esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environments with Single High Frequency Acoustic Waves
#### 5.2.5 Multipath Effect  
As the multipath effect has the most impact on the accuracy of indoor direction finding, we also test the VUCA system stability in the multipath signals environment. In brief, we set two loudspeakers A and B broadcasting the same frequency acoustic signals in an empty room. We also set \(L_{A}=L_{B}=8m\), \(\phi_{A}=45^{\circ}\), \(\phi_{B}=135^{\circ}\) and \(\theta=\beta=0^{\circ}\) in Figure 11a.  
The volume of Speaker A is constantly 60%. Then, we change the volume of speaker B from 0% to 60% to emulate the impact of multipath effect on our results. The mean direction finding results are plotted in Figure 11b. When the volume of speaker B is set from 0% to 40%, it has little effect on our results due to the anti-multipath MUSIC algorithm.  
We then conduct experiments with the receiver standing close to the wall and only one speaker broadcasting in the environment. We set \(\phi=45^{\circ}\), \(\theta=\beta=0\) and \(L=2\), \(8\), \(15\), and \(20\) m. The standard deviation of the direction finding results is shown in Table 1.  
From Table 1, we find that if the user stands close to a wall and a signal source at the same time, the VUCA system will become unstable. We believe that the reason is the signal reflected from the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance** & **2 m** & **8 m** & **15 m** & **20 m** \\ \hline
**Standard Deviation** & \(8.7^{\circ}\) & \(3.9^{\circ}\) & \(4.2^{\circ}\) & \(4.5^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviation when user stands close to a wall.  
Figure 11: (**a**) multipath effect experiment; and (**b**) the mean azimuth of speaker A with different volumes of speaker B.  
Figure 10: (**a**) mean error of VULA and VUCA when \(\theta=0^{\circ}\);\(10^{\circ}\);\(20^{\circ}\) and \(L=8\) m; (**b**) standard deviations of angular error when \(L\leq 6\) m; and (**c**) standard deviations of angular error when \(L=24\) m.  
wall sometimes is stronger than the one directly from the acoustic source in this particular situation. Although such a circumstance is rare in practice, we still need to solve it in future work.
## 2 Pump Geometry and Asymmetric Pitch
### Shape of Gear-Type Oil Pump  
Vane pumps are relatively complex in shape, making them difficult to miniaturize, and are expensive compared to gear-type pumps called internal and external gear pumps. Internal and external gear pumps, which are widely used in automotive oil pumps due to their relatively low cost, generate hydraulic pressure by meshing two gears with the same clearance (as shown in Figure 2), such that the pitch between each tooth must be mechanically symmetric; therefore, asymmetric pitch application is impossible [3].  
The main source of noise in internal and external gear oil pumps is hydraulic pulsation, which occurs in various forms depending on the design of the pump's teeth and port shape, as shown in Equation (3) [18].  
\[\begin{array}{c}V_{i}=bV_{i}\\ \dot{m}_{i}=V_{i}\frac{d}{dt}p_{i}\frac{d}{dt}p_{i}+\rho_{i}\frac{d}{dt}V_{i} \end{array} \tag{3}\]  
where \(A_{i}\): the instantaneous area of the i-th chamber of the ump; \(V_{i}\): the instantaneous volume of the i-th chamber of the pump; \(\rho_{i}\): the instantaneous density of the i-th chamber of the pump; \(m_{i}\): the instantaneous liquid mass of the i-th chamber of the pump; \(p_{i}\): the instantaneous pressure of the i-th chamber of the pump; and \(b\): the gear width.  
Figure 1: Vane pump with asymmetric pitch [8].  
Figure 2: Structures of gear-type pumps: (**a**) internal gear type; (**b**) external gear type.  
