esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research on Flexural Strengthening with Textile Reinforced Concrete
### General Information  
There are standards on design rules and models for RC structures, e.g., in Reference [41]. In case of bending, the calculation can be done by an iterative process. A similar procedure was also chosen for the calculation of the flexural strengthening with TRC. In accordance with the design method for steel reinforced concrete, the following assumptions and simplifications are defined (compare e.g., References [41; 42] and also References [23; 36]):  
* Cross sections remain plane (Bernoulli hypothesis).
* Strain compatibility between reinforcement and concrete is assumed.
* The concrete's tensile strength is ignored; all tensile forces are taken up by steel and textile reinforcements.
* Rigid bond between steel, concrete, and textile reinforcement may be assumed.
* The design is carried out at the ultimate limit state (ULS), i.e. at least one material (concrete, reinforcing steel, or textile reinforcement) reaches the ultimate strain.  
A first model was presented by [PERSON] in Reference [43] in 2007. Meanwhile, it was further developed [44; 42; 45; 46; 47] and modified due to new knowledge and research findings for the later used materials. Furthermore, a similar design proposal is presented in [PERSON] et al. [23]. The main difference lies in the choice of the reference plane for the formation of the internal equilibrium. Compared to Reference [23], the model presented in the following has the advantage that the reference horizon is fixed by the geometry and the structural design of the component (position of the reinforcements) and does not have to be determined by stress or strain distribution.
## 3 Results and Discussion
### Corliss Valve Gear System  
High-pressure steam enters the cylinder block (Figure 4), equipped with two inlet and two exhaust valves, inducing a linear reciprocating motion in the piston rod, assisted by the crosshead and connecting rod (Figure 5). The connecting rod converts this linear motion into rotational motion and is connected to the crankshaft to generate mechanical work. Each time the piston reaches one of the two dead centers (top dead center or bottom dead center), the steam is expelled to be condensed and the cycle is repeated.  
Figure 3: Master assembly and subassemblies used in the modeling process.  
The Corliss valve gear incorporated into this invention consists of four valves positioned at the corners of the cylinder block (Figure 6), with two dedicated to steam inlet located at the top of the cylinder block and two for steam outlet, located at the bottom. These valves are actuated by the motion of the crankshaft through their connection to the eccentric strap via coupling rods (Figure 7), thus creating an automatic control of the steam flow within the cylinder.  
Additionally, the inlet valves are influenced by the governor, which modifies the position of the cam levers (Figure 8), located at the front of the cylinder block, to stabilize steam intake and machine speed.  
Figure 4: Cross-section of the intake and exhaust valves inside the cylinder.  
Figure 5: Axonometric view of the elements involved in transforming linear movement into circular movement.  
As previously mentioned, high-pressure steam enters the cylinder through one of the steam inlet valves. To allow for the piston's return stroke, the steam outlet valve located at the opposite end must permit the passage of steam until the bottom dead center is reached. Thus, once the piston rod has retracted, the second inlet valve, which initially blocked the steam flow, positions itself to connect the steam intake to the cylinder. Simultaneously, the second outlet valve connects to the exhaust, causing the rod to move forward until it reaches the top dead center (Figure 9).  
Figure 6: Cross-section of cylinder block showing the steam inlet and outlet valves (**left**) and steam inlet valve distribution (**right**).  
Figure 7: Top view of the ensemble showing the eccentric straps, inlet steam lever coupling rod, and exhaust valve coupling rods.
###### Abstract  
A new method based on reverse-phase HPLC combined with photodiode array (PDA) was developed to quantify the release of _trans_-resveratrol (tRES) from solid lipid nanoparticles (SLN). The mobile phase was composed of 75:0:25 (V/V) water/methanol/acetonitrile at 0-3.5 min, 32.5:30.0:37.5 (V/V) water/methanol/acetonitrile at 3.6-5.8 min, and 75:0:25 (V/V) water/methanol/acetonitrile at 5.9-10 min. The flow rate was set at 1.0 mL/min, and tRES was detected at the wavelength of 306.6 nm. A concentration range of 1-100 ug/mL was used to obtain the linear calibration curve. SLN were produced by ultrasound technique to load 0.1% (wt/wt) of tRES, and the in vitro release of the drug was run in modified Franz diffusion cells. The mean recovery of tRES was found to be 96.84 \(\pm\) 0.32%. The intra-assay and inter-assay coefficients of variation were less than 5%. The proposed method was applied to in vitro permeability studies, and the Weibull model was found to be the one that best fits the tRES release, which is characterized by a simultaneous lipid chain relaxation and erosion during drug release.  
