esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Targeted Delivery of Antibiotics to Sites of Infection
### Active Targeting
#### 4.2.1 Active Targeting to Bacterial Cells
Each cell type can be adapted and fully specialized for its own features. For example, while red blood cells are responsible for circulating and supplying oxygen, platelets play a central role in maintaining hemostasis and adhering to many foreign substrates, such as infectious diseases. By exploiting these available features and their natural functions, researchers have used different derivatives or entire cell membranes to target specific cells and tissues (Table 3) [108]. For example, _H. pylori_ is shown to bind with integrin \(\beta\)1 (CD29) and a group of carbohydrate receptors (such as mannose or fucose) in gastric epithelial cells (AGS cells). Inspired by this natural pathogen-host interaction, whole membranes of AGS cells were coated onto antibiotic-loaded polymeric cores, in a study by [PERSON] et al. [110]. The resulting biomimetic NPs bore the same surface antigens as the source AGS cells and, thus, had inherent adhesion to _H. pylori_ (Figure 9C). Superior therapeutic efficacy of the antibiotic-loaded NPs has been demonstrated both in vitro and in a mouse model infection when compared with the free antibiotic as well as the non-targeted counterparts. Similarly, macrophage or platelet-coated nanocarriers are typical examples of this potential dosage form [108; 109]. Figure 9: Active targeting of antibiotic-loaded nanocarriers to bacterial cells. (**A**) Schematic illustrations of formation, bacterial targeting, and drug release from micelles with vancomycin-mediated targeting (reprinted with permission from [54], copyright 2018 ACS). (**B**) Schematic illustrations of antibody-conjugated nanocarriers for targeted antibiotic delivery, and 3D fluorescent image showing a specific interaction between targeted NPs (red sphere) and _S. aureus_ (green sphere) (adapted with permission from [33], copyright 2021 ACS). (**C**) Schematic illustrations of the application of gastric epithelial cell membrane-coated NPs for targeted antibiotic delivery to treat _H. pylori_ infection (reprinted with permission from [110], copyright 2018 John Wiley and Sons).
## 3 Performance Evaluation  
Due to the presence of noise and other disturbances like fading and hardware imperfections, the detected bits in the receiver contain errors. In addition, it is well known that noise and other interferences over a channel constraint the mutual information. Because of that, the primary aim is to estimate BER and mutual information in the presence of the previously mentioned effects. In this Section, we offer formulas for analytical and numerical evaluation of BER and present the model for Monte Carlo simulations. After that, we present a procedure for estimating numerical values of the mutual information under given constraints.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 5 Dam Deformation Prediction Model Based on Spatio-Temporal Correlation and IEALL  
The IEALL model is based on the analysis and forecasting of its monitoring sequence in the case of a single monitoring point, but more frequently, it is necessary to take into account the strong correlation between the deformation sequences of multiple monitoring  
Figure 3: The flowchart of IGWO to optimize the parameters of LSTM model in EALL.  
points in the case of multiple monitoring point deformation data. This section focuses on how to analyze data on dam deformation in both time and spatial dimensions to produce more precise predictions of dam deformation.  
It is assumed that in the multi-point prediction of dam deformation, there are \(N\) deformation measuring points, each of which \(D\) is a dimension sequence, and the data of the deformation time series of the measuring point is taken as the prediction sequence, the other indexes of \(D-1\) are the characteristic series of the measuring point (such as temperature, water level, aging, etc.), and the length of the time series of each measuring point is \(T\), then the global input \(X\) of the model can be expressed as the historical feature set of each measurement point: \(X=(X_{1},X_{2},\ldots,X_{T})\in\mathbb{R}^{N\times T\times D}\), where \(X_{t}\) represents the dataset of all measuring points at \(t\in(1,2,\ldots,T)\) time, in which \(x_{t}^{1},x_{t}^{2},\ldots,x_{t}^{N}\) represents the combination of monitoring sequences of all measuring points at \(t\) time. The data of \(i\) at the time \(t\) of each measurement point is composed of the deformation data and the characteristic sequence of the point, which is expressed as: \(x_{t}^{i}=x_{t}^{i,j}=\left(x_{t}^{i,1},x_{t}^{i,2},\ldots,x_{t}^{i,D}\right) ^{T}\in\mathbb{R}^{N}\), At the same time, some measuring points \(i\) can be expressed as \(x_{t}^{i}=\left(x_{t}^{i},x_{t}^{i},\ldots,x_{T}^{i}\right)\in\mathbb{R}^{N \times T}\). The research content of this section is to use a prediction model for dam deformation prediction task. That is to say, the historical sequence \(X\) of \(N\) deformation measuring points with time length \(T\) is input into the proposed model to predict the deformation prediction value of the measuring points in \(K\) days.The prediction performance of the model is concluded from the prediction results to \(\hat{Y}_{T+K}^{i}=\left(\hat{y}_{T+1}^{i},\hat{y}_{T+2}^{i},\ldots,\hat{y}_{T+K }^{i}\right)\in\mathbb{R}^{K}\). By comparing the predicted value \(\hat{Y}_{T+K}^{i}\) with the actual value \(Y_{T+K}^{i}=\left(y_{T+1}^{i},y_{T+2}^{i},\ldots,y_{T+K}^{i}\right)\in\mathbb{R }^{K}\) on \(K\) days, the conclusion is drawn regarding the model's prediction performance.
