esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell-Laden Thermosensitive Chitosan Hydrogel Bioinks for 3D Bioprinting Applications
## 2 Materials and Methods  
### Preparation of the Chitosan Hydrogel  
The chitosan hydrogel solution was prepared as described before [17; 18; 19]. A 1.67% (_w/v_) chitosan (CS, 95% or greater degree of deacetylation, YB Bio, Yeongdeok, Korea) solution was prepared by stirring powdered chitosan in 0.1 M aqueous acetic acid at room temperature overnight. The insoluble particles in the chitosan solution were then filtered. The 56% (_w/v_) \(\beta\)-glycerophosphate (\(\beta\)-GP, Sigma-Aldrich, St. Louis, MO, USA), 8% Potassium phosphate (K\({}_{2}\)HPO\({}_{4}\), Duksan, Ansan, Korea) and 3.5% Sodium bicarbonate (NaHCO\({}_{3}\), Duksan, Ansan, Korea) solution was prepared in an \(\alpha\)-minimum essential medium (\(\alpha\)-MEM, Welgene Inc., Gyeongsan, Korea) for use as a gelling agent, sterilized using syringe filters with 0.2 mm pore size and stored at 4 \({}^{\circ}\)C. The chitosan hydrogel solution was prepared by adding the gelling agent solution to the chitosan solution at a 9:1 ratio, followed by pH neutralization using 1 M of the NaOH solution (Duksan, Ansan, Korea). In order to avoid precipitation of the chitosan, a NaOH solution was gradually added until the pH of the chitosan solution reached above 6.5.  
Figure 1: Characteristics of the chitosan hydrogels. (**A**) Schematic representation of the 3D bioprinter for 3D printing and gelation using CBIs. (**B**) Representative images of the chitosan solution and hydrogel in the presence of different solvents. (**C**) Gelation times of the chitosan hydrogel solution in different solvents. The test tube inversion method was used to determine the gelation time. (**D**) Cell viability of PDLSCs via indirect culture estimated using the WST assay. Error bars indicate standard deviation (n = 5 for each group). * represents a statistically significant difference compared to the control group (_p_ < 0.05). CBIs, chitosan bioinks; PDLSCs, periodontal ligament stem cells; WST, water soluble tetrazolium salt.
## 3 Results and Discussion
### Sutter Mean Diameter  
Figure 20 illustrates the comparison of SMD for circular and elliptical spray patterns. As illustrated in the figure, SMD initially exhibited a pronounced decline, followed by a period of stabilization after spraying from the nozzles. The reason for this was that when diesel fuel was simply sprayed from the nozzle, the droplet diameter was larger. However, as the spray process occurred, the diesel fuel droplets entered a high-speed state, resulting in their rapid fragmentation into smaller droplets. This significantly reduced SMD. When the speed of the diesel fuel droplets was reduced to a certain degree, the droplets underwent fragmentation and polymerization until they reached a dynamic equilibrium. This led to a stabilization of SMD. At ASOI of 1.2 ms, the SMD of both the circular nozzle and the elliptical nozzle with a smaller aspect ratio reached about 6.3 \(\upmu\)m, and SMD of the elliptical nozzle with a larger aspect ratio reached 6.1 \(\upmu\)m. The SMD of elliptical nozzles was stabilized in a shorter period compared to that of the circular nozzle, indicating that the elliptical nozzles had faster spray fragmentation, and SMD of elliptical nozzles with larger aspect ratio was smaller, indicating that their atomized particles were smaller and the atomization effect was better. The underlying cause of this phenomenon could be attributed to the distinct curvature of the major and minor axes of the elliptical nozzle, which subjected the gas-liquid interface of the spray to asymmetric shear forces. The shear effect exerted by the surrounding gas was more pronounced, rendering the spray more susceptible to instability and turbulence. This, in turn, accelerated the fragmentation of droplets, while the instability of liquid surfaces further exacerbated the fragmentation of the liquid-phase core and atomization. Figure 21 illustrates the Weber number of some particles in the spray at ASOI of 0.1 ms, with longer arrows indicating larger Weber numbers. From the figure, elliptical nozzles exhibited a higher Weber number during the injection process, a phenomenon that could be attributed to their geometric asymmetry. At elevated Weber numbers, the fluid inertial forces exceeded the surface tension forces, thereby facilitating droplet atomization. An elliptical nozzle with a larger aspect ratio had greater asymmetry, so the spray broke up faster and the particles were smaller.  
Figure 19: SA of circular and elliptical nozzles for the same STP: (**a**) SA in the XZ plane; (**b**) SA in the YZ plane.  