Keywords:_trans_-resveratrol; nanotechnology; solid lipid nanoparticles; high-performance liquid chromatography +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Battery Capacity Signal Decomposition  
Taking batteries B0005 and CS2_35 as examples, the IWOA-DELM model was used to predict the battery capacity sequence after Wavelet Decomposition. Figure 7 displays the predicted results of the SOH components after decomposition for battery B0005. Figure 8 displays the predicted results of the SOH components after decomposition for battery CS2_35. Using battery B0005 as an example, the fitness function variation during the optimization process is compared with PSO and WOA optimization algorithms in Figure 9. Throughout the iterations, the fitness values of IWOA gradually decrease, indicating that  
Figure 5: The WD decomposition results of B0005 battery (**a**) and CS2\(-\)35 battery (**b**) SOH.  
Figure 6: Comparison of reconstruction errors for three algorithms on B0005.  
suitable parameters were found and optimized during the search process. The convergence speed and accuracy of IWOA are faster and higher compared to WOA and DELM.  
In Figures 7 and 8, a4 represents the overall degradation trend, while d1 to d4 represent localized regeneration and random fluctuations. There is a slight fluctuation error in the prediction of the high-frequency component d1, but the predictions of the other components are very close to the actual values. This indicates that the trained IWOA-DELM model possesses a strong capability for nonlinear mapping of time series data.  
Figure 7: Prediction results of global degradation trend and local degradation trend for the B0005 battery.  
Figure 8: Prediction results of global degradation trend and local degradation trend for the CS2\(-\)35 battery.  
Figure 9: The fitness curve of the algorithm.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Lack of Standardized AI Integration  
While the 3 GPP has begun incorporating AI/ML techniques in SONs and RICs [236; 237], there is still a lack of standardized, interoperable frameworks for AI-IMI. Challenges such as generalization across network topologies and hardware heterogeneity remain unsolved.
## 5 Case Study: Smart Conservation of Cultural Heritage
### Reasoning in a Smart Cultural Heritage Space
#### 5.2.1 Uncertainty Module  
When contextual data collected from uncertain sources (e.g., sensors) are required, the uncertainty module is responsible to pre-process such data by fusing different sources and tagging them with a confidence value that is related to the sensor quality, oldness of measurements and other parameters. Furthermore, if requested, the uncertainty module provides fuzzy sets instead of crisp sets to represent the required property. Defuzzification is performed by the _rules module._
## 3 A Comprehensive Monitoring System for Landslides in 'Space-Sky-Ground-Underground'  
With the rapid development of various high-tech technologies such as computer science and information technology, advanced monitoring technologies such as InSAR, and DFOS have been widely applied in geological disaster monitoring as well as engineering construction and maintenance. Especially in the field of landslide monitoring, various contact and non-contact monitoring technologies have emerged, providing essential evaluation criteria for scientifically studying the stability of landslides [25; 26]. Specifically, a  
Figure 8: Atlas of association analysis of posts by various countries.  
comprehensive monitoring system based on 'Space-Sky-Ground-Underground' can achieve comprehensive and refined monitoring as well as real-time and continuous acquisition of landslides. Among them, space-based and sky-based monitoring focus on the overall deformation of the landslide surface, ground-based monitoring focuses on the deformation and failure process of the surface and shallow layers of the landslide, and underground-based monitoring focuses on the interaction between various rock layers in the deep part of the landslide [27; 28; 29]. Based on multiple advanced monitoring technologies in various monitoring dimensions, a comprehensive and integrated monitoring system of 'Space-Sky-Ground-Underground' was constructed (Figure 9) to obtain the evolution process of surface and internal state characteristics information of landslides from different dimensions. Thereby, the crucial guarantees for the accurate warning, the emergency response, and the scientific prevention and control of landslide hazards were provided.