# Mechanical Properties of Green Synthesized Graphene Nano-Composite Samples
## 2 Materials and Methods
### Sample Preparation  
After the production of graphene quantum dots by green synthesis, the polymer-based nanocomposites should be prepared. For this purpose, first, it is necessary to prepare the matrix (resin). Polyesters are a category of polymers that contain the ester functional group in their main chain. Polyesters are usually transparent, flammable and brittle materials. The prepared polyester resin from Boytec company (made in Turkey) was a thermoset polymer, which was polymerized by adding a hardener. Two different samples with and without nanoparticles should be prepared for investigation of the mechanical properties. For the preparation of samples without nanoparticles, 70 g of polyester resin was poured into a beaker, and 0.350 g of a cobalt additive (0.5% wt.) was added to it. A mechanical stirrer was utilized to stir the solution for 5 min at an 80-rpm rotational speed. The solution was left in a room environment for 15 min (debubbling). The last step was adding methyl ethyl ketone peroxide (usually called acid or hardener) to the mixture. The 0.423 g of methyl ethyl ketone peroxide (0.6% wt.) was added to the mixture and stirred for 3 min at an 80-rpm rotational speed. The polymerization started, and after 6 min, the mixture viscosity increased such that pouring of the die was impossible. Therefore, before ending the gelation time, the mixture would be transferred to the die. After pouring the nanocomposite mixture into the die, the die was left at room temperature for 40 h and then post-cured at 80 \({}^{\circ}\)C for 8 h in an oven according to the resin producer's instructions.  
For the preparation of samples with nanoparticles, a similar procedure was repeated. The green synthesized graphene quantum dots were added to the resin (70 \(\pm\) 0.001 g) with different weight percentages (0.25%, 0.5% and 1% wt.). The mixture of resin and graphene was mechanically stirred for 30 min at a 1500-rpm rotational speed. The mixture was sonicated for 15 min by a 14 mm in diameter probe sonicator (Hielscher UP4005) at an output power of 200 W and a frequency of 24 kHz. The solution was left in the room environment for 40 min (debubbling). The 0.350 g of cobalt additive (0.5% wt.) was added to the mixture of styrene and graphene quantum dots and mechanically stirred for 3 min at an 80-rpm rotational speed. The solution was left again for 15 min. The last step was adding methyl ethyl ketone peroxide (hardener) to the mixture. The 0.423 g of methyl ethyl ketone peroxide (0.6 % wt.) was added to the mixture and stirred for 3 min at an 80-rpm rotational speed. Then, the mixture was transferred to the sample preparation die, cured at room temperature for 40 h and post-cured at 80 \({}^{\circ}\)C for 8 h in an oven. Figure 3 shows the graphene nanocomposite mixture after stirring and the prepared samples with and without graphene quantum dots.  
Figure 3: (**a**) The graphene quantum dot composite mixture, (**b**) the final graphene composites and (**c**) polyester composites (without graphene).
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 2 Related Work
### Back-Projection  
Back-projection [30] is an efficient iterative algorithm aimed at minimizing reconstruction errors. It has been widely utilized for super resolutions [31; 32; 33]. Back-projection learns the difference between high- and low-resolution images in the super-resolution domain to enhance high-resolution images and has been proven to be effective in previous studies. In order to avoid the limitation of predefined parameters, such as blur operators, DBPN [33] uses up-projection and down-projection units, which implement an algorithm with deep learning. Recent work has also validated the fact that the back-projection network can improve the reconstruction of super-resolution video [34].  
