esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Presumably, the high SR value is the reason why the viability rate of the bacteria entrapped in this hydrogel was similar to the viability of the free cells. The main differences in the survival level of probiotic bacteria was visible after incubation in pH 1.5; under these conditions, free cells were not able to survive for 2 h, while protected cells could maintain a certain level of viability after 4 h. Moreover, it was found that increasing the hydrogel amount may further improve bacterial survival under acidic conditions. A larger gel amount might provide a thicker barrier between probiotic bacteria and harmful environments, especially for bacteria in the core of hydrogel samples. The obtained results proved the bacterial protective ability of the synthesised \(\gamma\)-PGA-based hydrogels in a solution with low pH. It was established that the hydrogel swelling ratio and amount had noticeable influence on the survival rate of bacteria protected by hydrogels. Products that claim to contribute to the improvement of intestinal microflora should contain a minimum 10\({}^{6}\) CFU/mL of probiotic bacteria [35]. Depending on the consumed food and drinks, gastric-residence time could vary between 15 and 194 min [29]. Probiotic bacteria entrapped in 50 mg of the \(\gamma\)-PGA-PEG1000-3 arm hydrogel maintained a cell population of \(6.53\pm 0.31\) Log CFU/mL after incubation for 2 h in pH = 1.5. Despite visible loss in cell viability for bacteria protected by the hydrogels after 4 h of incubation in pH 1.5, the \(\gamma\)-PGA-based hydrogels could be further investigated to develop probiotic-delivery systems. Synthesised hydrogels demonstrated improved viability of the entrapped probiotic bacteria under acidic conditions in comparison to free cells. If a sufficiently high starting dose of bacteria were introduced into the appropriate \(\gamma\)-PGA-based hydrogel amount, and subjected to low pH, such a delivery system could contain the required amount of viable bacteria. In order to evaluate in vitro cytotoxicity, two cell lines, MSTO and PANC 1, were treated with hydrolys \(\gamma\)-PGA-PEG400 hydrogel. On the basis of the obtained results, it was observed that the presence of hydrolysis products negatively affected the cellular viability of both tested lines. However, percentages of cell viability in the range of 80%-60% were still recognised as weak cytotoxicity [36].
## 2 Background
### About Scrum  
Scrum is recognized as an incremental, iterative framework composed of three roles: the **product owner (PO)**, who oversees the maximizing of the value of the delivery by being transparent about the scope and helping to define the functional needs of the system so that everyone shares the same vision of the product; **the scrum master (SM)**, who has the greatest knowledge of Scrum and helps with its implementation; and the **scrum development team (SDT)**, which develops the work [9].  
Additionally, it has five events: **the first is the sprint**, which is considered a time box with a duration of 1 to 4 weeks in which the product is developed through partial deliveries added together until the completeness of the project is reached. **The second is the sprint planning meeting**, which takes place at the beginning of a sprint and allows the team to be clear about what will be done and how it will be done. **The third event is the daily scrum**, which is an informative meeting that allows them to be aware of the progress and impediments in the execution of the sprint, aiming at transparency and constant communication between all participants, in addition to the control of project risks. **The penultimate event is the sprint review**, a meeting inviting stakeholders to validate the fulfillment of agreed commitments. Its goal is to get early feedback and detect if there are improvements that need to be included as soon as possible to ensure satisfactory delivery. **The sprint retrospective is the last event**. It is a meeting where the team recognizes what they are doing well and what they can improve to create strategies to implement those improvements in the following sprints.  
Finally, Scrum focuses on delivering three artifacts [9]. **The first is the product backlog (PB)**, which aims to group the user stories to allow the SDT to build those functionalities. It is the result expected from the previously mentioned stage of analysis and the fundamental input of the planning. **The second is the sprint backlog (SB)**, obtained by applying estimation and prioritization techniques to the PB to decompose that artifact into minor backlogs. SBs are the commitments agreed upon for each sprint and allow greater control over delivery. **Finally, the increment**, resulting from the construction completed in each sprint, undergoes quality assurance and deployment in the test environment before the fully developed product is ultimately deployed in the production environment. Figure 1 summarizes the above.
# Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System
## 2 Mathematical Model for Back-Scanned Step/Stare Imaging System
Calculating the difference between image points \(\left(h_{x}\right',h_{y}\)\({}^{\prime}\)) and \(\left(h_{x},h_{y}\right)\) in Equation (13),  
\[\left\{ \begin{array}{l}h_{x}\text{${}^{\prime}$}-h_{x}=0\\ h_{y}\text{${}^{\prime}$}-h_{y}=0\end{array} \right. \tag{13}\]  
it can be seen that the image wander caused by the projection distortion during the scanning of the mirror is zero. When the FSM is put in the middle of the optical system, an imaging system is schematically shown in Figure 4, which includes a front-end afocal telescope, a FSM, an imager and an FPA. Back-scanning with a FSM behind the afocal telescope provides an agile method to increase the integration time for the FPA by compensating for movement of the imaging system. As the system scans in object space, the FSM tilts to attempt to keep the image fixed on the FPA during the integration time. In Figure 4, the imager's FOV is scanned through the afocal telescope's FOV. The green box represents the imager's FOV at the earlier point in State A. The blue box represents the imager's FOV at the later point in State B. The black circle represents the afocal telescope's FOV. The gray arrow represents the scanning direction. The corresponding back-scanned imager's FOV in object space are also showed. The afocal telescope's FOV is scanning to the down, while the FSM moves to keep the imager's FOV (the orange box) fixed during the integration time. Assuming that the angular magnification of the afocal telescope is \(A_{\text{mag}}\) and the focal length of the imager is \(f\text{${}^{\prime}$}_{\text{c}}\), in order to meet Equation (14), the following conditions should be satisfied:  
\[\left\{\begin{array}{l}h_{x}=A_{\text{mag}}f_{\text{c}}\text{$  
Modifying Equation (11), we can get the Equation (16):  
\[\left\{ \begin{array}{c}h_{x}=f^{\prime}\tan\!\left[\frac{\tan(\theta_{x})}{ \sqrt{1+\tan^{2}(\theta_{y})}}\right]\\ h_{y}=f^{\prime}\theta_{y}\end{array} \right. \tag{16}\]  
The power series expansion to \(h_{x}\) can be obtained in Equation (17):  
\[h_{x}\sim f^{\prime}\theta_{x}+\left(-\frac{f^{\prime}\theta_{x}}{2}+\frac{f^ {\prime}{\theta_{x}}^{3}}{3}\right){\theta_{y}}^{2}+\frac{f^{\prime}\theta_{x }}{24}{\theta_{y}}^{4}+\ldots \tag{17}\]  
Unlike the conventional field mapping of equation \(h=f^{\prime}\tan\theta\) and \(h=f^{\prime}\theta\), the optimal field mapping according to Equations \(h_{x}=f^{\prime}\theta_{x},h_{y}=f^{\prime}\theta_{y}\) are not rotationally symmetric. It has an an anamorphic nature. This non-rotationally symmetric field mapping deletes all field points wander during back-scanning.
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 4 Experiment Description and Result Analysis
### Kinematic Self-Calibration  
Based on the results of the simulation tests, a real dataset was captured using the back-pack system. The data acquisition site is a corridor of approximate 2.5 m \(\times\) 40 m \(\times\) 2.5 m dimensions (Figure 13a). To test the performance of the kinematic in situ self-calibration of the backpack-based MBL system, the user wore the backpack system and walked along the corridor to acquire point clouds. A schematic drawing of the data acquisition site and trajectory are also provided in Figure 13b. As can be seen, two scan locations were selected from whole trajectory. The first scan was captured near the corner, and the second scan was captured apart from the first scan. The omega angle of each scan was slightly tilted, and the kappa angle was rotated by 180\({}^{\circ}\). Then, planar feature extraction and the matching process were carried out using two scan datasets. Figure 14 describes the planar features commonly seen in both scans, which include six vertical planes and two horizontal planes.  
Figure 11: RMSE of estimated APs for Calibration III.  
Figure 12: RMSE of estimated Aps: (**a**) five runs of Calibration III-5; (**b**) five runs of Calibration III-10.  
A total of eight planes were identified, including walls, doors, a window, the floor, and the ceiling.  
