esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Approach  
### Governing Equations  
Three-dimensional compressible Navier-Stokes equations with the source term added were solved in the present study. The equations are non-dimensionalized by the free-stream density, free-stream velocity, and chord length of the airfoil and are represented as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u_{j}}{ \partial x_{j}} = 0, \tag{2}\] \[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\big{(}\rho u _{i}u_{j}+p\phi_{ij}\big{)}}{\partial x_{j}} = \frac{1}{Re}\frac{\partial\tau_{ij}}{\partial x_{j}}+D_{\rm c}S_{ i},\] (3) \[\frac{\partial\epsilon}{\partial t}+\frac{\partial\big{(}(e+p)u _{j}\big{)}}{\partial x_{j}} = \frac{1}{Re}\frac{\partial u_{k}\tau_{jk}}{\partial x_{j}}-\frac{ 1}{(\gamma-1)PrReM_{\infty}^{2}}\frac{\partial q_{j}}{\partial x_{j}}+D_{\rm c }S_{j}u_{j}, \tag{4}\]  
where \(\rho\), \(u_{i}\), \(p\), \(e\), \(q_{i}\), \(x_{i}\), \(t\), and \(\tau_{ij}\) denote the non-dimensional forms of the density, velocity vector, pressure, energy per unit volume, heat flux vector, position vector, time, and stress tensor, respectively. \(\delta_{ij}\) is the Kronecker delta. Equations (2)-(4) follow [PERSON] notation. The subscript \(i\) is a free index, and \(j\) and \(k\) are dummy indices. The indices take the value 1, 2, or 3. \(Re\), \(M_{\infty}\), and \(Pr\) denote the Reynolds number, free-stream Mach number, and Prandtl number, respectively. They are defined as follows:  
\[Re=\frac{\rho_{\infty}U_{\infty}c}{\mu_{\infty}},\ M_{\infty}=\frac{U_{\infty }}{a_{\infty}},\ Pr=\frac{\mu_{\infty}c_{\rm P}}{\kappa_{\infty}}, \tag{5}\]  
where \(\rho_{\infty}\), \(U_{\infty}\), \(c\), \(t_{\infty}\), \(a_{\infty}\), \(c_{\rm P}\), and \(\kappa_{\infty}\) denote the density, velocity, chord length, viscosity, sound speed, constant pressure specific heat, and heat conduction coefficient, respectively. Here, a quantity with subscript \(\infty\) denotes the quantity in the free-stream condition. The viscosity is calculated using [PERSON]'s law. \(D_{\rm c}S_{i}\) and \(D_{\rm c}S_{j}u_{j}\) in Equations (3) and (4) correspond to the body force and power added to the unit volume by PA, respectively. Hereinafter, \(x\), \(y\), \(z\), \(u\), \(v\), and \(w\) are used to represent the position and flow velocity of \(x_{1}\), \(x_{2}\), \(x_{3}\), \(u_{1}\), \(u_{2}\), and \(u_{3}\), respectively, for ease of discussion. In the present study, the free-stream  
Figure 2: Alternating current (AC) voltage waveform for the burst actuation.  
Mach number is set to 0.2, which reduces the computational time. The compressibility of the fluid is almost negligible although the Mach numbers in the present study and previous experimental studies [29,30] are different.