Inspired by prior works, we propose a denoising multiscale back-projection feature fusion network that utilizes back-projection units to fuse multiscale features during underwater image restoration.
## 3 Results and Discussion
### Weldability of Cu-Containing Low Alloy Steel  
The weldability of the steel was evaluated using a weld plate cut from near the top part (TP) of the full-scale forged product. The macrostructures of the cross-sections of the joints welded at 2.5 kJ/mm with SAW and 2.3 kJ/mm with GTAW are shown in Figure 13. No welding defects were observed in either macrostructure photo. Figure 14 shows the Vickers hardness distribution of each weld joint at 1-mm depth from the surface. Both weld joints had over-matching profiles, and also a maximum HAZ hardness of about 290 HV, which were acceptable values for offshore applications.  
CTOD tests were performed at 273 K on five different notch locations of the SAW weld joint made under a heat input of 3.5 kJ/mm. The results are shown in Figure 15. The test load reached a maximum at the weld metal, F.L. + 2 mm and F.L. + 5 mm, and the CTOD values were judged to be \(\delta_{\text{m}}\). However, the CTOD value decreased approaching the F.L., and showed the lowest value at the F.L. The critical CTOD value was 0.06 mm at the F.L. location. An inverse pole figure (IPF) map with HAGB was measured at the F.L. and F.L. + 2 mm locations using EBSD in order to estimate the low CTOD value of the F.L. The results are shown in Figure 16. Fine grains were observed at the F.L. + 2 mm location, but there were coarse grains at the F.L. The temperature rise caused by welding probably leads to significant grain coarsening near the F.L. It is well-known that the toughness of the HAZ is decreased by the formation of coarse grain HAZ. Hence, it is important to reduce the heat input to the welded part, and suppress the grain coarsening in order to improve the toughness of the welded part.  
Figure 14: Hardness profiles of the SAW (**a**) and GTAW (**b**) weld joints.  
Figure 13: Macrostructures of cross-sections of submerged arc welding (SAW) (**a**) and gas tungsten arc welding (GTAW) (**b**) weld joints.  
properties were significantly improved with low-heat input GTAW. The critical CTOD values with heat inputs of 2.7 kJ/mm and 1.6 kJ/mm were 0.17 mm and 0.62 mm, respectively. The results suggest that the CTOD property has a tendency to improve with the decreasing heat input of the weld, and the tendency in GTAW was clearer than that in SAW. The difference between the SAW and GTAW results might be due to the shape of the HAZ, and we will work to clarify this in future studies. We have concluded that the long part forgings of Cu-containing low alloy steel have a good weldability, because the maximum hardness of HAZ is less than 300 HV, and the HAZ of steel has a good CTOD property with heat input less than 2.3 kJ/mm using GTAW.
# Data-Driven Control Algorithm for Snake Manipulator
## 4 Control Simulation of Snake Manipulator Based on Data-Drive
### Analysis of Simulation Results  
A new DDPG agent was built according to the method described in Section 3.3. The observed value of the agent is the coordinate position \(\begin{bmatrix}x_{o}&y_{o}&z_{o}\end{bmatrix}^{\text{T}}\). The output action value is the coordinate location point \(\begin{bmatrix}x_{a}&y_{a}&z_{a}\end{bmatrix}^{\text{T}}\). The reward value function is set to the error between the action value and the expected value. The sampling time of the training network is 1 s, the simulation time is 100 s, and the number of samples for a single episode is 100. The number of sample training episodes is set to 3000. The 3000 training results of the agent are shown in Figure 11. In Figure 11, it can be found that with the increase in training times, the reward value of the sample gradually tends toward zero, and the gap between the sample Q value and the sample Q value decreases and tends to be stable. When the sample reward value tends to be stable, it means that the system has converged and the network training has reached the current best state. The closer the sample reward value is to zero, the smaller the system error value is and the higher the system accuracy is.  
The 12 th, 1000 th, and 3000 training states were randomly selected, and the error values are shown in Table 2. The output results and error changes of the control system during training are shown in Figure 12.  
Figure 11: One hundred samples for a single episode and 3000 training maps for agents.  
Figure 10: Input and output data set.  
Figure 12: Sampling of the system training process: (**left**) error curve; (**right**) output value.  