Figure 3 shows the analytical simulation model based on Equation (3), which is formulated based on the geometry of one pump with six teeth. After the pump geometry is designed, the instantaneous hydraulic pulsation can be analyzed by calculating the instantaneous area of each chamber, \(A_{i}\), which varies according to the tip clearance, side (face) clearance, and port shape. Figure 4 shows the analytical simulation results of pressure fluctuation for two different types of pump designs. This study does not explore detailed pump design specifications, which are confidential, but emphasizes that the instantaneous shape of hydraulic pulsations appears differently according to the clearance and inlet/outlet port shape, as shown in Figure 4. These hydraulic pulsations mainly affect the noise and vibration during pump operations.  
This section describes the asymmetric pitch control technique that operates internal and external gear pumps, which cannot be applied mechanically to an asymmetric pitch, under conditions similar to applying an asymmetric pitch by modulating the rotation speed and torque of the motor.
## 5 Simulated Case Study  
A residential building has been considered to evaluate the proposed methodology. It is an apartment that has been created with Google SketchUp 2013 (Trimble Inc., Sunnyvale, CA, USA) [29], which is a well-known software tool to design 3D models of buildings. Then, the model has been imported in TRNSYS 17 (TESS LLC, Fort Lauderdale, FL, USA) simulation environment, that is a simulation software used to describe the thermal behavior of a system. The building has been placed in Milan Linate (Italy). Real weather conditions have been downloaded from TRNSYS 17 Weather Data [30] and have been employed.  
The apartment is shown in Figure 3 and it is composed of seven rooms (see Table 1) and placed in between two residential floors, which are represented by a grid in the figure. There are only two exterior walls, the southern and the eastern ones, for which their external surface temperature depends directly on the weather conditions. The other walls are shared with two adjacent other flats. It can be noted that there is also a corridor that is adjacent to all the other rooms. All the windows are placed in the East direction. The building has a heating system in each room, implemented in TRNSYS 17 by using TESS (Thermal Energy System Specialists) HVAC Library [31]. Two cases have been considered for it: in the first case the system is composed of fan-coil units (denoted as Type 600 block in the software), while in the second case radiators (denoted as Type 1231) are used. To better simulate a retrofitted already existent building, radiators have been employed as heating source. In fact, in many countries such as Italy these are the most common heating sources especially in old houses. The main characteristics of the different parts of the buildings are listed in the Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Room** & **Dimensions [m]** & **Number of Windows** & **Area of Each Window [m\({}^{2}\)]** \\ \hline
4 & \(5\times 3\times 2.7\) & 1 & 3 \\
5 & \(5\times 3\times 2.7\) & 1 & 3 \\
6 & \(7.5\times 15\times 2.7\) & 4 & 2.25 \\
7 & \(2.5\times 15\times 2.7,5\times 15\times 2.7\) & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the considered building. Rooms are numbered starting from the most southeastern one and following the anticlockwise direction.  
Figure 3: The seven rooms flat used as a benchmark. The corresponding room number is written in black.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Part** & **u-Value**\(\left[\frac{W}{m^{2}}\right]\) \\ \hline Ceiling & 0.806 \\ Interior walls & 0.508 \\ Exterior walls & 0.51 \\ Windows & 2.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construction characteristics of the different parts of the building.
## 4 Results and Discussion
### Flow Characteristic Analysis
On the other hand, with an increase in \(\pi\), the high-velocity regions gradually intensi  
Figure 14: Streamlines and velocity in seal cavities with different \(Re\) of \(10^{2}\), \(10^{5}\), and \(10^{8}\) and \(\pi\) of 4.5, 5.0, 5.5, and 6.0.  