In comparison to a circular nozzle, elliptical nozzles had a more pronounced impact on the spray surfaces, leading to a notable increase in aerodynamic drag during the spraying process. This resulted in a reduction in STP and an elevation in the SCA. This phenomenon enabled the air to be drawn in more effectively, facilitating the contact between fuel and air, and consequently enhancing the mixing of fuel and air.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.2 Mobility  
The choice of traction type is a fundamental aspect in the design of a fruit harvesting robot, as it directly influences its ability to move efficiently through different types of agricultural land. On firm, flat terrain, the wheels offer greater speed and maneuverability compared to other alternatives. This allows the robot to move quickly between rows of plants, increasing harvesting efficiency [57].  
Wheeled systems are typically more energy-efficient, which can extend battery life and increase the robot's operating time before needing a recharge. They are generally simpler and easier to maintain, which can reduce maintenance costs and downtime [58].  
The maximum speed of a fruit-harvesting robot must balance operational efficiency with the delicacy necessary not to damage plants or fruits. A controlled speed, typically around 1.5 km/h, allows the robot to move fast enough to maintain a high picking rate, but slow enough to ensure that each fruit is properly identified and harvested without causing damage [59].  
A fruit-harvesting robot's ability to operate in fields with moderate slopes is crucial to its versatility and usefulness in various agricultural environments. A slope capability of up to 20 degrees allows the robot to maintain its efficiency in terrain with varied topography. Many agricultural fields are not perfectly flat and can have hills and slopes. The robot's ability to operate in these terrains ensures that it can be used in a wide range of locations and conditions [60].
###### Abstract  
This study attempted to determine the dimensional stability of maxillary and mandibular edentulous denture bases constructed using three-dimensional (3D) printing systems based on stereolithography and digital light processing according to the postcuring treatment time and the removal time of the support structure. Three-dimensional printing of the designed denture base file was performed using two types of 3D printing photocurable resin (standard gray resin (Formlabs) (Somerville, MA, USA) and MAZIC D resin (Vericom) (Anyang, Korea)) and their compatible 3D printers (Form3 (Formlabs) and Phrozen Shuffle (Phrozen) (Hsinchu City, Taiwan)). Different postcuring times (no postcuring, and 15, 30, 45, and 60 min) and times of removal of the support structure were set for each group. Data relating to the denture bases in all groups were obtained using 3D scanning with a tabletop scanner after postcuring. All acquired data were exported to 3D analysis software, and the dimensional changes during postcuring of the denture base were analyzed using RMSE (root-mean-square error) values. It could be confirmed that the dimensional changes increased with postcuring time, and the accuracy was higher in the maxilla than in the mandible. The accuracy was highest for the group in which the postcuring process was performed while the support structure was present.  
3D printing; dimensional stability; support structure; postcuring +
Footnote †: journal: Applied Sciences  
**Evaluation of Dimensional Changes during Postcuring of a Three-Dimensionally Printed Deture Base According to the Curing Time and the Time of Removal of the Support Structure: An In Vitro Study**  
[PERSON]  
1 Department of Advanced General Dentistry, Dankook University College of Dentistry, Dandae-ro 119, Dongnam-gu, Cheonan 31116, Korea; [EMAIL_ADDRESS]  
2 Department of Prostodontics, College of Dentistry, Yonsei University, Yonsei-ro 50-1, Soodaemun-gu, Seoul 03722, Korea; [EMAIL_ADDRESS]  
3 Department of Prostodontics, Oral Research Science Center, BK21 PLUS Project, Yonsei University College of Dentistry, Yonsei-ro 50-1, Soodaemun-gu, Seoul 03722, Korea; [EMAIL_ADDRESS] (N.-E.N.); [EMAIL_ADDRESS] (S.-H.S.); [EMAIL_ADDRESS] (J.-H.L.)  
* Correspondence: [[EMAIL_ADDRESS]; Tel.: +82-2:2228-3157  
+ These authors contributed equally to this work as first authors.