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD
#### 5.3.2 Gravitational Time Delay  
Due to the effects of general relativity, gravitational time delay errors occur during signal transmission. These errors can be calculated and corrected using the following formula [6]:  
\[\begin{array}{l}\tau_{ij}^{rel,g}=(1+\gamma)\frac{GM}{c^{2}}\ln(\frac{|\tau_ {i}(t^{1})|+|\tau_{j}(t^{2})|+|\tau_{i}(t^{r1})-\tau_{i}(t^{r1})|}{|\tau_{i}(t^ {r1})|+|\tau_{j}(t^{r1})|-|\tau_{i}(t^{r1})-\tau_{i}(t^{r1})|})\\ \tau_{ji}^{rel,g}=(1+\gamma)\frac{GM}{c^{2}}\ln(\frac{|\tau_{i}(t^{2})|+|\tau_{ i}(t^{2})|+|\tau_{i}(t^{r2})-\tau_{i}(t^{r2})|}{|\tau_{j}(t^{r2})|+|\tau_{i}(t^{r2}) |-|\tau_{i}(t^{r2})-\tau_{i}(t^{r2})|})\end{array} \tag{43}\]  
where \(\gamma\) is the post-Newtonian parameter, and \(GM\) is the Earth's gravitational constant.
## 3 System Model
### Trajectories of AUV Mobility  
After the formation of the CDS, the MST is executed on the CDS as the baseline to formulate the HC, which is the trajectory of the AUV. The MST of the given CDS having links \(\{l\left({4,7}\right),l\left({7,9}\right),l\left({9,13}\right),l\left({8,9} \right),l\left({8,14}\right)\}\) is shown in Figure 3. \(V\) is further divided into V\({}_{CDS}\) and V\({}_{non-CDS}\) sets, such that V\({}_{CDS}\subset\)V and V\({}_{non-CDS}\subset\)V. We can define V of graph G as V = V\({}_{CDS}\cup\) V\({}_{non-CDS}\), where V\({}_{CDS}\) is a set of CDS nodes and V\({}_{non-CDS}\) is a set of non-CDS nodes; moreover, V\({}_{CDS}\)\(\cap\) V\({}_{non-CDS}\) = \(\emptyset\). Similarly, edges, E, are subdivided into E\({}_{CDS}\) and E\({}_{non-CDS}\), such that, E = E\({}_{CDS}\)\(\cup\) E\({}_{non-CDS}\). E\({}_{CDS}\) is a set of edges between two CDS nodes. E\({}_{non-CDS}\) is a set of edges between non-CDS nodes or CDS and non-CDS nodes. Edges in E\({}_{CDS}\) are further divided into E\({}_{MST}\) and E\({}_{non-MST}\). According to Definition 1 and Definition 2, all vertices of V\({}_{CDS}\) are connected with each other through edges, E\({}_{CDS}\). The path for the AUV is accomplished along E\({}_{MST}\), which is the sub-optimal random track.  
After establishment of the CDS, the criterion for the traversing of the AUV is as follows. There are three lists that are maintained to find the next hop of the AUV:  
* Forward traversed list (FTL)
* Reverse traversed list (RTL)
* Trajectory list (TL)  
An MST is constructed by initiating from the highest ID of the CDS and by adding edges in the MST, which have less weights. FTL is always scanned first to find the shortest outgoing edge. RTL is scanned, if FTL is empty. Once the leaf node of the CDS reached, the incoming edge is immediately traversed back. When all of the links are added to the TL and the current node is a starting node, then the HC is successfully constructed.  
Figure 3: Formation of the CDS-MST on the basis of the Euclidean distance.
## 2 Dissolution Damage Model
### Proposed Model for the Evolution of Porosity and Permeability Coefficient  
The dissolution of solid-phase hydration products such as CH and C-S-H gels lead to an increase in the porosity of the microstructure. [PERSON] [30] et al. found that the increase in porosity after complete dissolution of ordinary cement paste was basically the same as the volume fraction of CH in the hardened cement paste, while C-S-H precipitation of calcium caused only microscopic pores, which had less effect on porosity. Therefore, to simplify the process of calculating the porosity increment, the dissolution of C-S-H gel can be equated to the dissolution of solid phase CH. The increment of porosity \(\Delta\varphi\) in the dissolution process is shown in Equation (7). [PERSON] [31] et al. established a three-dimensional calcium dissolution mathematical model based on the mass conservation law, solid-liquid equilibrium relationship curve, porosity evolution model, and effective diffusion coefficient evolution model. The ion-exchange boundary conditions were introduced to consider the contact boundary concentration changes, and the accuracy of the results was verified by comparison with experiments to derive the porosity versus time curves, which were fitted as shown in Equation (8):  
\[\left\{\begin{array}{l}\varphi=\varphi_{0}+\Delta\varphi\\ \Delta\varphi=\left(C_{s}^{0}-C_{s}\right)M_{CH}/\rho_{CH}\end{array}\right. \tag{7}\]  
\[\varphi=\varphi_{0}+\frac{(0.361)}{\left[1+\left(\frac{348.924}{t}\right)^{1. 121}\right]^{5.733}} \tag{8}\]  
where \(\varphi_{0}\) indicates the initial porosity; \(\rho_{CH}\) indicates the density of solid phase CH, taking the value of 2.24 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\); \(M_{CH}\) indicates the molar mass of CH, taking the value of 74 g/mol; and \(t\) indicates the calendar time in days.  
