esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RGD Immobilization on the Hydroxyapatite (HA) Surfaces
#### 2.2.2 Chemical Immobilization (c-RGD/HA)  
RGD immobilization followed three steps, as schematized in Figure 1. Briefly, the HA samples were sonicated in absolute ethanol 99% (Tedia Company Inc., Fairfield, OH, USA) and immersed in APTES (99%, 706493 Sigma-Aldrich, St. Louis, MO, USA) solutions at room temperature for 8 h using intense stirring. Due to their distinct surface areas, the HA powder and the discs were treated with an APTES concentration of 40 mM/ethanol and 20 mM/ethanol, respectively.  
The samples were washed three times with ethanol, sonicated, and dried (100 \({}^{\circ}\)C/24 h) to allow APTES polymerization, obtaining silanized HA. The silanized samples were sterilized by gamma radiation, as described above, and immersed in a 0.3% (_w/v_) 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC, E1769, Sigma-Aldrich, St. Louis, MO, USA)  
Figure 1: Molecularly theorized scheme representing the three steps of chemical immobilization by RGD covalent attachment (c-RGD) on the hydroxyapatite (HA) surface through (3-aminopropyl) triethoxysilane (APTES). First step: HA silanization with APTES producing c-HA1; second step: the silanized samples (c-HA1) reacted with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC); third step: c-HA2 samples were reacted overnight in RGD aqueous solution producing c-RGD/HA.  
aqueous solution at room temperature for 6 h using intense agitation and washed three times with ultrapure water. The HA samples were reacted overnight with a 100 ug/mL RGD aqueous solution. As described above, the c-RGD/HA samples were then sonicated for 5 min at room temperature to remove the weakly bound RGD peptides. The aqueous solutions used in the experiments were sterilized using Sigma-Millipore membranes (0.22 um, Sigma-Aldrich, St. Louis, MO, USA). Table 1 summarizes the sample designation, composition, and solutions used at each immobilization stage. Immobilization using RGD-FITC (ChemPep Inc., Wellington, FL, USA) occurred in the same manner as described in the third step of the immobilization.
## 5 Materials and Methods
### Methodology  
The developed tool can be accessed by three different types of users: anonymous, user, and administrator. The anonymous user is the one who does not need to be registered and can consult the relationships between pathologies, evidence and information related to probiotics. The other two types of roles, user and administrator, need to be registered in the system, and the authentication system is based on JWT, which is stateless, allowing an easy scale-out if it were necessary [28].  
The user and administrator roles can perform all operations for anonymous users and also have the privileges to create, modify or delete information. The difference between them consists of the information they can modify. An administrator can modify sensitive information or information that hardly varies, which allows for a more controlled access. More specifically, an administrator can create, delete or edit the users of the platform, as well as modify the roles. In addition, since the list of known allergens is limited, only an administrator will be authorized to modify it.  
Whoever has a user role can create, edit or delete the rest of the information, i.e., information about pathologies, evidence, strains, probiotics, doses, references and all the relationships among them.  
The procedure for adding the information to the tool is very straightforward. First, one has to enter the entities and then the relationships among them. That is, one should first add the entities that have no relationship with others, and later create the hierarchy based on relationships, which implies that it will not be possible to create evidence if the pathology or the dose of the probiotic has not been previously available. The methodology suggested so that someone with the role of user can enter the information is the following:1. Enter the pathology if it does not exist.
2. Add all the strains of probiotictic that are not discharged into the system.
3. Enter the probiotictic with its corresponding relationships (strains and allergens).
4. Specify the doses at which the probiotictic can be administered (type of dose, quantity, and frequency).
5. Include the references that guarantee that the doses of a certain probiotic are adequate for a certain pathology.
6. Insert the evidence that relates all the information entered in the previous steps.
## 4 Simulation Experiment Analysis
### Comparative Analysis of CG-TSA Benchmarking Function Search  
To verify the advantages of the CG-TSA for benchmark test function seeking and to analyse the simulation experimental results more intuitively, the WOA, GWO algorithm, SCA, AOA, and basic TSA were selected for the benchmarking function finding performance comparison experiments with the CG-TSA. Each algorithm was run 30 times independently. Figure 4 shows the average convergence curves of some of the benchmark functions. The horizontal coordinate is the number of iterations, and the vertical coordinate is the fitness value.  