## 2 Heat-to-Power Applications
### Supercritical CO\({}_{2}\) Brayton Cycles
#### 2.4.1 Various Layouts  
Since the 1960s a number of test facilities have been constructed in the world, with power output ranging from kW up to MW scale with the aim of investigating heat transfer and turbomachinery related to supercritical CO\({}_{2}\) operation but research in this area has been frozen for decades [68]. However, since 2000s there has been a steady increase in experimental research regarding components developments, fluid behavior and systems integration. In the US, the Department of Energy (DOE) has been supporting efforts to develop highly efficient power conversion systems, and now more than a dozen of companies (Echogen, GE, Net power, etc.) and research centers (Sandia National Lab, Oak Ridge national Lab, National energy technology lab, etc.) are involved in research around supercritical CO\({}_{2}\). In Europe, the European commission under the framework of H2020 program recently funded two major projects related to the development of supercritical Brayton: the sCO\({}_{2}\)-HeRo and the sCO\({}_{2}\)-Flex. The goal of the sCO\({}_{2}\)-HeRo was to develop a small-scale Brayton cycle to be incorporated in a PWR glass model [69] and the sCO\({}_{2}\)-flex project aims at developing a scalable, modular and flexible 25 MWe coal power plant utilizing a supercritical CO\({}_{2}\)-based Brayton cycle. [PERSON] et al. [54]. In a survey reported experimental studies being carried out in South Korea. Several research centres are involved, and include the KAIST (Korea Advanced Institute of Science and Technology), the KAERI (KoreanAtomic Energy Research Institute) and the Korea Institute of Energy Research (KIER). Many other research centres based in China, Japan, India, Canada, UK and Australia are reported to be involved in the development of Brayton cycles.  
Under the Sunshot initiative [70; 71], the South West Research Institute (SwRI) along with its partners General Electric (GE), Thar Energy LLC (Thar), Bechtel Marine Propulsion Corporation, operator of Knolls Atomic Power Lab (KAPL), Aramco Services Co., and Electric Power Research Institute (EPRI) have developed a first MW size Brayton recuperated cycle. The project aimed at designing and engineering cycle components as well as testing the closed loop. [PERSON] [72] reported ongoing R&D at Echogen Power Systems LLC where they compared several cycle architectures based on supercritical Carbone dioxide Brayton cycles. They came out with a \"dual rail s-CO\({}_{2}\) cycle\" that was further tested in a simple version [61]. It is a modified standard recuperated Brayton cycle which utilizes multiple stages of recuperation and heat recovery. The cycle architecture integrates two turbines, the main one being decoupled from the compressor. The working fluid stream is split into two streams, and directed in both turbines. The power producing turbine bear the highest share while the other coupled to the compressor receives enough flow to drive the compressor.
# Experimental Study on the Impact of CO2 Treatment on Different Lithofacies in Shale Oil Reservoirs
## 3 Results and Discussion
### Porosity and Permeability Analysis of Different Lithofacial Types of Shale during CO2 Treatment
After CO\({}_{2}\) treatment,  
Figure 8: Porosity analysis of shale core samples during CO\({}_{2}\) treatment. Figure 9: Permeability analysis of shale core samples during CO\({}_{2}\) treatment. the large-scale dissolution of these minerals occurs and results in many large-sized pores, and the sizes of new dissolution pores can reach 10-30 \(\upmu\)m in diameter. Although the precipitation and migration of minerals caused by CO\({}_{2}\) treatment can partially block pores and throats, the large-scale dissolution effect dominates the change rule, leading to an increase in porosity and permeability. Therefore, it might be better to apply CO\({}_{2}\) technology for enhancing oil recovery when the target formation mainly consists of laminated felsic shale. For laminated diamic shale, both porosity and permeability improve significantly after CO\({}_{2}\) treatment. After 7 days' treatment, the porosities of the Q5 and Q6 samples increase by 17.68% and 24.78%, and their permeability increases by 412.47% and 872.21%, both respectively. In combination with SEM analysis, besides large-sized feldspar particles, there are many biogenic calcium carbonate minerals. The dissolution effect results in many dissolution pores and dissolution microfractures, which can significantly improve the porosity and pore connectivity of shale. Although the precipitation and migration of minerals caused by CO\({}_{2}\) treatment can partially block pores and throats, the existence of dissolution pores and fractures dominates the change rule, leading to a dramatic increase in porosity and permeability. When the target formation mainly consists of laminated diamictic shale, the application of CO\({}_{2}\) technology is recommended. Through the mechanisms of miscible flooding, viscosity reduction, formation energy supplements and improvement in porosity and permeability, CO\({}_{2}\) technology can effectively enhance shale oil recovery.
## 2 Materials and Methods
### Development and Design Process of the Control System Hardware  
This section gives a detailed explanation of the design process and implementation of each of the interface components. It also describes how the materials fit into the overall development of the interface.  