# Design, Analysis, and Experiment of a Wheel-Legged Mobile Robot
## 2 Structure Design
### Statics Modeling
\begin{array}{l}f_{1}\text{cos}\,\delta_{1}-N_{1}\text{sin}\,\delta _{1}+f_{4}\text{cos}\,\delta_{4}-N_{4}\text{sin}\,\delta_{4}{=}\,0\\ \frac{1}{2}G-f_{1}\text{sin}\,\delta_{1}-N_{1}\text{cos}\,\delta_{1}-f_{4}\text {sin}\,\delta_{4}-N_{4}\text{cos}\,\delta_{4}{=}\,0\,,\\ M_{\mathcal{O}(N_{4})}-M_{\mathcal{O}(f_{4})}-M_{\mathcal{O}(N_{1})}-M_{\mathcal{ O}(f_{1})}=0\end{array} \right. \tag{3}\]  
Figure 1: Force analysis of single wheel-leg climbing. Formula (4) can be obtained from the force balance of the robot:  
\[\left\{ \begin{array}{c}{{P_{1}}^{\prime}-{P_{4}}^{\prime}}{=0}\\ \frac{1}{2}G-{F_{1}}^{\prime}-{F_{4}}^{\prime}{=0}\\ {{T_{m1}}^{\prime}+{T_{m4}}^{\prime}+{P_{1}}^{\prime}{l_{1}}{\sin\gamma}+{P_{4 }}^{\prime}{l_{2}}{\sin\gamma}+{F_{1}}^{\prime}{l_{1}}{\cos\gamma}-{F_{4}}^{ \prime}{l_{2}}{\cos\gamma}{=0}}\end{array} \right., \tag{4}\]  
Formula (5) can be obtained from the force balance of the robot's front wheel-leg:  
\[\left\{ \begin{array}{c}{{P_{1}}+{f_{1}}{\cos\delta_{1}}-{N_{1}}{\sin\delta_{ 1}}{=0}}\\ {{F_{1}}-{f_{1}}{\sin\delta_{1}}-{N_{1}}{\cos\delta_{1}}{=0}}\\ {{T_{m1}}-{M_{{O_{1}}({N_{1}})}}-{M_{{O_{1}}({f_{1}})}}{=0}}\end{array} \right., \tag{5}\]  
The formula for the force balance of the robot's rear wheel-leg is similar to the one above. In general conditions, \(\delta_{4}=0\) during the climbing process of the robot. According to Formulas (3)-(5), the maximum driving force that the driving wheel-leg could provide for the robot body is obtained as:  
\[{P_{4}}_{\max}=\frac{\mu}{({l_{1}}+{l_{2}})(1-\mu\tan\gamma)}\left(\frac{{{Gl_ {1}}}}{2}+\frac{{{T_{m1}}+{T_{m4}}}}{{\cos\gamma}}\right), \tag{6}\]  
Then, the maximum of \(\delta_{1}\) is derived as:  
\[\delta_{1}{\max}=\arctan\left(\frac{{\mu^{2}G}}{{\mu G-2(1{+}{\mu^{2}}){P_{4} }_{\max}}}\right), \tag{7}\]  
In other words, when the robot is driven with the rear wheel-leg and \(\delta_{4}=0\), the robot is capable of climbing a step if the angle between the front wheel-leg and the step meets the condition \(\delta_{1}\leq\delta_{1}{\max}\). Figure 2: Force analysis of robot climbing steps. (**a**) Force analysis of the robot; (**b**) force analysis after equivalence to the geometric center; (**c**) force analysis of the front wheel-leg; and (**d**) force analysis of the rear wheel-leg. Similarly, when the robot is driven with the front wheel-leg and \(\delta_{1}=0\), the following calculations are derived:  
\[\left\{ \begin{aligned} P_{1\text{max}}&=\frac{\mu}{(1_{1}+1 _{2})(1-\mu\tan\gamma)}\left(\frac{C\delta_{1}}{2}-\frac{T_{\text{mt}}+T_{\text {mt}}}{\cos\gamma}\right)\\ &\delta_{4\text{max}}=\arctan\left(\frac{\mu^{2}G}{\mu G-2(1+\mu^ {2})P_{1\text{max}}}\right)\end{aligned} \right., \tag{8}\]  
According to the above analysis, we attain the values of \(\delta_{1\text{max}}\) and \(\delta_{4\text{max}}\) with the value of the mass and center-of-mass coordinate of the robot, friction coefficient \(\mu\), step size, and other parameters of the robot.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 8 The Optimized ZVDD Input Shaper
### Example-2, Non-Rest-to-Rest Example  
For the non-rest-to-rest example problem, the result obtained using the proposed method is presented in Figure 12.  
In this example, two bounds are defined as follows:  
1. Impulse amplitude constraints: \(-1<(A_{1},A_{2},A_{3})\leq 1\).
2. Time increment constraints: \(0.1<(\epsilon_{1},\epsilon_{2},\epsilon_{3})\leq 12\).  
In Figure 12, all four elements of the state vector reach zero at the end of the rotational maneuver. Table 4 summarizes the results obtained after using different methods.  