As seen in Figure 4, the CG-TSA achieved theoretical optima for both unimodal and complex multimodal functions. In particular, for the unimodal F3 function, the CG-TSA expanded the search range in the early stage and accelerated the convergence of the algorithm in later stages, indicating that the CG-TSA introducing the Halton sequence had advantages in finding the optimal accuracy of the basic function. For the single-peaked F6 function, the CG-TSA jumped out of the local optimal solution, which indicated that the introduced cosine mutation effectively helped the algorithm to jump out of the local extremum. As seen in the multipeaked F7-F10, the CG-TSA converged to the theoretical optimum quickly compared with the other algorithms under the same number of iterations. Based on the above analysis, the grouping improvement strategy of the cosine mutation introduced in this paper effectively helped the TSA to show obvious advantages in the basic function optimisation and improve the convergence accuracy of the algorithm.
## 1 Introduction
India has the highest freshwater demand, led by its burgeoning population, agricultural expansion, socio-economic progress, rapid urban sprawl, and industrial developments [21]. According to World Resources Institute [22], 54% of India's total geographical area faces high to extremely high water stress. The water availability, in terms of per year per capita in India, declined from 5177 m\({}^{3}\) in 1951 to 1567 m\({}^{3}\) in 2011 and is projected to further dwindle to about 1140 m\({}^{3}\) in 2050 [23; 24]. Globally, the agriculture sector consumes a major chunk of fresh water [25] and more so in India [26]. A study by National Bank for Agriculture and Rural Development (NABARD) [27] revealed that the agriculture sector ingests about 78% of the available fresh water resources of India. Despite this, till now, only 48% of the net sown area, i.e., 68.6 million hectares of the country, gets irrigated and the rest is rainfed [28]. Further, groundwater accounts for about 62% of the total fresh water used for irrigation in India, contributing to the significant groundwater depletion rate [29]. The Central Groundwater Board (CGWB) of India reported a sharp decline in the groundwater tables between the years 2007 to 2017, especially in the northern region [29]. [PERSON] et al. [27] have reported that water availability would be the primary limiting factor in Indian agriculture, rather than land. A recent study by the State of Food Security and Nutrition in the World [30], released jointly by five UN organizations, states that India still suffers from food and nutrition insecurity. In the Indian context, [PERSON] et al. [31] have analyzed the trends and patterns of important water demand drivers of India in the recent past. Their study reveals that, with the current water use pattern of the country, meeting future food demands will lead to a severe water crisis. By the end of 2050, physical water scarcity will be a common phenomenon in various river basins of India. [PERSON] et al. [32] have evaluated the performance of the Bhakra canal system in Haryana, India and found that the practice of Warabandi principle-based allocation and distribution of the canal water leads to high water productivity. [PERSON] et al. [33] have employed WA+ to estimate the total water consumption in the Subarnarekha basin, India, using open source earth observation datasets. A data scarce, semi-arid river basin has a challenge in accounting for the total water depletion. The study area is a semi-arid river basin and will link with Ken River, India. Hence, this study aimed to use the open access satellite precipitation products and to assess the total water consumption and seasonal water yield in the basin using the WA+ framework, which is the novelty of this study.
