esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nowadays, massive advances have been made in research fields to improve cell culture techniques [1; 2; 3]. Previously, a model of a two-dimensional (2D) monolayer cell culture was the fundamental technique for cellular research [4]. However, this technique often failed to fully imitate the cellular functions and behaviors of cells [4; 5]. In addition, in a 2D cell culture, the microenvironment is not physiologically uniform due to the absence of nutrients, oxygen, and extracellular matrix (ECM), which leads to a failure in exhibiting the cell development process [6; 7]. Other limitations of this method, such as loss of cell mechanical and biochemical signals, loss of tissue-specific architecture, loss of cell-to-cell interactions, and loss of cell-to-matrix interaction have also been reported [4; 8; 9]. Due to this fact, it is crucial to develop a suitable environment to properly nourish cells and preserve cell growth and proliferation [4; 10]. In this way, a three-dimensional (3D) cell culture has emerged as the current state-of-the-art, providing better cellular characteristics of an in vivo environment for cells [5]. In light of that, numerous modifications have been proposed to improve cell growth in three dimensions, some of them are increasing scaffold bioactivity and using a scaffold for cells [11; 12]. Nevertheless, in this technique, the challenge for a more sophisticated scaffold system to support and facilitate cell growth and desirable tissue formation needs to be considered [2].  
In recent years, Pluronic F127 hydrogel has attracted particular interest in 3D cell culture [13]. Unique characteristics of F127 such as thermo-reversible, micellar properties, and gelation behavior with excellent solubility allowed this polymer to be used as a carrier for drug delivery, controlled-release, and tissue engineering applications [14]. A previous study investigated the potential of F127 as a cell encapsulation material using a human hepatic carcinoma cell [13]. Additionally, F127 as graft materials has also been proved to facilitate the growth and differentiation of mesenchymal stem cells (MSCs) into osteogenic lineage [15; 16]. In spite of that, high demand still exists for the formulation of this material purposing to ameliorate cell growth, proliferation, and differentiation when seeding the cells in vitro [5].  
Arg-Gly-Asp (RGD) is an integrin-binding site, which belongs to the class of adhesive proteins [17]. The RGD region is found in various proteins and is crucial for facilitating cell-adhesive activity. Moreover, it has been reported that the Gly-Arg-Gly-Asp (GRGD) peptide sequence is identical to the cell-binding region of fibronectin protein [18]. Therefore, the present study aimed to investigate the F127 with modified GRGD copolymer (hereafter defined as 3 BE) hydrogel in its biocompatibility with distinct cell types. In this study, we used human hair follicle-derived mesenchymal stem cells (HFMSCs) due to its easy accessibility [19; 20]. The HFMSC has been proved to have similar characteristics with bone marrow-derived MSCs and is suggested as an important and alternative source for MSC [20].
###### Abstract  
Recent experiments have shown interactions between the cavitation and fluid vortex formation in a hydrodynamic torque converter. This study aimed to clarify the unsteady cavitation trigger mechanism and flow-induced vibration caused by turbulence-cavitation interactions. The mass transfer cavitation model and modified Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model were used with a local density correction for turbulent eddy viscosity to investigate the cavitation structure in a hydrodynamic torque converter under various operating conditions. The model results were then validated against test data. The multi-block structured gridding technique was used to develop an orthogonally structured grid of a three-dimensional full-flow passage as an alternative analysis method for the cavitation flow. The results indicated that the re-entrant jet is the main cause of the shedding cavitation and breaking O-type cavitation. The re-entrant jet is driven by the reverse pressure gradient to move upstream towards the stator nose, and it lifts and splits the attached cavitation, which periodically induces shedding cavitation. When the cavitation was considered, the prediction error of the capacity constant was reduced from 13.23% to \(<\)5%. This work provides an insight into the cavitation-vortex interactions in a hydrodynamic torque converter, which can be used to improve the prediction accuracy of the hydrodynamic performance.  