Figure 14: Common planar features seen in Scan #2.  
Figure 13: Real dataset acquisition site and schematic set-up. (**a**) Corridor at the Myongji University (**b**) schematic drawing of test site and trajectory.
## 5 Results and Discussion
### Simulation Results
#### 5.2.2 Comparison of Different Controller Cases Under Different Scenarios
It should be noted that scenarios without any controllers are unstable; thus, no time characteristics were calculated for them. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**No Controllers**} & \multicolumn{2}{c}{**PSSs**} & \multicolumn{2}{c}{**BESS Controllers**} & \multicolumn{2}{c}{**PSSs and BESS Controllers**} \\ \cline{2-9}  & \(\text{OS}\) (\%) & \(T_{s}\) (s) & \(\text{OS}\) (\%) & \(T_{s}\) (s) & \(\text{OS}\) (\%) & \(T_{s}\) (s) & \(\text{OS}\) (\%) & \(T_{s}\) (s) \\ \hline
1 & - & - & 5.1581 & 19.3900 & 6.8738 & 26.0372 & 5.1398 & 12.9783 \\
2 & - & - & 5.3265 & 19.8046 & 8.1256 & 25.0869 & 5.2671 & 13.0508 \\
3 & - & - & 4.7237 & 19.3348 & 6.1518 & 29.9032 & 4.6581 & 13.1429 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of time responses of \(P_{53\text{-}54}\) using the proposed method. Figure 14: Time-domain simulations under different scenarios. **(a)** Responses using the proposed method under Scenario 1. **(b)** Responses using OBTM under Scenario 1. **(c)** Responses using the proposed method under Scenario 2. **(d)** Responses using OBTM under Scenario 2. **(e)** Responses using the proposed method under Scenario 3. **(f)** Responses using OBTM under Scenario 3.
# A 3D Agent-Based Model of Lung Fibrosis
## 2 Materials and Methods
### Hybrid Multi-Agent-Based Model
#### 2.4.2 Proliferation Behaviors  
Since the number of agents is constant throughout a simulation, proliferation behaviors simply translate into updates in the number of cells, which are stored as attributes by the alveolar ducts. The proliferation behaviors thus make use of the internal methods of the agents as an interface to update the number of cells of a specific type.  
* The proliferation of AEC2 is governed by a constant rate that allows for the survival of both the AEC2 and AEC1 populations. To do so, at every time step, the AEC2 population increases by a constant fraction.
* Proliferation of fibroblasts depends on the number of healthy AEC2 in homeostasis and is further increased by damage-associated mediators such as FGF2, TGF\(\beta_{a}\), and IL13. To uncouple the two mechanisms, we implement (i) the F_addition behavior by which the number of fibroblasts is incremented according to a fixed fraction of AEC2 (represented by the parameter \(\lambda_{F,AEC2}\)), and (ii) the F_proliferate that computes the fraction of newborn fibroblasts using the rate in Equation (8). \[\frac{FGF2}{FGF2+K_{fibr,FGF2}}*\left(\frac{IL13}{IL13+K_{fibr,IL13}}+\frac{TGF \beta}{TGF\beta+K_{fibr,TGF\beta}}\right)\] (8)
## 1 Introduction
Using [PERSON] and [PERSON] analyses, the study of [PERSON] and colleagues [17] reported good agreement between BIA (i.e., InBody 720) and DXA (i.e., GE Lunar DYX-iQ 2288) devices for whole-body fat mass and whole-body fat-free mass in men and for whole-body fat mass and segmental fat-free mass in women aged between 18 and 49 years. From these findings, it was concluded that BIA may serve as a suitable alternative of DXA for the (segmental) analysis of body composition. However, due to the mutual variation in results between different BIA and DXA devices, each device should be assessed separately [18; 19]. To date, little research has been carried out on the (dis)agreement of segmental in addition to whole-body composition using BIA and DXA methods. Moreover, and to the best of our knowledge, there are currently no studies available comparing body composition outcome measures from the relatively new InBody S10 BIA device (InBody, Korea) [7] against the Norland Elite DXA scanner (Swissray Medical AG, Switzerland) [13] in young healthy adults. Therefore, the aim of this study was to compare the test outcomes from the InBody S10 BIA device and Norland Elite DXA scanner regarding body composition estimates of the whole-body (i.e., fat%, fat mass, lean mass and bone mineral content) as well as the arm and leg (i.e., fat mass and lean mass) in young healthy male and female adults.