The evolution equation of the permeability coefficient is obtained from the Kozeny-Carman equation:  
\[k=k_{0}(\varphi/\varphi_{0})^{3}[(1-\varphi_{0})/(1-\varphi)]^{2} \tag{9}\]  
where \(k_{0}\) indicates the initial permeability coefficient, cm/s; \(\varphi_{0}\) indicates the initial porosity; and \(\varphi\) indicates the concrete porosity after dissolution.
## 5 Simulations
### Experiment Settings  
Data were collected from a small Taiwanese enterprise engaged in the manufacture of springs. Most of the springs were meant for export to enterprises in Japan, where the tolerances are particularly low. As a result, the enterprise was eager to obtain assistance in monitoring machinery in order to maintain/improve product quality. Furthermore, this enterprise produces items for more than 50 companies, with new orders coming in every month (many of which are urgent). This greatly complicates scheduling, which often has to be updated several times each day. Clearly, this enterprise requires a systematic method by which to monitor and schedule the use of machinery.  
To enable the monitoring of machinery, we employed a three-axis accelerometer (KSM001 ADXL345) with Arduino microcontroller (Uno r3) to collect vibration data from the machinery. This system was applied to a well-worn machine (approximately 10 years old), as shown in Figure 7. This machine was designed based on a cam mechanism mixed with an electrical 3-axis station. Also, this machine offers a Computer Numerical Control (CNC) with an AC servo motor. Metal wire is output from the small hole in the middle of the machine to be shaped into a spring by the surrounding tools. The accelerometer was attached along main axis of the machine, as shown in the middle in Figure 7. All data collect from the Arduino unit was stored in a computer as a Comma-Separated Values (CSV) file to facilitate subsequent processing.  
Scheduling was based on data pertaining to actual orders, which was held in an enterprise database (Table 6). Note that all of these tasks were implemented on a single machine; that is, they were not split up among multiple machines. For the sake of simplicity, we established a set time for scheduling (31 May 2016; 22:00:00). Based on a suggestion from the enterprise workers, we focused only on the maximum delay time and maximum flow time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Job** & **Working Time** & **Deadline** \\ \hline A & 350 min & 17 June 2016 \\ B & 600 min & 23 May 2016 \\ C & 300 min & 3 June 2016 \\ D & 500 min & 10 June 2016 \\ E & 240 min & 6 June 2016 \\ F & 230 min & 28 May 2016 \\ G & 300 min & 29 June 2016 \\ H & 400 min & 30 May 2016 \\ I & 240 min & 27 May 2016 \\ J & 130 min & 26 June2016 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Jobs for the spring enterprise.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 5 Results
### Evolution Law of Pressure  
Figure 5 illustrates the evolution of the osmotic pressure field during the tunnel traversal. As depicted in Figure 5a, the pore pressure within water-rich faults decreases radially from the fault core towards the damage zone. With an external source term of 0.005 m\({}^{3}\)/s, the maximum pore pressure reaches 10.5 MPa. When the tunnel face enters the fault zone, the flow boundary conditions are altered, leading to the formation of a \"bullet-shaped\" pressure drop zone around the tunnel, as observed in Figure 5b.  