regions remain primarily unaffected, which suggests that the influences of centrifugal force are more primary than those of pressure difference. Regardless of the \(Re\) and \(\pi\) values, vortices are generated within the cavities due to the velocity gradient between the walls and center, as presented in the streamlines in Figure 14, which is a primary reason for windage loss. Nevertheless, as discussed in Section 4.3, the windage loss in the labyrinth seal is considerably lower than that occurring in the impeller back gap, which is attributed to the fewer number of teeth and cavities presented in Figure 14, resulting in the impeller back gap being the primary contributor to windage loss. The pressure contour and streamlines at the outlet region for Case I when \(Re=10^{4}\) and \(\pi=4.5\) is illustrated in Figure 15a. It is apparent that the pressure is unchanged in the impeller back gap and it gradually decreases from the inlet of the labyrinth seal to the outlet. This behavior indicates that the increase in velocity in the impeller back gap is relatively small compared to that in the labyrinth seal, which is similar to the conclusions drawn in Figures 13 and 14. At the outlet region, the pressure decreases to a minimum value of 2 MPa, which matches the boundary condition specified in Table 3. In the last cavity of the labyrinth seal,  
Figure 15: Pressure contour and streamlines. (**a**) Pressure contour and streamlines at outlet region. (**b**) Streamline distribution in labyrinth seal. high-pressure regions near the walls and low-pressure regions at the center are observed, which is the opposite of the velocity contour presented in Figure 14. The reason for the discrepancy is the conservative conversion between pressure energy and kinetic energy. Furthermore, it is observed that the whole outlet region is filled with vortices because of the influences of shear stress generated by the rotational walls, which results in the leakage flow rate being inhibited. Figure 15b illustrates the distribution of streamlines in the labyrinth seal. The vortices formed in the seal can be clearly visualized, consistent with those in Figure 14. And a high velocity region appears at the top. After the fluid passes through the seal, a large cavity is formed between the two seals, resulting in a sudden increase in fluid volume and the formation of a strong vortex. Pressure recovery in this area is limited.
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
## 4 Practical Applications
### Co-Cultures  
Co-cultures of articular chondrocytes (ACs) and MSCs have been proposed to overcome problems associated with the dedifferentiation of chondrocytes during in vitro expansion or to the tendency of MSCs to acquire hypertrophic features [42; 43]. In co-cultures, the milieu created by the chondrocytes can, in fact, stimulate stem cell differentiation, which, in turn, acts as an enhancer of phenotype stability and the proliferation of chondrocytes by stimulating cell-cell adhesion mechanisms and by secreting several paracrine factors like growth factors and cytokines. Proper mechanical stress, in the form of cyclic sinusoidal dynamic tensile mechanical stimulation, can stimulate the co-culture by improving the deposition of ECM (GAGs, type II collagen) and the expression of regulatory factors (TGF\(\beta\), SOX9) and promoting the exchange of molecules between MSCs and chondrocytes [44].  
Co-cultures offer the advantage of overcoming the tissue engineering challenge of maintaining a sufficient number of functional chondrocytes which will provide structural reliability with the right amount of ECM. Scaffolds embedded with ADSCs, subjected to cyclic compression in bioreactors, could benefit from the presence of chondrocytes, since they are able to release paracrine factors (i.e., TGF-\(\beta\)1 and IGF-1), guiding stem cells towards chondrogenic differentiation. This approach has been used to reduce the use of exogenous growth factors, which can instead be synthesized by a proper number of chondrocytes during in vitro expansion [35]. Even though similar production of type II collagen and GAGs was observed in single and co-cultures, the latter were shown to be able to suppress the expression of Col I, Col X, and Tumor necrosis factor alpha (TNF-\(\alpha\)) markers [45].
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 5 Simulation Results  
An incremental evaluation of the algorithms has been made. All fixed parameters are reported in Table 1. As the objective is to evaluate the performance of the UAV topology control algorithms, the spreading factor of LoRaWAN was fixed to SF7, the worst case, resulting in the shortest AtG communication range. The routing algorithm used in the UAV mesh is Optimized Link State Routing Protocol (OLSR) [22]. The main evaluation metrics are the Average End-to-End Packet Reception Ratio (AE-PRR) and the Average Total Delay (ATD). The first metric measures the average PRR of unique packets at the BS, while the second metric measures the average end-to-end delay, including the buffer delay of uplink packets. Averages consider 100 runs of the same scenario (seed in \([0,99]\)). The 95% confidence interval is also computed assuming a Student's t-distribution of the population. The default simulation parameters are reported in Table 2.  
