esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Epithelial Homeostasis and Perturbation in Three-Dimensional Human Esophageal Organoids
## 3 Results
### Exogenous EGF Is Dispensable for Normal Human Esophageal Organoid Growth in ADF-Based Medium
Figure 3: EPC1 and EPC2 organoid formation is suppressed in the presence of AG-1478, an EGFR inhibitor (100 nM), or in the absence of either A83-01, an inhibitor for TGF\(\beta\) receptor-mediated signaling or Y-27632, an inhibitor for ROCK-mediated signaling. Note that HOME0 contains 5 \(\upmu\)M A83-01 and 10 \(\upmu\)M Y-27632. (**A**), EPC1; (**B**), EPC2. *, \(p<0.05\) vs. HOME0, n = 3 in (**A**,**B**). One-way ANOVA was used for the overall comparison. Figure 2: EPC1 and EPC2 organoids were grown in KSFMC, HOME, or ADF3+, containing recombinant human EGF at indicated final concentrations and subjected to H&E staining. Structures grown in KSFMC, HOME0, or HOME0.1 displayed concentric layers of spinous-like cells with a squamous cell differentiation gradient with a keratinized material to a partial (HOME0 or HOME0.1) or a total extent (KSFMC). In HOME50 and ADF3+, structures tended to display small clusters of cells (<32 cells in a cross-sectional plane) often with debris, largely involutional or atrophic. Atypical basaloid cells (mild to moderate) were noted in structures grown in HOME1, HOME50, and ADF3+. Scale bar, 100 \(\upmu\)m.  
Inhibition of TGF\(\beta\) Receptor-Mediated Signaling or Rho-Associated Kinase (ROCK) Increases Organoid Formation Efficiency  
In monolayer culture, TGF\(\beta\) induces cell-cycle arrest to trigger senescence in EPC1 and EPC2 cells [31]. A83-01, a potent inhibitor of TGF\(\beta\) receptor I, is a common component of ADF-based organoid media. However, A83-01 is not required for normal or neoplastic murine esophageal organoids to grow in the ADF-based murine organoid medium (MOM) (Table 3) [20]. We next compared HOME0 containing A83-01 and HOME0 devoid of A83-01 (HOME0\({}^{\mathrm{AA}}\)). When EPC1 and EPC2 cells were seeded in Matrigel and fed with HOME0 and HOME0\({}^{\mathrm{AA}}\), OFR was found to be significantly better in the presence of A83-01 (Figure 3A,B), indicating that A83-01 is beneficial for normal human esophageal 3D organoid culture with the ADF-based medium. ROCK inhibitor Y-27632 is another common ingredient found in ADF-based organoid culture media. The Rho GTPases-ROCK pathway has functional interplays with downstream effectors SMAD2/3 to augment TGF\(\beta\) receptor-mediated signaling [41; 42]. When ROCK was inhibited by Y-27632 in HOME0, both EPC1 and EPC2 cells exhibited an improved organoid formation rate (Figure 3A,B), confirming the beneficial effect of Y-27632 compound in human esophageal organoid culture utilizing the ADF-based medium.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## Materials and Methods
### Pressure Transmission Test  
The pressure transmission technology provides a scientific and effective method to evaluate the plugging performance of different fluids. A shale hydra-mechanics coupling simulation test device developed by China University of Petroleum (East China) was used for the pressure transmission experiment [43]. In this test, the shale samples were cut into cylindrical cores (diameter: 2.54 cm; length: 0.75 cm). Base fluid for the test was prepared by dispersing 4 g sodium bentonite into 100 mL distilled water. Firstly, the shale core was placed into the core holder, and the axial and confining pressure were set at 5 MPa during the experiment. Then, the test fluids were pumped into the upstream entrance to contact with the shale core, while the upstream pressure was maintained at 2 MPa and the downstream's initial pressure was set at 1 MPa. The pressure transmission curve was recorded through monitoring the downstream pressure change. Furthermore, the permeability of shale cores before and after being plugged with SMA/SiO\({}_{2}\) could be calculated by the Equation (1) [44].  
\[K=\frac{\mu\beta VL}{A}\frac{\ln\left(\frac{P_{m}-P_{b}}{P_{m}-P(L,L_{2})} \right)-\ln\left(\frac{P_{m}-P_{b}}{P_{m}-P(L,L_{1})}\right)}{t_{2}-t_{1}} \tag{1}\]  
The equation describes the shale permeability (\(K\)) as a function of viscosity (\(\mu\)), static compression ratio (\(\beta\)), enclosed volume (\(V\)), length (\(L\)), cross-sectional area (\(A\)), experimental time (\(t\)), upstream pressure (\(P_{m}\)), initial downstream pressure (\(P_{o}\)) and real-time downstream pressure (\(P(L,t)\)).  