hydrodynamic torque converter; turbulence-cavitation interactions; re-entrant jet; shedding cavitation; characteristics prediction
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 3 Non-Thermal Processing
### Pulsed Electric Field (PEF)  
PEF is a method used in food industry to control microbiological safety and changes in the characteristics (nutritional, sensory, physicochemical, etc.) of food products. Such possibilities are provided by the use of PEF in the electroporation process. It is a simple, non-toxic method that induces pores in the cell membrane under the influence of short, pulsating pulses of electricity. [PERSON] et al. [54] is one of the few studies focusing on the influence of PEF on the volatile compounds profile. The authors have shown in their work that some VOCs, including 2-nonanone, 2-pentylfuran, pyrrole, methylpyrazine, 2-ethyl-3-methylpyrazine, and thiophene correlate with the meaty and juicy taste of PEF-treated frozen lamb pieces. In addition, PEF is associated with the formation of amino acids in lamb (threonine, phenylalanine, isoleucine, tyrosine, and methionine) and some volatile compounds (heptanal, 2-ethylfuran, pyridine, dimethyl disulfide, dimethyl trisulfide, and 3,5-diethyl-2-methylpyrazine). On the other hand, it should be noted that PEF may adversely affect lipid oxidation, which results in a change in the profile of volatile compounds [55]. This is especially true for meat that is not refrigated. Such a discovery was made by [PERSON] et al. [56], who, in their work, described changes in the profile of beef _longissimus thoracis et lumborum_ and _semitendinosus_ volatile compounds caused by an increase in protein and lipid degradation products, such as dimethyl disulfide and 2,3-octanedione. Similar observations were made in [PERSON] et al.'s [57] study, where it was found that the use of porcine pancreas lipase (PPL) or lipid hydrolysis in PEF-treated skin prior to papain hydrolysis lowers the level of volatile compounds that are formed as a result of lipid oxidation. Nevertheless, they found that the processing of PEF had an effect on the VOCs profiles of both chilled and frozen pre-processed meat [58].
# The Effect of Different Socket Types on Implant Therapy While Using Flapless Ridge Preservation
## 4 Discussion
### Treatment Flowchart  
A flowchart of the treatment performed according to the condition of the extraction site and the socket morphology is shown in Figure 2. This classification system is simple and easily implementable by clinicians. Many factors need to be considered when making decisions about socket management after tooth extraction: systemic diseases, infection, primary stability, hard and soft tissue destruction of the socket, the thickness of the buccal wall, soft tissue phenotype, aesthetics, and pathological/physiological processes associated with wound healing [28; 29; 30; 33]. Signs of infection at the extraction site include abscess, sinus tract, and plus drainage. Tooth abscess could affect alveolar ridge remodeling [27] and infection could lead to poor healing outcomes when using flapless ridge preservation [33; 34]. Therefore, flapless ridge preservation is not indicated in cases with infected sockets. There are other treatment options that could be chosen according to the primary stability and aesthetic demand in Class I sockets. If primary stability could be gained by placing implants into the native bone (as ascertained from a CT scan), then immediate implantation may be suggested.  
A critical size defect is defined as an osseous defect that fails to heal spontaneously. Therefore, ridge preservation or ridge augmentation is suggested for a Class I socket size with a diameter greater than 10 \(\times\) 10 mm\({}^{2}\) because of poor bone repair [35; 36]. Additionally, when an extraction socket is close to the maxillary sinus, ridge preservation could be used to reduce sinus pneumaticzation and alveolar bone resorption [37; 38].  
Severe soft- and hard-tissue loss is characteristic of Class IV sockets, which induces poor space maintenance. It may be recommended that a collagen plug is placed into the extraction socket to avoid soft tissue wall collapse. Rather than using flapless ridge preservation, performing ridge augmentation (ridge contouring) after the soft tissue has healed would be preferable for such large defects.  
Each socket should be managed individually due to the complexity of the defect [29]. Furthermore, multiple root sockets of molars should be treated individually, based on the tissue loss of the area. For example, ridge preservation is necessary for mesial root sockets  
Figure 2: Treatment flowchart of management for extraction socket depending on Chang’s classification.  
without a buccal plate, but natural healing is preferred for distal root sockets with intact bone walls.