According to Figure 3, the main control unit (STM32F103 VE) collects and processes eight EMG signals using an analogue signal acquisition module (AD7606), which is used to convert EMG output signals from analogue to digital. It then determines the movement angle based on the calculation process, outputs the corresponding control signal to the motor circuit, and rotates the motor accordingly to drive the slide motion of the lower limb. At the same time, the main control unit acquires the signal from the angle sensor in real time to identify and correct the current angle of the mechanical device. An LCD screen displays the current operation angle; at the same time, the EMG signals are sent to a PC, displayed on screen, and saved in the memory. The terminal block SCSI-II is required to take the EMG signal from the base station to the acquisition ADC circuit.  
A prototype circuit board manufactured for some parts, such as a driver circuit board with a protection board, was designed to control the motor motion by STM32. Furthermore, an angle sensor acquisition circuit was designed for the filtering and amplification of the signal before it was received by STM32. In addition, an ADC circuit was designed for circuit protection and filtering of the signals.  
Figure 2: The CPM machine (**left**) and the disassembled mechanical section (**right**).
## 3 Results and Discussion
Studies on the influence of a magnetic field, both in terms of its impact on cell function and drugs attached to magnetic nanoparticles for delivery, have been described in the literature [32; 50; 51; 53; 68; 71]. Indeed, the intersection of nanotechnology, medicine, and external influences like magnetic fields holds significant promise and raises intriguing questions about the potential use of magnetic nanoparticles functionalized with drugs and participation of the magnetic field in medical diagnostics or treatment processes. Thus, this research holds immense importance and warrants continuous exploration.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 3 Experimental Results and Discussion
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(\mathbf{R_{TP}}\)*} & \multicolumn{3}{c}{**LasHeR**} & \multicolumn{3}{c}{**DepthTrack**} & \multicolumn{3}{c}{**VisEvent**} \\ \cline{3-8}  & & & \(\mathbf{Sr(\uparrow)}\) & \(\mathbf{Pr(\uparrow)}\) & \(\mathbf{F1(\uparrow)}\) & \(\mathbf{TRe(\uparrow)}\) & \(\mathbf{Pr(\uparrow)}\) & \(\mathbf{Sr(\uparrow)}\) & \(\mathbf{Pr(\uparrow)}\) \\ \hline TBSI [4] & 100\% & 0.506 & 0.638 & - & - & - & - & - \\ APFNet [5] & 100\% & 0.362 & 0.500 & - & - & - & - & - \\ FANet [17] & 100\% & 0.309 & 0.441 & - & - & - & - & - \\ CAT [12] & 100\% & 0.314 & 0.450 & - & - & - & - & - \\ MDNet [36] & 100\% & - & - & - & - & - & 0.426 & 0.661 \\ PrDIMP50 [37] & 100\% & - & - & - & - & - & 0.453 & 0.644 \\ LTMU [38] & 100\% & - & - & 0.460 & 0.417 & 0.512 & 0.459 & 0.655 \\ OSTrack [32] & 100\% & 0.412 & 0.515 & 0.529 & 0.522 & 0.536 & 0.534 & 0.695 \\ TransT [39] & 100\% & 0.394 & 0.524 & - & - & - & 0.474 & 0.650 \\ DAL [8] & 100\% & - & - & 0.429 & 0.369 & 0.512 & - & - \\ TATrack [40] & 100\% & 0.559 & 0.702 & - & - & - & - & - \\ TABBTrack [41] & 100\% & - & - & 0.618 & 0.615 & 0.622 & - & - \\ ProTrack [29] & \textless{}1\% & 0.420 & 0.538 & 0.578 & 0.573 & 0.583 & 0.471 & 0.632 \\ ViPT [19] & 0.4\% & 0.525 & 0.651 & 0.594 & 0.596 & 0.592 & 0.592 & 0.758 \\ Un-Track [42] & 6.74\% & 0.536 & 0.667 & - & - & - & 0.589 & 0.755 \\ MPVT & 0.9\% & 0.578 & 0.724 & 0.628 & 0.626 & 0.631 & 0.624 & 0.761 \\ \hline \hline \end{tabular}
* \(R_{TP}\)—ratio of trainable parameters.  