In addition, the morphology of the shale cores before and after being plugged with SMA/SiO\({}_{2}\) were observed by scanning electron microscopy (SEM) to investigate the plugging mechanism.
## 4 Experiments and Results
### Policy Learning
For the navigation task, the navigation scenario was a manually designed rectangular area with a length of 4.5 m and a width of 4.0 m.The success rates were calculated by conducting the navigation task 20 times with different initial positions and target positions. The success rates were also evaluated every 30 policy iteration steps. To test the generalization performance of the trained policy, we randomly changed the robot (or gripper) starting position, target object position, obstacle position, and the number of obstacles in real-world scenarios. We trained three different instances of each algorithm with different random seeds. Figure 7a,b illustrates the learning curves of our proposed method and the baseline methods applied in the manipulation task and navigation task, respectively. The solid curves corresponds to the mean success rates and the shaded region to the minimum and maximum success rates over the three trials. Table 2 summarizes the average success rates of the final trained polices. The proposed method achieves an average success rates of 89% in the manipulation task and 93% in the navigation task. Compared to the DR and DA methods, the results on two designed real-world tasks show that our proposed learning pipeline shows a better accuracy performance and higher-generalization capability. We find that the policy trained in a simulated environment with RGB images cannot be successfully deployed in real-world scenarios, confirming that the reality-gap has a significant harmful influence on policies directly transferred from simulated environments to the real world. The policy trained with depth images also demonstrates poor performance. Although the generated simulated-like synthetic images do not contain rich information like the real-world ones, the experimental results show that images rendered in low-fidelity with our method provide useful information for policy learning. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Manipulation Task** & **Navigation Task** \\ \hline Transfer-RGB & 24\% & 15\% \\ Transfer-Depth & 42\% & 32\% \\ DR (domain randomization) & 67\% & 61\% \\ DA (domain adaptation) & 53\% & 43\% \\ RSR(ours) & **93**\% & **83**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average success rates of the final trained policies employed in real-world scenarios. Figure 7: Learning curves for the performance of the policies trained with different methods. (**a**) Manipulation task. (**b**) Navigation task. Compared to the baseline methods, our proposed method shows a better performance.
## 2 TCN-BiLSTM Predictive Model  
### TCN Model Architecture  
TCN is a temporal convolutional network specifically designed for time-series data processing. Compared to traditional Convolutional Neural Networks (CNNs), TCN introduces causal convolutions to ensure that the output at any given time step does not depend on future time steps, preserving the temporal order of the data. Furthermore, through the use of dilated convolutions, TCN can effectively expand its receptive field without increasing the number of parameters, allowing it to capture long-range dependencies within sequences. The basic structure of the dilated convolutions model is illustrated in Figure 1. The receptive field regions of the TCN with different dilation factors (d = 4, d = 2, and d = 1) are also depicted.  
The calculation formulas for the causal convolution in TCN are shown as follows:  
\[y(t)=\sum_{k=0}^{K-1}f(k)\cdot x(t-d\cdot k) \tag{1}\]  
In this equation, \(x(t)\) denotes the input sequence, \(f\) represents the convolutional kernel, \(K\) is the kernel size, \(y(t)\) is the output sequence, and \(d\) is the dilation rate.  
Figure 1: Dilated convolution architecture diagram.  
To mitigate the overfitting issue in the network and enhance its performance, learning efficiency, and accuracy, techniques such as batch normalization, activation functions, and dropout are commonly used. Additionally, to ensure connection stability in deep TCN networks, residual connections are introduced. Combined with other methods, these form the residual module, which further optimizes the performance of TCN.  