## 3 Theoretical Background
### Atmospheric Attenuation  
Atmospheric attenuation is mainly caused by atmospheric gasses, and especially at high frequencies, it has a significant impact on the budget for the communication systems connectivity [13]. The attenuation \(\gamma\) is defined as  
\[\gamma^{\mathrm{[dB/km]}}=\gamma_{o}^{\mathrm{[dB/km]}}+\gamma_{w}^{\mathrm{ [dB/km]}} \tag{4}\]  
where \(\gamma_{o}\) is the specific attenuation due to dry air and \(\gamma_{w}\) is the specific attenuation due to water vapor. The atmosphere loss is determined by multiplying the specific attenuation \(\gamma\) by the distance \(d\) that the signal travels.  
The power loss from gas attenuation, denoted as \(L_{atm}\), is defined as  
\[L_{atm}^{\mathrm{[dB]}}=\gamma^{\mathrm{[dB/km]}}*d^{\mathrm{[km]}} \tag{5}\]  
where \(\gamma\) is the atmospheric attenuation and \(d\) is the distance between the transmitter and the receiver.
# Resonance-Driven Passive Folding/Unfolding Flapping Wing Actuator
## 2 Structure and Modelling
### Modelling
#### 2.2.2 Lift Force  
Similar to the derivation of the centrifugal force, we consider that the lift force acts on a small element of the wing, \(\mathrm{d}F_{L}\), which is expressed as  
\[\mathrm{d}F_{L}=\frac{1}{2}\rho_{\mathrm{Air}}C_{L}v^{2}b_{w}\mathrm{d}r=\frac{1 }{2}\rho_{\mathrm{Air}}C_{L}\Big{[}(r\cos\theta R_{0})\dot{\phi}\Big{]}^{2}b_{w }\mathrm{d}r, \tag{14}\]  
where \(C_{L}\) and \(b_{w}\) are the lift coefficient and wing chord width, respectively [15]. Torque \(\mathrm{d}T_{L}\) is given calculated as \(\mathrm{d}T_{L}=\mathrm{d}F_{L}\); thus, total lift torque \(T_{L}\) is obtained by integration of \(\mathrm{d}T_{L}\) based on Equation (14) as  
\[T_{L}=\int_{0}^{L_{w}}\frac{1}{2}\rho_{\mathrm{Air}}C_{L}\Big{[}(r\cos\theta R _{0})\dot{\phi}\Big{]}^{2}b_{w}\mathrm{d}r=\frac{1}{2}\rho_{\mathrm{Air}}C_{L} b_{w}\Big{(}\frac{1}{4}L_{w}^{2}\cos^{2}\theta+\frac{2}{3}L_{w}R_{0}\cos \theta+\frac{1}{2}R_{0}^{2}\Big{)}L_{w}^{2}\dot{\phi}^{2}. \tag{15}\]  
The time-averaged lift torque is calculated based on Equation (15) as  
\[\overline{T_{L}}(\theta,\omega,\Phi)=\frac{1}{2}\rho_{\mathrm{Air}}C_{L}b_{w} \Big{(}\frac{1}{4}L_{w}^{2}\cos^{2}\theta+\frac{2}{3}L_{w}R_{0}\cos\theta+ \frac{1}{2}R_{0}^{2}\Big{)}L_{w}^{2}\frac{\Phi^{2}\omega^{2}}{2}, \tag{16}\]  
Similar to \(\overline{T_{C}}\), by substituting Equations (6) and (8) into Equation (16), \(\overline{T_{L}}\) can be rewritten as a function of \(\theta,\ \omega\), and \(V\).