A higher value of the \(K_{p}\) parameter favors the compactness of the aerial mesh, reducing the disconnections. However, it reduces the coverage. \(K_{p}\) should therefore be optimized in each scenario to achieve the best compromise between the desired coverage and delay. This optimization is relegated to future work. In the presented simulation results, \(K_{p}\) is treated as an independent variable.  
Figure 3: Firefighter team mobility model. Teams start from a starting point and go down to the furthest cell in their assigned column.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 2 Related Works
### Traditional Methods  
Histogram-based methods enhance image contrast by adjusting the pixel value distribution at both global and local levels. For instance, [PERSON] et al. [7] employed an iterative approach to select the segmentation threshold in order to drive the mean toward convergence to the optimum. Moreover, [PERSON] et al. [8] separated the input image into the background and target regions, whereas [PERSON] et al. [9] independently processed the textured and smoothed portions of the image.  
Retinex-based methods divide images into components related to reflection and illumination, with reflection capturing intrinsic object qualities independent of illumination. Consequently, low-light image enhancement can be reframed as a well-exposure estimation problem. For instance, [PERSON] et al. [10] estimated illumination by initially calculating the maximum value in the R, G, and B channels, refining the illumination map through the application of a structural prior. Furthermore, [PERSON] et al. [11] introduced a weighted variation model to accurately preserve the estimated reflectivity while mitigating noise to some extent.  
S-curve-based methods transform pixel values of low-light images to normal-light images using mapping functions, where gamma correction is deployed as a representative approach. For instance, [PERSON] et al. [12] effectively compressed dynamic range by applying an adaptive logarithmic function to map the image's gray values. Moreover, [PERSON] et al. [13] introduced a low-light color image enhancement algorithm using a logarithmic processing model. It involves a nonlinear enhancement step applied to image brightness components, followed by adjusting the membership function through the introduction of an enhancement operator. The final enhanced image is obtained through the inverse transformation of the membership function. In addition, [PERSON] et al. [14] proposed an adaptive gamma correction algorithm that dynamically determines the gamma correction parameter based on the cumulative probability distribution histogram. Furthermore, [PERSON] et al. [15] constructed a double gamma function, adjusting it according to the distribution characteristics of the illumination map. This modification aimed to improve gray values in low-brightness areas while suppressing them in local high-brightness regions.
## 3 Hydroxyapatite Particles in Scaffold Manufacturing and Cell Signaling  
Intensive research has been carried on finding a suitable alternative for the proper healing of bone tissue. Metallic implants started as bioinert devices accepted by the host and after decades scientists were focused on how they can actively influence the actions of the surrounding tissues. The same pathway was also followed in the case of scaffolds, the first generation being composed of bioinert scaffolds, the second one was represented by bioresorbable and bioactive scaffolds, and presently, the third generation under investigation is the one of bioinstructive scaffolds which are meant to stimulate a particular cellular response [38].  
One of the most exploited classes of materials in bone tissue applications is represented by ceramics which already have a broad range of applications available on the market. In terms of properties, ceramics stand up owing to their high hardness, low conduction of electricity, porosity, and their enhanced corrosion and compression resistance. Additionally, their surface characteristics are able to promote the adhesion of a wide range of molecules and cells [41].  
First of all, one of the most discussed ceramics in hard tissue area is hydroxyapatite (HA), the mineral constituent of the tissue, which is an inorganic compound able to trigger both osteoconduction and osteoinduction. The structure of natural HA presents multiple trace elements, such as Mg\({}^{2+}\), Si\({}^{2+}\), Ba\({}^{2+}\), K\({}^{+}\), Na\({}^{+}\), F\({}^{-}\), Zn\({}^{2+}\), and CO\({}_{3}\)\({}^{2-}\). When compared, it can be stated that non-stoichiometric (natural) HA is deficient in calcium (Ca) or phosphorus (P), while stoichiometric (synthetic) HA has a molar ratio of 1.67, exhibiting a beneficial effect on bone regeneration processes [42].  