## 3 Results and Discussion
\[K=\frac{Q_{e}}{A_{e}\Delta t} \tag{22}\]2 Test and calculated COP as a function of charge at each capacity. \[K=\frac{Q_{e}}{A_{e}\Delta t} \tag{22}\]3 Compressor efficiency factor map. For the test chiller, the most important factor that causes the variation of COP is the \(E_{fg}\) and its variation. If the variation of cooling capacity and LIFT is not considered, the system COP decreases with the increase of \(E_{fg}\), because the liquid droplets entrained by the vapor do not contribute to the corresponding cooling capacity, while the evaporation and compression of the liquid droplets in the compressor consumes compression power. Moreover, due to the density difference under vapor and liquid phases and the interactions between the two phases, entrained droplets will alter the flow field within the compressor, making the compressor operation deviate from the design conditions and thus decreasing compressor performance, so 1% liquid entrainment may result in more than 1% reduction on system COP [15]. From Figure 11, it can be seen that at 600 RT, the relative difference between the tested and calculated COPs is very small (<0.4%). This is because at 600 RT the entrainment factor \(E_{fg}\) increased by only about 0.3% at the end of charging (see Figure 8). At 700 RT, \(E_{fg}\) increased by 0.87% at the end of charging, resulting in a 1.2% decrease in test COP relative to the calculated COP. As can be seen from Figure 11, a large deviation starts from the 5 th point because \(E_{fg}\) at this point is relatively large (\(E_{fg}=0.89\%\)) and \(E_{fg}\) increases rapidly with the increase of charge at 700 RT (see Figure 8), leading to the COP test value drops significantly at this point. At 800 RT, \(E_{fg}\) increased by 1.9% at the end of charging, which made the test COP decreased by 2.5% relative to the calculated COP. It can be seen from Figure 11 that the test COP has a significant decrease from the calculated value at point 4 (\(E_{fg}=1.02\%\)). In view of the important influence of \(E_{fg}\) on the unit COP, it is recommended that the maximum \(E_{fg}\) for efficient operation of the unit be controlled at less than 1%.
## 5 Results and Discussions  
The measured guided wave signals from the damaged specimen and baseline signals in intact specimen are used to construct magnitude/amplitude-based and energy-based DIs for quantitatively characterizing crack growth in an SMA490 BW steel plate-like structure along with the actuator-sensor paths. In cases where a crack is exactly located near the path or the crack size is equivalent to the same order of magnitude of the detection wavelength, the representative acoustic features of the measured guided wave are changed significantly, which corresponds to the larger DI coefficient between two signals. In contrast, the signal deviation would be trivial if the crack is far away from the actuator-sensor path or the crack size is much smaller than the probing wavelength [25]. In particular, the magnitude/amplitude-based and energy-based DIs have proven the susceptibility to signal magnitude/amplitude variation as well as signal energy transformation.  
This section includes four parts: (1) data comparison of different sampling points, (2) magnitude-based and energy-based DIs in the time domain, (3) amplitude-based and energy-based DIs in the frequency domain, and (4) signal fusion for all the actuator-sensor paths.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Field Test on an Embedded Linux Platform
#### 5.2.1 Analysis of the Forwarding Certification Process  
In the test, node 0 was set as the root node, and node 8 was the node to be synchronized. Nodes 1, 2, 3, and 4 were set as the second hop, being one hop from the root node, and nodes 5, 6, and 7 were set as the third hop, being two hops from the root node. The topology described above is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num.** & **Wireless Card Mac Address** & **Frequency Offset Node 0 (ppm)** \\ \hline
0 (Standard) & FC:4D:D4:6D:D7:F4 & 0 \\ \hline
1 & E0:2A:82:71:F4:2C & \(-\)115.5 \\ \hline
2 & 02:25:65:38:34:F0 & 12.4 \\ \hline
3 & 44:33:4C:49:41:DE & 53.6 \\ \hline
4 & 44:33:4C:45:37:BA & 102.7 \\ \hline
5 & 44:33:4C:49:50:BA & 103.1 \\ \hline
6 & 44:33:4C:49:42:0C & 14.9 \\ \hline
7 & 44:33:4C:49:41:EA & 4.6 \\ \hline
8 & 44:33:4C:46:D1:C9 & 82.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency offset of embedded Linux platforms.  
Figure 8: Distribution of nodes and the single node platform. (**a**) Distribution of nodes in the field test and (**b**) embedded Linux platforms as nodes in the test.  
Figure 7: Synchronization error comparison of different \(T_{\textit{mf}}\).  