Figure 12 shows the 12 th training state in the initial stage, the 1000 th in the middle of the training, and the 3000 th at the end of the training. The three pictures on the left are error curves, which represent the data output error values for the database. The average errors of the 12 th, 1000 th, and 3000 th training states are 0.0142, 0.006, and 0.0034, respectively. The specific data are shown in Table 2. The three pictures on the right side of Figure 12 are the output action graphs of the three training states, which clearly show that the curves become smoother and that the fluctuations are reduced. The fluctuation of the output curve represents the stability and accuracy of the output of the control system. With the increase in the number of training episodes, the stability of the system increases.  
It can be found from Table 2 that, with the increase in training times, the control effect of the snake manipulator control system is better, and the control accuracy is continuously improved. In addition, compared with the traditional control methods, the control accuracy and stability of the snake manipulator control system are significantly improved, which verifies the effectiveness and superiority of the data-driven control algorithm based on DDPG.
## 5 Result and Discussion  
The MANET network and simulations were analyzed using the Network Simulator 3 simulator. NS-3 is an open-source discrete-event network simulator that is widely used for network research and education ([[https://www.nsnam.org/](https://www.nsnam.org/)]([https://www.nsnam.org/](https://www.nsnam.org/)) accessed on 25 February 2023). The study done in this article employed NS-3 to investigate and assess the performance of several networking protocols, notably, OLSR, proposed OLSR, and AODV. NS-3 provides a complete collection of network simulation frameworks and tools. Using NS3, the simulation process entails defining the network topology, configuring the network parameters, and running the simulation using a simulation script. The NS3 simulation has been executed in the following steps: Installing NS3 on the system, writing a simulation script using the NS3 API, compiling and executing the simulation script using the NS3 build system, and at last analyzing the simulation results using NS3 tools, and scripts.  
Modules and header files are presented at the beginning of the code. Throughout the simulation run on the NS3 simulator, these modules are employed. After that, nodes in the network are created using the Create method and accessed using a NodeContainer in the NS-3 simulator. Nodes require a predetermined grid structure. Nodes are deployed using MobilityHelper and the SetPositionAllocator method in the NS-3 simulator. Initial coordinates (MinX, MinY), node distance represented by (DeltaX, DeltaY), number of nodes placed in the network shown by (GridWidth), and node layout approach (RowFirst or ColumnFirst) are all depicted.  
The nodes in this network are aligned in a row and are 50 m apart. There are 70 nodes used, and since their mobility is constant, they do not move. The WifiMacHelper, YanswifiPhyHelper, and YansWifiChannelHelper helpers are employed to make sure that the nodes can communicate with one another via the wireless channel. The parameters, such as physical and line addresses, are then assigned to specific nodes using the Install technique. The production of pcap files is then enabled, which Wireshark will display. All nodes in the developed network's communication are preserved in the files.  
These are some of the key parameters that need to be considered when deploying OLSR. It's important to select appropriate values for these parameters based on the specific requirements of the network (the simulation environment is represented in Table 1) and to monitor their performance over time to ensure optimal operation. Like other routing protocols, OLSR relies on a number of parameters to operate correctly. The selection of these parameters can affect the performance and stability of the protocol. Based upon the Table 1 setup network proposed optimized OLSR scheme is investigated on the parameters such as delay, load, packet delivery, TC message sent, and Throughput. To examine how the optimized OLSR protocol performs as compared with traditional OLSR in various scenarios, numerous in-depth simulations were run using the NS3 tool.
## 4 Results
### Model Construction
#### 4.1.2 Hyperparameters  
In the context of XGBoost, Bayesian optimization is used to identify the best hyperparameters for the models, due to its ability to identify them. It efficiently and effectively explores the search space using prior knowledge about the objective function. Unlike traditional methods, Bayesian optimization adapts and adjusts as more data are obtained. This helps to reduce the number of model evaluations and allows for a more focused search, resulting in optimal model performance in less time.  
XGBoost hyperparameters like learning rate and max depth have consistent behavior across all seasons, while others vary seasonally. This is shown in Table 7.  