In this study, Leaky ReLU is employed as the activation function instead of the traditional ReLU. Leaky ReLU retains a small slope on the negative axis, allowing some negative values to pass through. This prevents the issue of completely inactive neurons for negative inputs, which is commonly observed with ReLU. Leaky ReLU is more adaptive to time-series data with uneven feature distributions. The formula is expressed as follows:  
\[f(x)=\left\{\begin{array}{l}x,x>0\\ ax,x\leq 0\end{array}\right. \tag{2}\]  
In this formula, \(x\) represents the input value, and \"a\" denotes the slope for the negative input region. The ax part indicates that when the input \(x\) is negative, the output will be the product of the input value \(x\) and the constant \"a\". Here, \"a\" is a small constant, typically set to 0.01, which ensures that there is still a slight output even for negative inputs. This prevents neurons from becoming \"dead\"--that is, not activating at all--when faced with negative input.
# Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices
## 3 Adaptive Kinetic Scrolling
### Adaptive Kinetic Scrolling Speed  
Kinetic scrolling is a technique developed using inertial effects and friction, and the scrolling speed is dependent on these two physical concepts. Inertia is caused by the flicking or sliding gestures input by the user. Flicking [18; 28] is a throw-like gesture, which is converted into an inertial effect wherein friction is constantly applied to the scroll. However, with the proposed AKS technique, the inertial effect is determined by both the input gesture of the user and the situation. Figure 2 shows factors other than the user input that affect AKS.  
Figure 1: State machine for adaptive kinetic scrolling (AKS). The top part (**A**) (gray-boxed) represents the available kinetic scroll as a state machine and the bottom part (**B**) (blue-boxed) represents a state machine added newly for AKS.  
According to the proposed solution, if the amount of remaining data is large, the speed is higher than that of standard kinetic scrolling; similarly, if the amount of remaining data is small, the speed is adjusted marginally. Using this mechanism, the initial speed of kinetic scrolling is determined dynamically on each occasion.  
Kinetic scrolling speed exhibits an exponential regression curve based on the frictional effect. Scrolling velocity decelerated by friction is implemented in the form of an exponential decay factor [27]. The variable denotes the elapsed time, and the constants depend on the mobile device and Operating System. Figure 3 presents a comparison of the kinetic scrolling speed with and without AKS.  
In our model, the adjusted initial velocity (\(v\)) is calculated as:  
\[v=v_{0}\times(\log_{A}(\frac{RD}{B}+C)) \tag{1}\]  
The variable \(RD\) represents the amount of the remaining data, and the constants \(A\), \(B\), and \(C\) can be optimized according to environmental factors such as the screen size of the device and type of information. Because user interfaces should be designed with consideration to the task and size of the touch screen and not applied universally across all devices [29].  
Because the speed of kinetic scrolling is represented by an exponential decay, multiplying it by a monotonically non-decreasing positive function can produce an amplifying effect. Excessive speed amplification can adversely affect user experience; therefore, we have proposed a logarithm-based amplification function.  
Figure 3: Kinetic scrolling speed for different data sizes, compared with standard kinetic scrolling.  
Figure 2: Remaining data determining scrolling speed. In available kinetic scrolling methods, the speed of automatic scrolling is determined only by the input gesture regardless of the situation. In AKS, we further adjust the speed of automatic scrolling by taking into consideration the remaining data.
## 2 Coded CDMA
### Suboptimal Detectors in Coded CDMA  
As optimal detectors are too computationally expensive for implementation in practical systems, there has been significant interest in linear multiuser detectors in coded  
Figure 1: Coded Code Division Multiple Access transmission.  
multiuser receivers [27; 28; 29]. It is in the field of coded multiuser receivers with sub-optimal multiuser detectors that this thesis expresses an interest. Specifically, the primary focus of this work involves the reception of coded CDMA with a receiver which utilises Interference Cancellation (IC) techniques. A typical diagram is shown in Figure 2. Operation of the receiver of Figure 2 is as follows:  
At iteration \(n\), the PIC takes as input the received signal \(r\) and the signal estimates \(x_{k}^{(n-1)}\), from the previous iteration \((n-1)\). Based on these inputs, the PIC provides an updated signal estimate \(\tilde{x}_{k}^{(n)}\). The output signal is considered Gaussian, \(\tilde{x}\sim\mathcal{N}(\tilde{\mu}_{k}^{(n)},(\tilde{\sigma}_{k}^{(n)})^{2})\), and the output statistics \(\tilde{\mu}_{k}^{(n)}\), ((\(\tilde{\sigma}_{k}^{(n)})^{2})\) are also determined. The output signal \(\tilde{x}_{k}^{(n)}\) is then converted to LLR format through the function block \(T\), which is then de-interleaved to form the prior LLR input \(L_{k,p}^{(n)}\) for the SU SISO decoders. Based on the inputs \(L_{k,p}^{(n)}\) and knowledge of the code trellis, the SISO generates extrinsic LLR outputs \(L_{k,e}^{(n)}\), which are then interleaved, and converted back to signal form through the function block \(U\) to form a-priori signal estimates \(\hat{x}_{k}^{(n)}\) for the next iteration. An iterative cycle is now complete.