\end{table} 
Table 2: The results on LasHeR, DepthTrack, and VisEvent. Regarding efficiency, MPVT has 0.9% (2.8 M) trainable parameters while achieving competitive tracking performance. It shows a higher transfer efficiency compared with traditional fully fine-tuning models, whose trainable parameter ratios are 100%, and learnable parameters are generally greater than 100 M. ViPT is a prompt-based model and has 0.4% (0.84 M) trainable parameters, and although MPVT has more trainable parameters than this, it achieved better tracking performance across all three datasets. Experimental results across the RGB-T, RGB-D, and RGB-E scenarios demonstrate that MPVT preserves the feature extraction capabilities of pre-trained models. Through decoupled input enhancement and dynamic prompt fusion, it avoids feature degradation while effectively integrating complementary multi-modal information. Compared with full-parameter fine-tuning, MPVT maintains higher transfer efficiency. Figure 3: Precision in 15 degraded scenarios in the LasHeR dataset.
# Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach
## 3 Development of a Hazard Model
### Existing and External Seismic Models of QuakelST  
QuakelST has a built-in library with existing seismic and attenuation models that can be used to simulate the response of a seismic event based on local (geological, structural, etc.) conditions [24]. The simulation can be carried out using attenuation models that are typical for Europe. More specifically, the attenuation can becalculated using expressions proposed by [PERSON] et al. [47], [PERSON] et al. [48; 49], [PERSON] [50], among others.  
Additionally, the user can use real data of a regional earthquake and create an \"external\" seismic scenario. In this case, the user should provide data, e.g., accelerations, recorded during an earthquake at various points to represent spatial variability of the examined region, since it is realistic to assess the hazard and the expected damage using data from past seismic eventsof this specific area. Provided that sufficient field data are available, the simulation results obtained via QuakelST can be compared with the recorded ones to assess the most suitable seismic and attenuation models. The interpretation of the results can be done both qualitatively (identification of the most dangerous zones) and quantitatively (comparison between the average expected degrees of damage).  
For comparable results with those of the external scenario, the data (using coordinate system WGS84) of the nearby M\({}_{\text{W}}\) = 6.9 earthquake at Kythira (36.16 N 23.36 E, with a focal depth of 66 and 93.86 km NW of Chania city) in 2006, and M\({}_{\text{W}}\) = 5.3 earthquake at Gavdos (34.90 N 24.112 E, with a focal depth of 6.5 and 68.62 km S-SE of Chania city) in 2012, at different (from NW and SSE of Chania city) azimuths, was used. To further assess the seismic risk of the city of Chania, a parametric study was performed with extreme seismic hazard scenarios using a known seismic source with different magnitudes. For this purpose, the Kythira earthquake data were used [38], but with higher magnitudes (M\({}_{\text{W}}\) = 7.5, M\({}_{\text{W}}\) = 8.0, and M\({}_{\text{W}}\) = 8.5) as it will be presented in the sequence.
## 3 Design of Various Lingual Retractors with TsADs
### Design of the APLR with TSADs  
The APLR basically consists of a C-lingual retractor bonded to the lingual surfaces of the six anterior teeth, lever arms, posterior splints, and posterior tubes, which act as pathways for the guide bar. The lever arms are soldered to the anterior splint, and the posterior tubes are soldered to the posterior splint. A guide bar with a diameter of 0.036-in extending from the lever arm passes and slides through the posterior tube (Figure 7) [1; 5]. Friction in the APLR is minimized compared to conventional lingual brackets as the friction in the APLR occurs only between the guide bar and the posterior tube during retraction.  
Figure 6: Schematic representation of the Double J retractor (DJR) designed to apply Class II elastics to the extended spring to the buccal side. Redrawn with permission from [PERSON] [22].  
Figure 7: Antero-posterior Lingual Retractor (APLR) appliance.  
(1) Anterior Segment  
The anterior part of the APLR was similar to that of the CLR. In addition, the 0.036-in stainless steel guide bar, soldered to the lever arm or attachment pad extends posteriorly into the posterior tube [1; 5].  