Since, for the non-rest-to-rest example, vibration reduction is not a priority but rather the goal is to reach the final \"rest\"state regardless of the initial conditions (and the minimum number of switches required), we do not compare the optimized ZVDD solution with the minimum-time GPOPS-II solution.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 3 Pattern-Reconfigurable Antennas  
Pattern reconfiguration is an interesting characteristic of antennas and is often used of changing the coverage from one area of interest to another one, switched operation between two or more wireless nodes, determining direction of arrival, increasing or decreasing the coverage area of a wireless node, or more generally, to adapt the performance of a wireless node in response to varying conditions in surrounding environment. Thus, our discussion of pattern reconfiguration includes antennas that can reconfigure the radiation pattern to two (a special case often also referred to as \"switchable antennas\") or more states. In the presented flexible pattern reconfigurable antennas, the reconfigurability was achieved by using an electrical switch by changing the ground or radiator shapes [80; 81; 82; 83; 84], switching between two types of the antenna combined in a main structure [85], shorting the radiator to the ground plane [86; 87], switching between feeding ports [88] or physical reconfiguration by changing the antenna's shape or type [89; 90; 91; 92; 93; 94; 95].
## 2 System Description and Problem Formulation
### System Description
#### 2.1.2 VESS System  
The ESS is constructed in two parts: a power subsystem (PS) and an energy subsystem (ES). The PS capacity limits instantaneous charging and discharging power. The ES stores the energy, and its capacity determines the ESS service time. The ESS operation is performed within these two constraint regions in the individual ESS [21]. However, the VESS is operated by logically dividing one physical ESS over several units. Therefore, the VESS operation region is limited not only by the ESS capacity of the PS and the ES, but also by the operation of each unit.  
Let \(a_{t}^{u}\) be the ESS charging or discharging action for the \(u\) th WPG at decision time \(t\). The action is first restricted by the PS capacity \(C_{PS}\)  
\[-C_{PS}\leq a_{t}^{u}\leq C_{PS},\ \forall\ t\in\mathcal{T}. \tag{3}\]  
Figure 1: Wind power generation system model with a virtual energy storage system; VESS: virtual energy storage system, and RL: reinforcement learning.  
Moreover, in VESS, the action is limited by the actions of other WPGs. Including this constraint, the action range in Equation (3) is modified as  
\[-C_{PS}-\sum_{i\in\mathcal{U}/|u|}a_{t}^{i}\leq a_{t}^{u}\leq C_{PS}-\sum_{i\in \mathcal{U}/|u|}a_{t}^{i},t\in\mathcal{T}. \tag{4}\]  
Considering the charging/discharging efficiency \(\eta\), the actual operation quantity \(q_{t}^{u}\) in Equation (2) is measured as  
\[q_{t}^{u}=\left\{\begin{array}{ll}\eta\,a_{t}^{u},&\text{if }a_{t}^{u}\leq 0, \\ a_{t}^{u}/\eta,&\text{if }a_{t}^{u}>0.\end{array}\right. \tag{5}\]  
Second, the action is performed in the \"energy stored\" range, referred to as the state-of-charge (SoC). The SoC at the decision time \(t\), \(s_{t}\), is measured to be  
\[s_{t}=s_{t-1}+\sum_{u\in\mathcal{U}}q_{t}^{u}\Delta T, \tag{6}\]  
where \(\Delta T\) is the operation time interval, which is limited by the ES capacity \(C_{ES}\),  
\[C_{ES}^{\min}\leq s_{t}\leq C_{ES}^{\max},\ \forall\ t\in\mathcal{T}. \tag{7}\]  
Herein, \(C_{ES}^{\min}\) and \(C_{ES}^{\max}\) express the minimum and maximum operable ES capacity, respectively. These values are determined by considering ESS characteristics, such as the depth of discharge (DoD).  
Note that power system requirements such as line flow and frequency should be considered for implementation into practical systems. However, this study focuses on the forecasting error management aspect on the WPG; hence, a simple model used in [23; 24] is considered herein. The VESS operation considering power system requirement is an open problem.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Prototypes Studies
#### 2.3.2 Prototype Based on the Millimeter-Wave Frequency Band  
As millimeter-wave is regarded as a key technology in the context of 6G, the photonic-based ISAC systems have received widespread attention due to their ability to generate high-frequency broadband signals of millimeter wavelengths using the optical devices [32; 33].  