## 2 Proposed Circuit
### Non-Ideality Analysis  
A non-ideal DDCCTA can be characterized by  
\[ \begin{array}{c}V_{x}=\beta_{+j}(V_{y+1}+V_{y+2})-\beta_{-j}(V_{y-1}+V_{y-2} )\\ I_{z\pm}=\alpha_{\pm j}I_{x}\\ I_{o\pm}=\pm g_{mnj}(V_{z}-V_{1})\end{array}  \tag{37}\]  
where \(\beta_{+j}\) and \(\beta_{-j}\) are the non-ideal voltage gains between \(y_{+}\)-, \(y_{y-}\)- and \(x\)-terminals, \(\alpha_{\pm j}\) is the non-ideal current gain between \(x\)- and \(z\)-terminals, \(g_{mnj}\) is the non-ideal transconductance gain of \(j\)th DDCCTA. Ideally, these gains are a unity, and in practice, they are slightly less than one. For the non-ideal voltage and current gains for the circuit operates at \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Filtering Function**} & **Input** & **Output** & **Condition** \\ \hline \multirow{8}{*}{BPF} & Non-inverting & \(l_{3}\) & \(l_{3}\) & - \\ \cline{2-4}  & Inverting & \(l_{2}\) & \(l_{03}\) & - \\ \cline{2-4}  & Non-inverting & \(l_{2}\) & \(l_{04}\) & - \\ \cline{2-4}  & Inverting & \(l_{3}\) & \(l_{04}\) & - \\ \cline{2-4}  & Non-inverting & \(l_{3}\) & \(l_{05}\) & - \\ \cline{2-4}  & Inverting & \(l_{2}\) & \(l_{05}\) & - \\ \cline{2-4}  & Non-inverting & \(l_{2}\) & \(l_{06}\) & - \\ \cline{2-4}  & Inverting & \(l_{3}\) & \(l_{06}\) & - \\ \cline{2-4}  & Inverting & \(l_{1}\) & \(l_{01}+l_{03}\) & - \\ \cline{2-4}  & Non-inverting & \(l_{1}\) & \(l_{02}+l_{04}\) & - \\ \hline \multirow{8}{*}{BSF} & Non-inverting & \(l_{1}=l_{4}\) & \(l_{03}\) & \(g_{m3}=1/R_{1}\) \\ \cline{2-4}  & Inverting & \(l_{1}=l_{4}\) & \(l_{04}\) & \(g_{m3}=1/R_{1}\) \\ \cline{2-4}  & Non-inverting & \(l_{1}=l_{4}\) & \(l_{05}\) & - \\ \cline{2-4}  & Inverting & \(l_{1}=l_{4}\) & \(l_{06}\) & - \\ \hline \multirow{8}{*}{APF} & Non-inverting & \(l_{1}=l_{2}=l_{4}\) & \(l_{03}\) & \(g_{m3}=1/R_{1}\) \\ \cline{2-4}  & Inverting & \(l_{1}=l_{2}=l_{4}\) & \(l_{04}\) & \(g_{m3}=1/R_{1}\) \\ \cline{1-1} \cline{2-4}  & Non-inverting & \(l_{1}=l_{2}=l_{4}\) & \(l_{05}\) & - \\ \cline{1-1} \cline{2-4}  & Inverting & \(l_{1}=l_{2}=l_{4}\) & \(l_{06}\) & - \\ \hline \end{tabular}
\end{table} 
Table 2: Cont. frequencies much less than the corner frequencies of the DDCC, which is the first stage of DDCCTA, these non-ideal gains can be expressed by [13]  
\[\beta_{\pm j}(s)=1-\varepsilon_{v\pm j}\atop\alpha_{\pm j}(s)=1-\varepsilon_{i \pm j}\biggr{\}} \tag{38}\]  
where \(\varepsilon_{v\pm j}/\bigl{(}|\varepsilon_{v\pm j}|\ll 1\bigr{)}\) and \(\varepsilon_{i\pm j}/\bigl{(}|\varepsilon_{i\pm j}|\ll 1\bigr{)}\) are, respectively, the voltage and current tracking errors of \(j\)th DDCCTA. The frequency-dependent transconductance gain \(g_{mn}\) of the OTA, which is the second stage of the DDCCTA when the circuit works at frequencies much less than the cut-off frequency of the OTA, can be given by [53]  
\[g_{mn}(s)\cong g_{m}(1-\mu s) \tag{39}\]  
where \(\mu=1/\omega_{g}\) and \(\omega_{g}\) is the first pole frequency of the \(g_{m}\).
# Siamese Tracking with Adaptive Template-Updating Strategy
## 4 Experiments and Results  
Our template-updating network and Siamese tracking network were implemented based on the TensorFlow framework for Python with OpenCV 2.7. We had already run our Siamese tracking network on public dataset OTB100 and compared it with nine other tracking methods. In this part. we selected eleven vehicle videos in the OTB100 dataset: Car1, Car2, Car4, Car24, BlurCar1, BlurCar2, BlurCar3, BlurCar4, Suv, CarScale, and CarDark. Following the instruction of the OTB100 benchmark, the precision plots and success plots of one-pass evaluation (OPE) were plotted. Then, our Siamese tracking network with an adaptive template-updating strategy was tested on images taken at Eglin Air Base during the DARPA Video Verification of Identity (VIVID) program. This experiment set was performed by comparing the performance of our Siamese tracking network with or a without template-updating strategy. The image resolution in this experiment set was 640 x 480. From five video sequences (egtest01, egtest02, egtest03, egtest04, and egtest05), we chose 5000 images constituting our datasets for testing. All the above experiments were tested on a server with a TITAN X GPU and 3.5 GHz CPU.