In ABAQUS, direct output of the fluid pressure gradient \(\
abla p\) for each fluid cell is not available. Therefore, to calculate the pressure gradient vector \(\
abla p\) at a specific fluid point \((x_{0},y_{0},z_{0})\) defined by the osmotic pressure function \(p(x,y,z)\), the following expression can be used:  
\[\
abla p=\left[p_{x}^{\prime}(x_{0},y_{0},z_{0}),p_{y}^{\prime}(x_{0},y_{0},z_ {0}),p_{z}^{\prime}(x_{0},y_{0},z_{0})\right]^{T} \tag{13}\]  
When \(z=0\), the pressure gradient \(\
abla p\) simplifies to: \(\
abla p=\left[p_{x}^{\prime}(x_{0},y_{0}),p_{y}^{\prime}(x_{0},y_{0}),1 \right]^{T}\).  
Using \((x,y)=(60,5)\) as the origin of the coordinate system, a temporary Cartesian coordinate system \(x^{\prime}oy^{\prime}\) is established around the tunnel. Within this coordinate system, two osmotic pressure contours, namely \(L_{i}^{DZ}\) and \(L_{i}^{FC}\), are extracted from the \(x=70\) m damage zone and the \(x=90\) m fault core (Figure 5c) working face, respectively.  
It is evident that when the excavation face enters the fault core, the contour lines become flatter, with the slope approaching zero, indicating that \(p_{x}^{\prime}(x_{0},y_{0})\to 0\). Simultaneously, as the tunnel is approached (\(y\to 0\)), the gradient component \(p_{y}^{\prime}(x_{0},y_{0})\) of the contour lines \(L_{i}^{DZ},L_{i}^{FC}\) both approaches 0. Therefore, the magnitude of the pore pressure gradient vector at the fault core is smaller than that in the damage zone. This suggests that the variation of pore pressure decreases as the excavation face approaches the fault core.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Zone** & **Material Properties** & **Value** & **Units** \\ \hline \multirow{4}{*}{Fault core} & The bulk modulus of elasticity \(K_{fc}\) & 60,000 & Mpa \\  & Initial permeability \(k_{fc0}\) & \(1.0\times 10^{-5}\) & m\({}^{2}\) \\  & Porosity \(\varphi_{fc}\) & 0.5 & 1 \\  & Biot’s coefficient \(\alpha_{fc}\) & 0.75 & 1 \\ \hline \multirow{2}{*}{Damage zone} & The equivalent bulk modulus of elasticity \(K_{dz}^{equ}\) & 2000 & Mpa \\  & Equivalent initial permeability \(k_{dz}^{equ}\) & \(2.48\times 10^{-8}\) & m\({}^{2}\) \\  & Equivalent porosity \(\varphi_{dz}^{equ}\) & \(1.0\times 10^{-4}\) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Calculation parameter table.
## 3 Path-Following Control Strategy with Deep Deterministic Policy Gradient
### Deep Deterministic Policy Gradient for Path-Following
#### 3.2.2 Rewards  
Given that the objective of an agent is to maximize long-term returns, the design of a reward function is crucial for the satisfactory performance of the agent. In the context of path following, a natural and intuitive approach is to reward the agent for minimizing the cross-track error with respect to the desired path. In [5], the agent is rewarded when the vehicle stays on the path and is penalized by an absolute value function when it deviates from the path. In [21], a Gaussian reward function centered at a cross-track error of 0, with a reasonable standard deviation, is employed. However, we believe that the exponential reward function, with its shape, incentivizes more effective minimization of cross-track error. To ensure smooth steering control, we apply penalties to excessive steering, without considering the consistency between the heading and the path. To mitigate the sparsity of the exponential reward and reduce insignificant experiences, the reward function, as shown in Equation (22), is kept concise. Moreover, if the vehicle deviates from the path beyond a certain distance \(e_{max}\) or moves in the opposite direction of the path, the current training episode is truncated, indicating a failure in completion. In such cases, a negative reward is given to penalize the failure. On the other hand, if the vehicle successfully completes the path or reaches the maximum time steps allowed for a single episode, a positive reward is given. The scaling parameters associated with the rewards are provided in Table 6.  
\[r(t)=e^{-c_{e}|e_{p}(t)|}-c_{\delta}|\delta(t)|-c_{\delta}|\delta(t)|+c_{H}H(t) -c_{F}F(t) \tag{22}\]  
where \(c_{e}>0\) determines the sparsity of the reward and the degree of convergence in training; a high \(c_{e}\) may result in overly sparse rewards. \(c_{\delta}>0\) and \(c_{\delta}>0\) scale the penalty term on the steering angle and its rate to penalize excessive steering. \(H\) and \(F\) are logical values that indicate the successful completion of an episode and failure, respectively.