## 4 Dataset and Methodology  
### Dataset  
The dataset used in this research is comprised of well log and seismic partial-stack data from the \"X\" field for model input. Three well logs, labeled A-1, A-2, and A-3, were used in this research. A-1 and A-2 are used to evaluate the model's performance in synthetic seismic analysis, whereas A-3 is utilized to validate the model's performance in real seismic analysis. This research makes use of partial stacks of seismic data. The partial stack is divided into near (05-15 degrees), mid (15-25 degrees), and far stacks (25-40 degrees) and covers an area of up to 185,000,000 m\({}^{2}\). All datasets are obtained from Centre for Subsurface Imaging (CSI) Universiti Teknologi PETRONAS internal report.
## Materials and Methods
### Experiment: Datasets and Location
In fact, despite both used receivers (Trimble NetR9 and Topcon Hiper HR) acquiring data from GPS, GLONASS, and Galileo constellations, their antennas are different. The CORS in Palermo is equipped with a Zephyr GNSS Geodetic II antenna (TRM55971.00 TZGD), also used in the IGS (International GNSS Service) network and calibrated by NGS (National Geodetic Survey), while the Topcon receiver is an integrated antenna (micro centered Fence Antenna(tm) with Ground Plane) [38; 39].
## 2 Principles and Algorithms  
### Deterministic Interpolation Method  
Inverse distance weighting (IDW) [41] interpolates values based on spatial proximity. It operates on the principle that closer distances exert greater influence on the interpolated result. The interpolation formula is:  
\[Z(x)=\frac{\sum_{i=1}^{N}\frac{Z(X_{i})}{d(x,x_{i})^{p}}}{\sum_{i=1}^{N}\frac {1}{d(x,x_{i})^{p}}} \tag{1}\]  
where \(Z(x)\) represents the predicted value for the insertion point, \(Z(x_{i})\) denotes the known value of point \(x_{i}\), \(d(x,x_{i})\) expresses the Euclidean distance between the insertion point \(x\) and the known point \(x_{i}\), \(p\) indicates a power parameter of distance, usually 2, and the degree of attenuation of the control distance, and \(N\) refers to the number of known points involved in the interpolation.  
RBF (radial basis function) interpolation is an interpolation method based on radial basis function, which produces smooth surface by fitting the known point position and attribute value. The basic formula for RBF is:  
\[Z(x)=\sum_{i=1}^{N}\lambda_{i}\cdot\varphi(d(x,x_{i})) \tag{2}\]  
where \(Z(x)\) represents the predicted value of \(x\) to be interpolated, \(\lambda_{i}\) denotes the weight coefficient, determined by the location and value of known points, \(\varphi\) expresses the radial basis function, the common radial basis functions include thin plate spline, regular spline, higher-order spline et al., and \(d(x,x_{i})\) refers to the distance between the interpolation point \(x\) and the known points \(x_{i}\).  
Common radial basis functions are:  
(1) Thin plate spline function (TPS):  
\[\varphi(r)=r^{2}\ln(r) \tag{3}\]  
(2) Regular spline function (RS):  
\[\varphi(r)=r^{p} \tag{4}\]  
(3) Higher-order spline function (HS):  
\[\varphi(r)=r^{2k-1},\ k\in N,\ k\geq 2 \tag{5}\]  
where \(r\) represents the Euclidean distance between a point to be inserted and a known point, \(\ln(r)\) denotes the natural logarithm; \(p>0\) expresses the parameter that controls the order of polynomials, and \(k\) indicates a non-negative integer that determines the order of functions.
# Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission
## 6 Conclusions  
In this paper, we have investigated an energy-efficient resource and power allocation algorithm for multicast D2D communication underlying a cellular network. The goal of this paper is to maximize the overall energy efficiency of D2D multicast groups through appropriate resource allocation and a power control scheme, while maintaining the SINR requirements of both CUs and D2D clusters. A heuristic resource and power control algorithm is then proposed to solve the above problem with less complexity. It is shown by simulation that the energy efficiency of D2D multicast clusters with the proposed scheme can be improved significantly compared with conventional resource allocation schemes. In future, we plan to extend this work to the multiple multicast problem which means that several multicast clusters are allowed to reuse the same RB of a CU to further improve the spectrum efficiency.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (No. 61501371), National Science and Technology Major Project of the Ministry of Science and Technology of China(project number: 2017 ZX03001012-005). The international Exchange and Cooperation Projects of Shaanxi Province (project number: 2016 KW-046).  
**Author Contributions:** [PERSON] conceived and wrote the paper, [PERSON] and [PERSON] designed and conducted comprehensive computer simulations, [PERSON] helped [PERSON] to conduct the theory analysis.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Calculations  
The normalized vegetation index (NDVI), which serves as a representation of the canopy structure, is computed as follows:  
\[\text{NDVI}=\frac{\rho_{\text{nir}}-\rho_{\text{red}}}{\rho_{\text{nir}}+\rho_ {\text{red}}} \tag{5}\]  
where \(\rho_{\text{nir}}\) and \(\rho_{\text{red}}\) represent canopy reflectance within 758-762 nm and 685-689 nm, respectively.  
The observed SIF above the canopy (SIF) can be expressed as the product of photosynthetically active radiation (PAR), the fraction of PAR absorbed by chlorophyll in vegetation (\(f_{\text{PAR}}\)), the fluorescence efficiency (\(\Phi\)F), and the canopy escape ratio (\(f_{\text{esc}}\)):  
\[\text{SIF}=\text{PAR}\times f_{\text{PAR}}\times\Phi\text{F}\times f_{esc} \tag{6}\]  
The canopy escape ratio (\(f_{\text{esc}}\)) describes the fraction of SIF that is not reabsorbed or scattered by the leaves in the canopy.  
The methods for the calculation of \(f_{esc}\) for SIF in the different bands are given below.  
For the red SIF [38]:  
\[f_{esc_{red}}\approx\frac{\text{REDv}}{f_{PAR}} \tag{7}\]  
For the far-red SIF [39]:  
\[f_{esc_{far-red}}\approx\frac{\text{NIRv}}{f_{\text{PAR}}} \tag{8}\]  
Here, REDv and NIRv represent the vegetation red-band reflectance and vegetation near-infrared-band reflectance, respectively, which can be calculated as follows:  
\[\text{REDv}\approx\text{NDVI}^{2}\times\rho_{red} \tag{9}\]  
\[\text{NIRv}\approx\text{NDVI}\times\rho_{nir} \tag{10}\]  
The canopy red SIF (SIF\({}_{687}\)) can also be expressed as follows:  
\[\text{SIF}_{687}\approx\text{PAR}\times\text{REDv}\times\Phi\text{F}_{687} \tag{11}\]  
The canopy far-red SIF (SIF\({}_{760}\)) can also be expressed as follows:  
\[\text{SIF}_{760}\approx\text{PAR}\times\text{NIRv}\times\Phi\text{F}_{760} \tag{12}\]  
The fluorescence efficiencies (\(\Phi\)F) are described as follows:  
\[\Phi\text{F}_{687}\approx\frac{\text{SIF}_{687}}{\text{PAR}\times\text{ REDv}} \tag{13}\]\[\Phi\text{F}_{760}\approx\frac{\text{SIF}_{760}}{\text{PAR}\times\text{NIRv}} \tag{14}\]  
According to [PERSON]'s light use efficiency model [40], GPP can be described as follows:  
\[\text{GPP}=\text{PAR}\times f_{\text{PAR}}\times\text{LUE} \tag{15}\]  
where LUE represents the light use efficiency of photosynthesis.