The LSTM-RNN model also employed Bayesian optimization to identify the optimal hyperparameters. However, it was observed that the values of each parameter were similar across all stations. Therefore, it was decided to use the same parameters in all experiments conducted on the LSTM-RNN model. Table 8 presents these hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & \(\mathbf{R}^{2}\) & **RMSE** & **MAE** \\ \hline Hi-Di-To & 0.9993 & 0.3128 & 0.0123 \\ Hi-Ho-To & 0.9762 & 1.8531 & 0.0451 \\ Hi-To-Rs & 0.9182 & 3.4352 & 0.0783 \\ Di-Rs-To & 0.8734 & 4.2746 & 0.0730 \\ Ho-To-Rs & 0.8043 & 5.3143 & 0.0853 \\ Hi-Di-Rs & 0.9994 & 0.3003 & 0.0095 \\ Hi-Di-Ho & 0.9989 & 0.3966 & 0.0099 \\ Hi-Rs-Ho & 0.7856 & 5.5629 & 0.3228 \\ Di-Rs-Ho & 0.9857 & 1.4368 & 0.0471 \\ Di-Ho-To & 0.9223 & 3.3477 & 0.0730 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of forecasts using different input variable combinations for LSTM-RNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **Learning Rate** & **Max Depth** & **Reg Alpha** & **Reg Lambda** & **Gamma** \\ \hline Fall & 0.3 & 2 & 0.2848 & 1 & 0.6529 \\ Winter & 0.3 & 2 & \(1\times 10^{-9}\) & 1 & 0 \\ Spring & 0.3 & 2 & 1 & 0.5071 & 1 \\ Summer & 0.3 & 2 & 0.8582 & \(1\times 10^{-9}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Best hyperparameters for XGBoost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & \(\mathbf{R}^{2}\) & **RMSE** & **MAE** \\ \hline Hi-Di-To & 0.8411 & 5.2970 & 3.9513 \\ Hi-Ho-To & 0.8483 & 5.1754 & 3.8196 \\ Hi-To-Rs & 0.9037 & 4.1225 & 2.7973 \\ Di-Rs-To & 0.9103 & 3.9797 & 2.6819 \\ Ho-To-Rs & 0.9047 & 4.1017 & 2.7585 \\ Hi-Di-Rs & 0.9189 & 3.9018 & 2.6149 \\ Hi-Di-Ho & 0.8596 & 4.9783 & 3.8024 \\ Hi-Rs-Ho & 0.9064 & 4.0640 & 2.7786 \\ Di-Rs-Ho & 0.9124 & 3.9318 & 2.7274 \\ Di-Ho-To & 0.8451 & 5.2304 & 3.8550 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of forecasts using different input variable combinations for XGBoost.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 2 Methodologies  
A dataset of the maximum load of Kuwait between January 2010 and May 2020 was acquired from the Ministry of Electricity and Water (MEW) to support this research. Figure 1 presents the maximum load (MW) of Kuwait for the past ten years. With reference to Figure 1, a yearly seasonality of the maximum load can be clearly observed. The maximum load peaks are within the summer period between June and August each year due to the subtropical weather nature of Kuwait with extreme temperatures in summer and cold winter seasons. In addition, the maximum load takes an increasing trend. The gradual and steady trend increase is likely to continue for the upcoming years due to the fact of the increasing population, increased factories, and the expansion of the urban and residential areas.  
In order to forecast the maximum load of Kuwait for the upcoming years, forecasting models that incorporate seasonality and trends are required to ensure a well-fitted model to the actual data. In this work, Facebook's Prophet and Holt-Winters forecasting models have been utilized in this research for the long-term forecasting of the maximum load in Kuwait.  
The Prophet forecasting model that is used for forecasting the maximum load is a relatively new modular forecasting method based on various tunable parameters by analyst-in-the-loop, as shown in Figure 2. One of the Prophet forecasting method's strengths is that it was designed to have intuitive tuning of the parameters and does not require knowledge of the underlying model. However, to date, long-term maximum load forecasting using Prophet has not been reported in the literature. Whereas, the Holt-Winters model, or else known as triple exponential smoothing, has been excessively reported in the relevant literature and was proven to be effective in forecasting energy consumption with high accuracy [32; 33; 34]. In addition, unlike the Holt-Winters method, the Prophet forecasting method is robust to missing data and does not require data interpolation. Furthermore, seasonality with multiple periods can be incorporated in the Prophet forecasting method to provide flexibility in modeling complex data using an analyst-in-the-loop experience. The Prophet method also enables the user to use extra regressors to present multivariate forecasting to explore the effects of different variables with  
Figure 1: Actual maximum load of Kuwait between January 2010 and May 2020.  
a very fast-fitting procedure. In the next sections, the implementation of the Prophet and Holt-Winters forecasting methods is presented in detail.