical Scheme
### Cgm  
CGM enhances the efficiency of DEM simulations by reducing the particle count and lengthening the timesteps through particle-size scaling. CGM aggregates multiple real particles into larger \"parcel particles,\" thus reducing computational requirements significantly without sacrificing simulation accuracy. The fundamental idea of CGM is to scale up the particle size while preserving the bulk properties. The CGM factor \(f_{CGM}\) is defined as the ratio of the parcel particle size to the real particle size:  
\[f_{CGM}=L_{parcel}/L_{real}, \tag{10}\]  
where \(L_{real}\) and \(L_{parcel}\) are the diameters of the original particle and the scaled-up particle, respectively. To maintain dynamic similarity, the mass, stiffness, and interaction forces must be adjusted based on the following governing equations:  
\[m_{parcel}=f_{CGM}{}^{3}/m_{real}\,, \tag{11}\]  
\[k_{parcel}=f_{CGM}/k_{real}\,, \tag{12}\]  
\[F_{contact,parcel}=f_{CGM}{}^{2}/F_{contact,\ real}\,, \tag{13}\]where \(m\) is the mass, \(k\) is the stiffness, and \(F_{contact}\) is the contact force. Figure 4 illustrates the concept of CGM scaling.  
CGM is especially beneficial for large-scale DEM simulations where direct analysis of real-scale particles is computationally infeasible. Nevertheless, rigorous comparative analysis is necessary to ensure that the deviations introduced by scaling are minimal. We systematically evaluated the applicability and accuracy of CGM across various industrial mixing processes, aiming to identify effective scaling strategies for reliable and computationally efficient simulations.
## 3 Proposed Optimization Method
### Overview of the FLA
#### 3.1.4 EO Operator (Transition Stage from Exploration to Exploitation)  
In this step, the exploration phase is transferred to exploitation. The position of molecules is updated considering the location of particle p in the group (\(X^{t}_{p,g}\)) and the equilibrium position in group g (\(X^{t}_{EO,g}\)), based on Equation (54) [29].  
\[X_{p,g}^{t+1}=X_{EO,p}^{t}+Q_{EO,g}^{t}\times X_{p,g}^{t}+Q_{EO,g}^{t}\times( MS_{p,EO}^{t}\times X_{EO,g}^{t}-X_{p,g}^{t}) \tag{54}\]  
The relative quantity of the region in group g (\(Q^{t}_{EO,g}\)) and the diffusion rate coefficient in group (\(DRF^{t}_{EO,g}\)), as in Equations (55) and (60) [29], are defined as follows:  
\[Q_{EO,g}^{t}=R_{1}^{t}\times DF_{g}^{t}\times DRF_{EO,g}^{t} \tag{55}\]  
\[DRF_{EO,g}^{t}=\exp{(-\frac{J_{p,EO}^{t}}{TF^{t}})} \tag{56}\]  
\[J_{p,EO}^{t}=-D\frac{dc_{g,EO}^{t}}{dx_{p,EO}^{t}} \tag{57}\]  
\(dc_{g,EO}^{t}\) and \(dx_{p,EO}^{t}\) are determined as follows [29]:  
\[dc_{g,EO}^{t}=x_{g,EO}^{t}-X_{m,g}^{t} \tag{58}\]  
\[dx_{p,EO}^{t}=\sqrt{\left(x_{g,EO}^{t}\right)^{2}-\left(x_{p,g}^{t}\right)^{2}+ eps} \tag{59}\]  
\[DF_{g}^{t}=\pm 1\;\;\mbox{{direction}}\;\;\;\mbox{{factor}} \tag{60}\]  
\[MS_{p,EO}^{t}=\exp(-\frac{FS_{g,EO}^{t}}{FS_{p,g}^{t}+eps})\;\;\mbox{{motion}} \;\;\;\mbox{{step}} \tag{61}\]  
\[R_{1}^{t}=rand[0,1]_{d}\;\;\;\;d=1\;:\;\;\mbox{{dimension}} \tag{62}\]
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 2 Materials and Methods
### Irradiation Tests  
Two types of high-flux radiation equipment have been used in this work: (i) a laboratory-scale high-flux 7 \(\mathrm{K}\mathrm{W}\mathrm{e}\)-solar simulator in which the radiation is provided by a xenon arc lamp; and (ii) highly concentrated natural sunlight provided at the top of a solar tower by a heliostat field.
## 4 Variables and Data Analysis
### GPS Match Variables  
GPS data (WIMU PRO(tm), Real Track Systems(r), Almeria, Spain) was collected for all players from all matches in which they participated before (PRE: PRE 1 and PRE 2) and after (RTP: RTP 1 and RTP 2) injury. This pre-injury data would serve as a control group for this cohort, as it would permit the comparison with the aim of evaluating the effects of the intervention, on the performance of the injured player. This could only be registered on 10 players, since three players were injured in the preseason, and there were no data from official competitions prior to the injury.  
The variables used to compare the performance represented actions where the quadriceps are known to be the most active: in accelerations, high intensity runs, and sub-maximal and maximal sprints [53]. The variables measured were (a) total distance covered in m (as a reference value); (b) distance covered at high intensities (between 18.1-21.0 Km-h\({}^{-1}\)) in m; (c) distance covered at very high intensities (21.1-24.0 Km-h\({}^{-1}\)) in m; (d) distance covered at sprint velocities (above 24.0 Km-h\({}^{-1}\)) in m; (e) the peak speed registered in Km-h\({}^{-1}\); (f) the peak acceleration registered in m-s\({}^{-2}\); and (g) the explosive distance (i.e., the distance covered when the acceleration exceeded 1.2 m-s\({}^{-2}\)) in m-min\({}^{-1}\).