(2) Posterior Segments  
The posterior connecting wire is soldered to the posterior attachment pad to be positioned on the second premolar, first and second molar to form a posterior splint. A posterior tube 1 mm in diameter, typically parallel to the occlusal plane is soldered to the first molar area of the posterior splint and acts as a sliding path. The guide bar soldered to the anterior segment passes through the posterior tube and the clearance between the guide bar and posterior tube is approximately 0.1 mm [1; 5].  
(3) Accessory Parts  
The transverse dimension is controlled by adding a TPA. It is soldered to the extended arm which is connected to the first molar pad. In some cases, additional hooks are added to the TPA for posterior intrusion or torque control [1].  
(4) TSADs  
Generally, miniscrews or C-palatal plate are placed in the midpalatal area for torque and vertical control of anterior teeth [5].
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 5 Conclusions  
In order to improve the performance of the traditional GSR model in image restoration, the paper extends the GSR model to a new multi-color-channels-based case, and process R, G, and B color channels of an image simultaneously. The problem of image restoration is transformed into how to use multi-color channels for sparse representation. The alternating minimization method with an adaptive parameter setting strategy is used to solve the proposed multi-color-channels-based model. The proposed method is compared with state-of-the-art methods. The experimental results show that the proposed model can obtain good results in terms of visual effects and quantitative metrics.  
Conceptualization, Y.K., C.Z. ([PERSON]) and C.Z. ([PERSON]); methodology, C.Z. ([PERSON]); software, C.Z. ([PERSON]); validation, Y.K. and C.Z. ([PERSON]); formal analysis, L.S.; investigation, C.Z. ([PERSON]) and C.Z. ([PERSON]); resources, C.Z. ([PERSON]); data curation, L.S.; writing--original draft preparation, C.Z. ([PERSON]); writing--review and editing, C.Z. ([PERSON]); visualization, L.S.; supervision, C.Z. ([PERSON]); project administration, C.Z. ([PERSON]); funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
The research of this paper was Funded by Ship Control Engineering and Intelligent Systems Engineering Technology Research Center of Shandong Province (Grant Number: SSCC20220003).  
Figure 4: The restoration result of girl with pixel missing rate of 80%. (**a**) Girl, (**b**) girl with pixels missing rate 80%, (**c**) SALSA [38] (PSNR = 23.79), (**d**) BPFA [4] (PSNR = 22.47), (**e**) GSR [13] (PSNR = 25.50), (**f**) MAR-LOW [41] (PSNR = 29.21), (**g**) JPG-SR [14] (PSNR = 25.55), (**h**) GSRC-NLP [27] (PSNR = 26.02), (**i**) our model (PSNR = 29.28).
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 3 Resource Allocation in MC Multicast
Note that our system model construction ensures that only one resource is allocated to the multicast stream in each time slot, which prevents overloading the system while serving several video streams. Therefore, we used the number of users served to illustrate the effectiveness of the resource allocation algorithms in meeting the video streaming demands of users within the limited resources. In the system under consideration, since a UE can receive the same content from several base stations, its performance is impacted by the resource allocation decisions across multiple cells. Therefore, the resource allocation needs to be optimized over all \(C\) cells in the system. Throughout this study, we assumed that the users are static and do not change positions for the entire duration of the multicast transmissions. For the mathematical formulation of the resource allocation problem, we first define the following sets. Assuming that every UE is trying to receive the multicast content from the base station of cell \(c\), let us use \(U_{jc}\subseteq[M]\) to denote the set of users that would successfully receive the multicast content if PRB \(j\) is allocated to the multicast service in cell \(c\), i.e., for all \(c\in[C]\) and all \(j\in[N]\), the set \(U_{jc}\) is given by  
\[U_{jc}=\{k\in[M]:R\leq r_{jk}^{c}[t]\}. \tag{1}\]  
The collection of all such sets corresponding to cell \(c\) is given by  
\[\mathcal{U}_{c}=\{U_{1c},U_{2c},\ldots,U_{Nc}\}. \tag{2}\]  
Let \(\mathcal{U}\) be the collection of sets \(\mathcal{U}=\{\mathcal{U}_{1},\ldots,\mathcal{U}_{C}\}\). In using these definitions, the resource allocation problem for the MC multicast system can now be stated as follows. **Definition 1** (Resource allocation problem \(\mathbf{K^{\star}}\)). : _Given the universal set of all users \([M]\) and the collection of sets \(\mathcal{U}=\{\mathcal{U}_{1},\ldots,\mathcal{U}_{C}\}\), determine \(\mathcal{U}^{\prime}\subseteq\mathcal{U}\) such that \(|\bigcup_{U_{jc}\in\mathcal{U}^{\prime}}U_{jc}|\) is maximized subject to the following:_  
\[|\mathcal{U}^{\  
set \(U_{j_{C}}\) is chosen for every cell \(c\), i.e., a PRB is allocated for multicast streaming in every cell. The resource allocation decisions are a function of (a) the channel states of UEs in each of the \(N\) PRBs, (b) the number of UEs streaming the multicast content, and (c) the location of the UEs with respect to each base station.