## 2 Materials and Methods
### Data  
The EHG signals used in this work are from the Icelandic 16-electrode EHG database, and are available from PhysioNet [31]. The Icelandic database has 122 recordings from 45 pregnant subjects, several of whom did more than one acquisition session. This database has 4 cases of preterm birth. All the recordings were performed in the third gestational trimester. The data were recorded using 16 electrodes in a 4-by-4 grid [31]. In the present study, a bipolar channel was used. Its main advantage is the reduction of the common mode noise found in maternal electrocardiograms and the signal interference of respiratory movements. The selected bipolar channel was chosen as one that allows for a wider coverage of the uterine muscle: 1-16 or 4-13. The selected arrangement for this study was the channel 4-13, as shown in Figure 1[32; 33]. The data was processed with MATLAB(r) version 2019a and the respective Statistics and Machine Learning, Wavelet, and Signal Processing Toolboxes. The pre-processing steps included a sampling frequency rate reduction from the original 200 Hz to 4 Hz, a 20-s signal borders trimming to eliminate onset and offset transients, and a wavelet packet passband filtering between 0.1 and 1 Hz where most of the relevant EHG energy rests [6; 18; 34]. Sixteen percent of the Icelandic database subjects were selected in order to cover the available range of the gestational weeks recording times: 29 to 41. None of these cases represent labor subjects. A total of 2382 contractions obtained from 20 subjects were scored for the five energy methods.
## Abbreviations  
The following abbreviations are used in this manuscript.  
\begin{tabular}{l l} QoS & Quality of Service \\ PL & Path Loss \\ ML & Machine Learning \\ NN & Neural Networks \\ RMSE & Root Mean Squared Error \\ CNN & Convolutional Neural Network \\ BS & Base Station \\ ANN & Artificial NN-based \\ GP & Gaussian process \\
5G & Fifth Generation network \\ AP & Access Point \\ EIRP & Equivalent Isotopically Radiated Power \\ WHIPP & WICA Heuristic Indoor Propagation Prediction \\ MAE & Mean Absolute Error \\ GA & Genetic Algorithm \\ Rx & Received Signal Level \\ LRM & Linear Regression Model \\ LAMS & Local Area multiple-line Scanning \\ PSO & Particle Swarm Optimization \\ Wi-Fi & Wireless Fidelity \\ WLAN & Wireless Local Area Network \\ BPSO & Binary PSO \\ O2I & Outdoor-to-Indoor \\ I2I & Indoor-to-Indoor \\ eMBB & Extreme Mobile Broadband \\ eMTC & Massive Machine Type Communications \\ URLLC & Ultra-Reliable Low Latency Communication \\
5G-NR & 5G New Radio \\ FR1 & Frequency Range 1 \\ FR2 & Frequency Range 2 \\ \end{tabular}
## 2 Position Domain Control for Contour Tracking
### PDC-PID Control Law  
Similar to a time domain PID controller, a PDC-PID controller can be expressed as:  
\[\tau_{si}(q_{m})=K_{Pi}e_{si}(q_{m})+K_{Di}e_{si}^{\prime}(q_{m})+K_{li}\int_{0 }^{q_{m}}e_{si}(s)ds \tag{7}\]  
where \(K_{Pl}\), \(K_{Di}\), and \(K_{Il}\) are the control gains for the \(i\)th slave motion, respectively. The tracking errors of the slave motions are defined as follows:  
\[\begin{cases}e_{s}(q_{m})=q_{sd}(q_{m})-q_{s}(q_{m})\\ e^{\prime}{}_{s}(q_{m})=q^{\prime}{}_{sd}(q_{m})-q^{\prime}{}_{s}(q_{m})\end{cases} \tag{8}\]  
It should be noticed that the errors of the system, and consequently the control input, are functions of the master axis motion. Furthermore, the master motion is still being controlled in time domain by a conventional PID controller [10]. The tracking performance mainly depends on the selection of PID control gains. DE, PSO, and GA are compared and used to determine the optimal gains of the PDC-PID controller to obtain better contour tracking performances.