## 4 Experimental and Theoretical Results
### Determination of Rubber Support Stiffness
The solution of Equation (3) can be written as  
\[\overline{w}(x)=\overline{F}G_{\mathrm{F}}^{w}(x,x_{F})-2\overline{k}[ \overline{w}_{1}G_{\mathrm{F}}^{w}(x,l_{1})+\overline{w}_{2}G_{\mathrm{F}}^{w }(x,l_{2})], \tag{4}\]  
where  
\[G_{\mathrm{F}}^{w}(x,\xi)=\left(1-\frac{\omega^{2}\rho I}{GS\kappa}\right)G^{ w}(x,\xi)-\frac{EI}{GS\kappa}\frac{\partial^{2}G^{w}(x,\xi)}{\partial\xi^{2}}\]  
\(G^{w}(x,\xi)\) is [PERSON]'s function associated with the free-free Timoshenko beam. Taking \(x=l_{1,2}\), it obtains  
\[ \begin{array}{l}\left(1+2\overline{k}G_{\mathrm{F}}^{w}(l_{1},l_{1}) \right)\overline{w}_{1}+2\overline{k}G_{\mathrm{F}}^{w}(l_{1},l_{2})\overline{ w}_{2}=\overline{F}G_{\mathrm{F}}^{w}(l_{1},x_{F}),\\ 2\overline{k}G_{\mathrm{F}}^{w}(l_{2},l_{1})\overline{w}_{1}+\left(1+2\overline {k}G_{\mathrm{F}}^{w}(l_{2},l_{2})\right)\overline{w}_{2}=\overline{F}G_{ \mathrm{F}}^{w}(l_{2},x_{F}).\end{array}  \tag{5}\]  
Figure 14: Rail coupon model on rubber supports. Considering \(x_{F}=L/2\) si \(l_{2}=L-l_{1}\), it follows for the reason of symmetry  
\[ \begin{array}{l}G_{F}^{w}(l_{1},L/2)=G_{F}^{w}(l_{2},L/2),\\ G_{F}^{w}(l_{1},l_{2})=G_{F}^{w}(l_{2},l_{1}),\\ G_{F}^{w}(l_{1},l_{1})=G_{F}^{w}(l_{2},l_{2}).\end{array}  \tag{6}\]  
Rail displacement above the rubber supports results  
\[\overline{w}_{1,2}=\frac{\overline{F}\cdot G_{F}^{w}(l_{1},L/2)}{1+2\overline {k}\big{[}G_{F}^{w}(l_{1},l_{1})+G_{F}^{w}(l_{1},l_{2})\big{]}} \tag{7}\]  
and the corresponding accelerance  
\[\overline{a}_{1,2}=-\frac{\omega^{2}\overline{w}_{1,2}}{\overline{F}}=-\omega ^{2}\frac{G_{F}^{w}(l_{1},L/2)}{1+2\overline{k}\big{[}G_{F}^{w}(l_{1},l_{1})+G _{F}^{w}(l_{1},l_{2})\big{]}} \tag{8}\]  
Figure 15 shows the accelerance of the rail coupon in the rubber supports section for the following model parameter values: \(E\) = 210 GPa, \(G\) = 81 GPa, \(\kappa\) = 0.4, \(\rho\) = 7850 kg/m\({}^{3}\), \(m\) = 49.4 kg/m, \(S\) = 62.93 \(\times\) 10\({}^{-4}\) m\({}^{2}\), \(I\) = 1816 \(\times\) 10\({}^{-8}\) m\({}^{4}\), \(L\) = 0.82 m, \(l_{1}\) = 0.184 m, \(l_{2}\) = 0.636 m and \(x_{F}\) = 0.41 m; loss factor of rail coupon material (steel) \(\eta_{\text{steel}}\) = 0.17%. With these values, the peak due to the first bending mode of the rail coupon has the frequency 1198 Hz and the value 0.5994 m/Ns\({}^{2}\). As regards the stiffness and loss factor of the rubber supports, these have been determined by tapping so that the peak corresponding to the rigid mode of vibration is close to that obtained experimentally. Taking the stiffness of the rubber support \(k\) = 1.329 MN/m and the loss factor \(\eta\) = 0.1, the rigid mode peak has the frequency 58 Hz and the value 0.2481 m/Ns\({}^{2}\). Given the proximity between the experimental and theoretical results, the above value of the rubber support stiffness shall be retained for insertion into the finite element model of the experimental scale demonstrator of the carbody.