## 2 Description of the IMM-UKF Filtering Problem  
### Maneuvering Target Tracking Dynamic System  
The general IMM-UKF structure diagram, including \(m\) models, is shown in Figure 1, where \(\mathbf{Z}\) represents the unit delay link. The IMM-UKF can be divided to two parts: the external link-IMM structure and the internal link-UKF filter.  
In Figure 1, \(\hat{\mathbf{X}}_{i,k}\) and \(\mathbf{P}_{i,k}\) are the state estimation and covariance matrix, respectively, of the single UKF filter \(i\) (\(i=1,\ 2,\ \dots,\ m\)) at time step \(k\); \(\hat{\mathbf{X}}_{i,k-1}\) and \(\mathbf{P}_{i,k-1}\) are the corresponding vector at time step \(k-1\); \(\hat{\mathbf{X}}^{o}_{i,k-1}\) and \(\mathbf{P}^{o}_{i,k-1}\) are the interaction result of \(\hat{\mathbf{X}}_{i,k-1}\) and \(\mathbf{P}_{i,k-1}\), and they are the inputs of UKF filter \(i\); \(\hat{\mathbf{X}}_{fusion,k}\) and \(\mathbf{P}_{fusion,k}\) are the final state estimation and covariance of the IMM-UKF filter, respectively; and \(\mathbf{Z}_{k}\) denotes the measurement vector for the single UKF filter.  
For single model \(i\), the state and measurement equations of the maneuvering target are [1; 4]  
\[\left\{\begin{array}{l}\mathbf{X}_{k}=\mathbf{f}[\mathbf{X}_{k-1},k-1]+\mathbf{\Gamma}_{k-1} \mathbf{W}_{k-1}\\ \mathbf{Z}_{k}=\mathbf{h}[\mathbf{X}_{k},k]+\mathbf{V}_{k}\end{array}\right. \tag{1}\]  
where the subscript \(i\) is omitted for the sake of brevity.  
Figure 1: General interacting multiple model-unscented Kalman filter (IMM-UKF) structure.  
Similarly, the state and measurement equations of the maneuvering target in the presence of sensor faults are [35; 37]  
\[\left\{\begin{array}{l}\mathbf{X}_{k}=f[\mathbf{X}_{k-1},k-1]+\mathbf{\Gamma}_ {k-1}\mathbf{W}_{k-1}\\ \mathbf{Z}_{k}=\boldsymbol{h}[\mathbf{X}_{k},k]+\mathbf{V}_{k}+\delta \boldsymbol{h}_{out,k}\end{array}\right. \tag{2}\]  
where \(f[\bullet]\) and \(\mathbf{\Gamma}_{k-1}\) represent the state transfer function and interference transfer matrix, respectively; \(\mathbf{W}_{k-1}\) and \(\mathbf{V}_{k}\) denote the system and measurement noise, respectively, and they are both white Gaussian noises and mutually uncorrelated; \(\boldsymbol{h}[\bullet]\) is a nonlinear coordinate transformation function; and \(\delta\boldsymbol{h}_{out,k}\) denotes the model mismatches caused by sensor faults.