###### Abstract  
In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (**R**emote Control, Image Acquisition, **O**peration Maintenance, and **D**ocument Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China's Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.  
unatended; machine learning; long- and short-term memory; lumped parameter method
## 3 Field Testing Results on Performance
### Obstacle-Adaptivity  
When the inspection robot is running in a horizontally placed pipeline, the wooden blocks with various heights are placed inside the pipeline to simulate obstacles or bulges in oil pipelines. The testing results show that the compliance mechanisms enable the inspection robot to successfully pass over the obstacles with 80-mm height inside oil pipelines, as shown in Figure 8.
## 5 Conclusions  
This paper introduced an analytical method to derive the vertical stiffness of fiber reinforced elastomeric isolators subjected to both axial and shear loads.  
A novel function has been introduced through this work to account for the increased vertical deformation of the elastomeric bearings following the horizontal displacement: the \(\psi\)_function_. This function, defined as the ratio between the vertical displacement under pure compression and the total vertical displacement (i.e., vertical displacement due to axial and shear loads), assumes values lower than one and reduces the vertical capacity of the bearings when displaced in the horizontal direction.  
The trends of the \(\psi\)_function_ with the main geometric and mechanical parameters of the FREI, namely the primary and secondary shape factors, as well as the shear modulus of the rubber, have been proposed. Different analytical approaches to compute the effective compressive modulus under pure compression and the buckling load of the bearings have been considered to investigate the influence of the different hypotheses used in the analytical studies. Better accuracy was obtained between analytical and numerical results when using the pressure solution for the effective compressive modulus and considering the reinforcement as rigid, with a maximum percentage difference of the order of 10% for a shear strain equal to 0.50. It has been seen how the secondary shape factor greatly affects the reduction of the vertical bearing capacity of the FREI. When this parameter assumes values lower than 2.5, the \(\psi\)_function_ rapidly decreases with the shear strain, and so does the vertical stiffness of the U-FREI. The shear modulus and the primary shape factor affects the shape of the \(\psi\)_function_ mainly when a great increase or decrease is operated on these parameters.  
The \(\psi\)_function_ has also been evaluated through a parametric finite element analysis on U-FREIs with different mechanical and geometric parameters. The numerical analyses validated the trends of the \(\psi\)_function_, showing how the proposed analytical method can be adequately adopted to estimate the vertical stiffness and the effective compressive modulus of U-FREIs under combined axial and shear loads.  
The results presented in this paper may be extended, considering different material models for the rubber, as well as variable mechanical properties of the reinforcements.  
S.G.: conceptualization, methodology, software, investigation, resources, writing--original draft; A.C.: resources, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 3 Motion Mapping Method for Position Teleoperation
### Fixed Scale Mapping in Free Space  
In the free space, the slave manipulator should be controlled to track the position at the human arm endpoint quickly and exactly. While there is no contact force in free space, the CyberForce without force feedback is used to track the motion of human arm. Considering the different configuration between human arm and slave manipulator, the appropriate mapping method should be implemented to maintain the tracking performance. For simplicity, the most frequently used fixed scale mapping method is utilized for position teleoperation in free space in this paper, as shown in Equation (3):  
\[\mathbf{X_{s}}=\mathbf{K_{m}}\mathbf{X_{m}}+\mathbf{X_{0}} \tag{3}\]  
where \(\mathbf{X_{s}}\) is the Cartesian position vector of the slave manipulator, \(\mathbf{X_{m}}\) is the position vector at the operator arm endpoint obtained by CyberForce, \(\mathbf{K_{m}}\) is the fixed scale matrix, and \(\mathbf{X_{0}}\) is the position offset vector. The detail calculation of \(\mathbf{K_{m}}\) and \(\mathbf{X_{0}}\) can be found in [24].