In [34], the system consists of an ISAC BS and sensing targets. By using the universal OFDM waveform in the millimeter-wave frequency band, with a center frequency of 28 GHz and a bandwidth of 400 MHz, the BS can sense the environment in real-time with high resolution. The study focuses on the impact of uneven cyclic prefixes on various sensing algorithms for Doppler estimation, and three methods are investigated to resolve this issue.  
In [35], the baseband signal is generated through the digital-to-analog converter module and intermediate frequency module from the millimeter-wave platform. It is upconverted by the 28 GHz RF head to reach the phased array antenna, where the receiving antenna at the target can receive the communication data of the integrated signal. At the same time, the receiving end of the integrated system demodulates the reflected echo of the sensing-integrated signal, enabling synchronous updating of target position information. The test results confirm that the proposed algorithm can achieve acceptable target detection performance within a 10 m range with an average position error of 0.189 m, as well as a stable data rate of 2.86 Gbps, in the 28 GHz millimeter-wave frequency band.
## 4 Conclusions and Future Work  
To address the problem of existing sampling-based planners having low exploration efficiency and poor environmental adaptability and the increasingly sophisticated level of intelligence requirements in the Industry 4.0 era not being met, this study proposed a path planning algorithm that was based on the architecture of the RRT algorithm and suited complex environments. The algorithm included a method of gradually changing the sampling area and a node control mechanism, which were used to guide the random tree exploration and reduce the expansion of invalid nodes, respectively. Furthermore, the node control mechanism could extract boundary nodes to improve the environmental adaptability. The algorithm was tested in three scenarios in two-dimensional space and was applied to a 6-DOF manipulator. The results revealed that the algorithm was effective and universal. It could significantly improve the planning efficiency and had a stronger applicability to complex environments, particularly narrow environments, compared with the traditional RRT algorithm.  
However, the proposed method had certain limitations. The selection of the parameters \(k\) and \(c\) in the algorithm was a problem. Although the algorithm was universal, these parameters sometimes needed to be adjusted appropriately for different environments in order to obtain the best results. In addition, the path quality needed to be improved. This issue will be further researched by attempting an adaptive adjustment of parameters and considering the introduction of curvature constraints and other kinematic and dynamic constraints to improve the performance of the algorithm.  
Conceptualization, X.W. and X.L.; methodology, X.W.; software, Y.C.; validation, G.L. and K.Z.; formal analysis, X.L.; investigation, G.L. and K.Z.; data curation, X.L.; writing, original draft preparation, X.W.; writing, review and editing, X.W.; visualization, Y.C.; supervision, B.H.; project administration, B.H. All authors read and agreed to the published version of the manuscript.  
This research was funded by National Key R & D Program of China (2016 YFC0803000, 2016 YFC0803005).  
Thanks to Professor [PERSON] for providing some suggestions to improve this manuscript. Additionally, thanks to [PERSON], [PERSON] and [PERSON] for the language help.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Basic-RRT** & **CSA-RRT** & **NC-RRT** \\ \hline Average computational time (s) & 3.658 & 1.541 & 1.469 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average computational time for each algorithm applied to the 6-DOF manipulator (10% goal bias, 100% success rate).  
Figure 8: Simulation of a 6-DOF manipulator. The three algorithms are separately applied to the manipulator to allow it to pass through a tunnel and reach the target configuration.
## 3 Experimental Methodology
### Feature Extraction Using Fast CWT Scalograms
#### 3.2.1 Feature Representation  
Feature computation is carried out in MATLAB, exploiting functionalities provided in the Audio System and Data Communications toolboxes. A total of 20 filter bank channels with 12 cepstral coefficients are used for the cepstral feature extraction, as per the standard after DCT application [54]. An FFT size of 1024 is utilized, while the lower and upper filter bank frequency limits are set to 300 Hz and 3700 Hz. This frequency range includes the main components of speech signals (specifically, narrowband speech), while filtering out the humming sounds from the alternating current power, as well as high frequency noise [55]. Further, this range is relevant to the sound classes of speech and stream, and was found to also include the main components of the other classes. While larger frequency ranges could also be considered, this would require much larger FFT sizes to maintain the same frequency resolution, which in turn would increase the computational requirements. The extraction of the feature vectors is carried out by computing the average of the four time-aligned channels in the time domain, \(y_{avg}(t)\). The coefficients are then extracted accordingly, from which single feature matrices are generated. The feature images are resized into 227 \(\times\) 227 matrices using a bi-cubic interpolation algorithm with antialiasing [56], in order to match the input dimensionality of the AlexNet neural network model. Figure 4 shows samples of feature images for each of the three features compared, using the 'Speech' and 'Kitchen sound' classes.