## 6 Ethical Correlates and Limitations
* Scalability: As organizations grow and evolve, their information systems need to scale accordingly to accommodate increased data volume, user load, and business requirements. Scaling up an information system can be challenging and may require significant investment in infrastructure and resources. Addressing these limitations requires a holistic approach that involves careful planning, investment in technology infrastructure and resources, ongoing training and support for users, and collaboration with healthcare stakeholders to ensure that the medical information system meets the needs of patients and providers while adhering to regulatory requirements.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Architecture for Adaptive Energy Management
#### 3.3.3 Mobile Network Operator (MNO) Modeling  
The MNO in the network slicing architecture is the entity that provides network resources (physical and virtual), is responsible for the creation, modification, and deletion of network slices, and is in charge of managing the energy resource for the operation of the entire mobile network.  
In this context, the total available power (\(P_{A}\)) in the mobile network ecosystem comes from the contribution of energy from renewable and non-renewable energy. The MNO is then able to control the contribution of one source over another, and particularly can promote the primary or majority use of green energy. The mathematical model of \(P_{A}\) in the proposed network management model is given by:  
\[P_{A}=P_{R}+P_{NR} \tag{2}\]  
where \(P_{R}\) represents the power obtained from renewable energy sources, while \(P_{NR}\) stands for the power from non-renewable energy sources. These parameters are given by:  
\[P_{R}=P_{A}\,\times\,w_{R} \tag{3}\] \[P_{NR}=P_{A}\,\times\,(1-w_{R}) \tag{4}\]  
where the factor \(w_{R}\in[0,1]\) denotes the weight related to the contribution of renewable energy in the total generated power \(P_{A}\), which can be controlled by the MNO according to the green energy availability and the application scenario.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Model  
In a grid network, 36 static nodes are located around each grid point with 10% variation in the spacing. Each node generates data traffic that follows the Poisson arrival process with the rate of \(\lambda\) and operates in half-duplexing mode, which may cause the busy terminal problem [38]. To reflect the characteristics of underwater channel, we apply the empirical underwater acoustic channel model [36]. Since we use no error correction technique, DATA with even a single bit error is assumed to be erroneous, while other control packets are assumed to be error-free. We determine the data rate and the transmission/receiving (Tx/Rx) powers by referring to the specifications of the commercial Teledyne Benthos ATM-903 underwater modem [39]. For simplicity, we ignore power consumption in idle mode. For CD-MACA, we set the duration time the cooperator holds DATA and the memory size to 30 s and 100 packets, respectively. Table 2 lists the default values of the system parameters used in the simulation.
###### Abstract  
Despite the construction of several curved prestressed concrete girder bridges with corrugated steel webs (CSWs) around the world; their shear behavior has seldom been investigated. Accordingly, this paper substitutes the lack of available information on the global elastic shear buckling of a plane curved corrugated steel web (PCCSW) in a curved girder. This is based on the equilibrium equations and geometric equations in the elastic theory of classical shells, combined with the constitutive relation of orthotropic shells. Currently, the global elastic shear buckling process of the PCCSW in a curved girder is studied, for the first time in literature, with an equivalent orthotropic open circular cylindrical shell (OOCCS) model. The governing differential equation of global elastic shear buckling of the PCCSW, as well as its buckling strength, is derived by considering the orthotropic characteristics of a corrugated steel web, the rational trigonometric displacement modes, [PERSON]'s method and variational principles. Additionally, the accuracy of the proposed theoretical formula is verified by comparison with finite element (FE) results. Moreover, the expressions of the inner or outer folded angle and radius of curvature are given by the cosine theorem of the trigonometric function and inverse trigonometric function. Subsequently, parametric analysis of the shear buckling behavior of the PCCSW is carried out by considering the cases where the radius of curvature is constant or variable. This parametric analysis highlights the effects of web dimensions, height-to-thickness ratio, aspect ratios of longitudinal and inclined panels, corrugation height, curvature radius and folded angles on the elastic shear buckling strength. As a result, this study provides a theoretical reference for the design and application of composite curved girders with CSWs.  
curved girder; curvature radius; corrugated steel web; global shear buckling; Galerkin's method; variational principle