When the time synchronization test finished, the parameters of each node were recorded and are shown in Table 3. This table shows that nodes 1, 4, and 5 were not certificated, and node 2 and 7 were selected as the reference nodes. Node 8 was synchronized with node 7. The test shows that the principle of forwarding certification in DRFC-TSP could introduce a better reference node for every hop. Therefore, node 8 had 3.03 ms time offset relative to node 0 in the test.
# Influence of Different Freezing Modes on the Base Slab Displacement of an Upper Structure
## 3 Testing Results and Discussion
### Displacement in Sequential Freezing Test  
As shown in Figure 7, when the downline was not frozen, the station base slab on it was lifted. Correspondingly, when the downline was frozen, base slab above the upline, which was in a maintenance freezing phase, was also lifted. The maximum lifting displacement in experiments was 0.82 mm that is smaller than the frost heave in a simultaneous freezing mode.  
According to the average temperature of three groups of experiments, the cooling volume generated by a simultaneous freezing mode was larger than that generated by a sequential freezing mode. That is, a larger cooling volume implies that more water was migrated from an area with normal temperature to a cooling area, causing a larger frost heave.  
The engineering lifting displacement after conversion by a similarity ratio was 20.5 mm, and the average uplift velocity was 0.12 mm/day. Here it is worth noting that these results provide a reference for estimation of frost heave in a freezing process.  
With a simultaneous freezing mode, the active freezing time was 45 days in the project, and with a sequential freezing mode, the sum time of active freezing was 135 days. Compared with the simultaneous freezing mode, it is shown that, although a construction period is extended with the increase of cost, the latter can effectively reduce the lift displacement of upper buildings and ensure their safety.  
Figure 6: Diagram of a simple method for the calculation of frost heave.  
It is also observed from Figure 7 that there is an apparent settlement of base slab due to excavation. However, the settlement was lifted several times. The reason for such a phenomenon can be explained as follows. When excavation of a section causes a settlement of base slab, other sections are still in a process of active freezing. Thus, settlement after excavation slows down and lifts up again. The settlement and uplift of base slab alternately occur with a zigzag shape. It implies that sequential excavation can reduce the frost heave during a construction process.  
In a sequential excavation process, frozen soil was excavated by a high-power drill, and then a circle steel plate with a thickness of 7 mm was inserted to simulate a steel tunnel segment. The average settlements of four sections in the twin-tunnel were 0.18, 0.23, 0.17 and 0.15 mm, respectively, which are equivalent to 4.5, 5.75, 4.25 and 3.75 mm in project.  
The settlement tended to be stable after about eight days of excavation in the project. Based on the average sinking value, there was the largest settlement when Section II of the upline was excavated. It is due to that after soil in the upper tunnel was completely excavated, overburden pressure was entirely supported by the frozen wall and tunnel segment, resulting in a large settlement. Here is worth noting that most of the settlements of base slab were induced by excavation of the upline. Therefore, settlement after excavation of the downline was less than that after excavation of the upline.
## 3 Results and Discussion
### Results of Parametric Study
#### 3.2.2 Results in Terms of Activation Function  
This section explores the influence of using different activation functions on the performance of the ANN model. To this end, other parameters such as training function was fixed as Levenberg-Marquardt, number of neurons was fixed as 20 and cost function was fixed as mean square error. Figure 7a-d show the box plot distribution of R2, Slope, RMSE and MAE, respectively, as a function of the six activation functions used. The presentation is the same as Figure 6--median, mean, 25-75% and  
Figure 6: Evaluation of training algorithms: box plot over 1000 random sampling runs with respect to (a) R2, (b) Slope, (c) RMSE and (d) MAE.  
9-91% percentiles of the probability distribution over 1000 random sampling runs are highlighted. The performance was ranked based on the median value of the distribution. Results showed a poor performance for the purelin activation function. On the other hand, the logsig, tansig and rectilin offered superior performance for the ANN model, especially the rectilin activation function. Therefore, ultimately, the rectilin function was chosen as optimal.