Synthetic HA (Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)) proved to be highly biocompatible with a chemical composition comparable to the native tissue. Furthermore, its bioactive and osteoinductive characteristics make it highly suitable for a multitude of applications. A variety of methods have been developed over time to obtain HA-based scaffolds, including solvent casting, gas foaming, electrospinning, 3D printing, or freeze-drying techniques. The bioactivity of HA has been demonstrated over the past decades [43; 44; 45]. Additionally, it has been observed that porosity gives an osteoinductive behavior to the scaffold, mediating the interactions at ECM levels. Therefore, investigation has been focused on developing porous scaffolds, with the help of different substances which can change the shape, dimension, or architecture of the pore matrix [13].
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 4 Real-Time Simulation Construction of Antenna Physical Field
### Surrogate Model Training
Therefore, based on Equation (13), the SSE for conditional optimization is expressed as  
\[SSE_{\text{optimized}}(j,s)=\min_{j,s}\left[\sum_{x_{i}\in R_{1}(j,s)}(y_{i}- \hat{c}_{1})^{2}+\sum_{x_{i}\in R_{2}(j,s)}(y_{i}-\hat{c}_{2})^{2}\right] \tag{14}\]  
where  
\[\hat{c}_{1}=\frac{1}{N_{1}}\sum_{x_{i}\in R_{1}(i,s)}y_{i}\quad\hat{c}_{2}= \frac{1}{N_{2}}\sum_{x_{i}\in R_{2}(i,s)}y_{i} \tag{15}\]  
After finding the optimal partition point through the above steps, the deformation values of the nodes within the input space are partitioned into two distinct regions, and the above procedures are iteratively applied to each of these regions until the specified conditions are met. The final form of the regression tree model is given as:  
\[f(x)=\sum_{m=1}^{M}\hat{c}_{m}I(x\in R_{m}) \tag{16}\]  
After the training is completed, two evaluation indexes of the root average square error (\(RMSE\)) and decision coefficient (\(R^{2}\)) are introduced to judge the accuracy of the surrogate model:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}\quad R ^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum \limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{17}\]  
where \(n\) is the number of nodes in the finite element model; \(y_{i}\) is the simulation value; \(\hat{y}\) is the simulation average value; and \(\hat{y}_{i}\) is the predicted value.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 2 Analysis of Robot Structure and Steering Radius  
### Analysis of Robot Structure  
In this study, a tracked robot with four swinging arms and six tracks, which can perform routine inspection under complex terrain conditions, was used. The robot's body structure is illustrated in Figure 1, based on previous work by this research group. The four swinging arms of the robot could rotate independently and freely. When the robot needs to cross an obstacle, it can complete the obstacle-surmounting tasks by controlling the rotation angle of its four swing arms [17]. When the robot is driving on a horizontal road surface, obstacle avoidance steering can be controlled by the speed difference between the two sides of the robot track. Based on an analysis of the structural parameters and steering radius of the robot, the traction force and resistance torque models of the tracked robot were established [18]. The current study is a continuation of previous studies by the authors focused on the obstacle-surmounting and obstacle avoidance performance of tracked robots. However, this study focuses on establishing the correlation between the steering radius, obstacle position, and track skid and slip of the tracked robot. The robot can autonomously devise its steering strategy by considering the relationship with obstacle positions and LiDAR measurement data, enabling swift and efficient steering.  
When the robot is driving on a hard road, the contact area between the swing arm track and the road is small (Figure 1, area A), and is far smaller than the contact area between the main track and the ground (Figure 1, area B). Therefore, the influence of the swing arm track on the steering performance of the robot can be ignored. However, it is necessary to consider the influence of the swing arm track on steering performance when the robot is driving on a relatively soft road surface, because area A will be much larger, resulting in increased resistance. The main parameters of the tracked robot are shown in Table 1.