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 2 Research Materials and Methods
### Experimental Setup  
An experimental setup was fabricated to simulate the ground of a damaged sewer pipe. Figure 2 shows the configuration. The experimental setup was fabricated using transparent acrylic panels to observe underground cavities, and it was reinforced with a metal frame to prevent the deformation of the soil box. The soil box used to simulate the ground measured 50 cm in width, 25 cm in depth, and 50 cm in height, and a permeable water tank 10 cm in width, 25 cm in depth, and 50 cm in height was attached to both sides of the soil box to simulate the groundwater level. As shown in Figure 3, circular holes 5 mm in diameter were created between the water tanks and the soil box so that water could flow  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specific Gravity** & **Maximum Void** & **Minimum Void** & **Maximum Dry** & **Minimum Dry** & **Unified Soil** \\
**(\(G_{s}\))** & **Ratio** & **Ratio** & **Density** & **Density** & **Classification** \\  & **(\(e_{max}\))** & **(\(e_{min}\))** & **(\(e_{dmax}\))** & **(\(e_{dmin}\))** & **(\(e_{dmin}\))** & **Classification** \\ \hline
2.63 & 0.94 & 0.65 & 1.59 t/m\({}^{3}\) & 1.36 t/m\({}^{3}\) & SP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the standard sand used in the laboratory model test.  
Figure 1: Particle size distribution of Jumunjin standard sand.  
from the water tanks to the soil box. Filters were installed on the sides of the soil box to prevent soil discharge between the soil box and the water tanks when the groundwater level rose. As shown in Figure 4, a crack was created on the bottom surface of the soil box to simulate a crack in a sewer pipe. The shape and size are significant factors affecting the experimental results, but this study assumed the circle crack with a diameter of 1.5 cm. It was used to induce soil discharge by forming a seepage flow under fluctuations in the groundwater level. In addition, a drainage valve was installed on the crack to prevent the loss of soil particles until the model ground was fully saturated. However, in this study, geometric, kinematic, and dynamic similarity laws between the prototype (actual structure) and the laboratory model were not strictly applied, and no specific similarity ratio (e.g., 1:20, 1:50) was adopted. The size of the soil box was determined primarily based on laboratory installation feasibility and minimization of boundary effects, rather than on analytical similarity laws. Therefore, the significance of the experimental results in this study lies in the qualitative analysis of the mechanism and behavior of ground subsidence, rather than in quantitative prediction.  
Figure 3: Details of the water flow system between the water tank and the soil box: **(a)** general view of the interface showing multiple 5 mm diameter holes, **(b)** close-up view of a circular hole (5 mm in diameter) for water inflow.  
Figure 2: **(a)** Schematic diagram of the model soil box and **(b)** image of the experimental setup including the soil box.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 3 The Proposed SDNPS  
### SDNPS Framework  
First, we present the SDNPS architecture. The client services are divided into two categories--mMTC and URLLC--which are assigned different network slices (Figure 5). Each network slice is logically independent of the other in terms of network resources, including equipment, access, transmission, and core networks. The SDN comprises three planes: application, control, and data. The SDN controller in the control plane uses client context-aware forwarding rules or server context-aware strategies to manage the network slices. The client context comprises the client identity, service quality requirements, and virtual network resources to satisfy all incoming end user requests. With INT [23], each P4 switch along a path in the data plane can directly record the network status, including the queue occupancy, link throughput, and processing delay, from an INT header added by the ingress switch before forwarding. When a data packet carrying INT data arrives at the egress switch, the INT data are duplicated and sent upward to the INT data collector module in the application plane to monitor the status of each switch on a flow path. The NS controller is responsible for changing paths and informing the reroute module of path reconfigurations. The P4 switches are activated to update the forwarding rules when necessary. The SDNPS can both reduce the burden on the SDN controller and achieve real-time control of transmission paths in network slices. Thus, both mMTC and URLLC can meet their respective service quality requirements by using 5G NS with SDN and P4 switches.