esa-sceva/satcom-chunk-collection huggingface dataset:
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator
### Semi-Deterministic (Stochastic) Radio Link and Mobility Modeling
#### 3.1.2 BAN Environment  
The module called \"BAN and physical environment\" aims at modeling the environment in which wireless nodes are deployed and the attenuation resulting from the propagation of radio signals depending on the radio link conditions and the scenarios. As shown in Figures 2 and 3, this module has a central position.  
Figure 3: Central position of the BAN environment between the mobility and propagation modules.  
The BAN and physical environment module exchanges information with the mobility model (e.g., positions, directions, speeds, obstruction) and with BAN propagation models (e.g., link conditions) in order to select the corresponding channel model in our database.  
As shown in Figure 4, we assume that a BAN is formed by three nodes: belt, head and wrist. A multi-BAN network can be seen as multiple star networks. The belt can be considered as the collecting node and/or the coordinator. Some walls and rooms have also been defined in order to evaluate indoor scenarios.  
The role of the BAN and physical environment module is to determine the link conditions between the transmitter and the receiver. The link conditions have several characteristics:  
* Communication type: off-body, on-body, body-to-body, off-to-off.
* Node position on the body: belt, wrist, head, off.
* Link type: belt-to-head, belt-to-wrist, belt-to-belt, _etc_.
* Scenario condition: same room, other room.
* Mutual orientation and body-shadowed condition for inter-BAN models.
* BANs' information, such as BAN ID, BAN size, _etc_.  
In Figure 5, the three dark blocks represent intra-BAN communications respectively for BANs 1-3 and the light blocks' inter-BAN communications. According to the link conditions, we will select the corresponding channel model parameters in our database.  
Figure 4: Scenario of several BANs collocated in the same or adjacent rooms.  
Figure 5: Link condition matrix for each link (Tx-Rx).
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 3 Results and Discussion
### Discussion  
The research results of this paper can provide technical guidance for coal washing and processing industries to reduce labor costs and realize efficient automation of coal production and transportation. First, it could be seen that the designed coal moisture content measurement system is able to be applied on the experimental site basically through the verification experiment in Section 3.5. Moreover, the measurement process is carried out in an open-air environment, and the horn antenna is resistant to environmental pollution. These advantages provide a reference for online measurement and control in the production processes, such as the measurement of coal moisture content during belt transport.
## 2 Related Works
### Deep Learning Based on Vertical Federated Learning  
In our previous work [7], we could classify existing vertical federated learning methods for deep learning [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] into three groups according to their design approaches. One group [12; 13; 14; 15; 16; 17], which takes a design approach called the training loop parallelization approach from [7], requires a very large communication amount since they split their training loop. However, their performances are similar to or better than centralized training. The other group [8; 9; 10; 11], which takes a design approach called the data-driven transfer approach from [7], requires a smaller amount of communication than the group of [12; 13; 14; 15; 16; 17]. This is because transfer learning is performed to train each target task distributedly. However, their performance is relatively low in the IoT domain because they utilize features trained using unsupervised representation learning.  
Even the latest vertical federated learning methods like [29; 30; 31] fall into one of the above two groups in terms of distributed learning mechanisms. Therefore, current vertical federated learning methods mainly deal with specific problems, for example, security problems in specific application scenarios [29], a target model with a specific structure [30], and even a specific optimization method [31]. Except for specific characteristics of each problem dealt with by current vertical federated learning methods, each method could be classified as one of the above two groups from the perspective of distributed learning mechanisms.  
In contrast, features trained from an aimed task are called task-driven features in [7]. Also in the experiments of our previous work [7], features trained from an aimed task are more suitable in the IoT domain. This is because aimed tasks are important in IoT domains. Therefore, the proposed TT-VFDL-ST and our previous work [7] utilize task-driven features for improved performance and transfer learning for communication efficiency. In [7], we called this kind of design approach a task-driven transfer approach. Newly proposed TT-VFLD-ST and our previous work [7] are designed by our task-driven transfer approach. Also, we called our methods based on our design approach simply, TT-VFDL methods, which are the other group of existing vertical federated learning for deep learning. The performance of our TT-VFDL methods is the best among existing methods. Also, they are efficient in communication.
## 3 Experiments
### Model Setup  
To investigate the performance of the surface roughness predictive model, three different models are considered in the present research study: (1) combine the FFT extractor with the DNN model; (2) combine FFT and the LSTM model; and (3) utilize the one-dimensional CNN model. The designed parameters of each of the models are presented below.  
Because DNN and LSTM could not deal with raw data, the FFT feature extractor is used at the beginning. After the FFT feature extractor is used, the represented vibration spectrum has 1500 spectrum feature inputs. The 50 datasets are further used for training and testing to implement the relevant regression predictive model, i.e., DNN of LSTM. For the DNN model, the study involves four fully connected layers with layer sizes of [12288, 6144, 6144, 1], and the activation function of each layer is set as ReLU. For the LSTM model, the deep layers of LSTM are conducted with 2048 cells in sequence, and the learning rate is set as 0.0035. For the 1-D CNN model, this model is composed of the following: one convolutional layer, a max pooling layer, and fully connected neural networks. The hyper parameters of the 1-D CNN of kernel size, kernel numbers, strides, learning rate, and padding are set as 500, 16, 300, 0.0035, and 250, respectively.
# Multi-Source Satellite Imagery and Machine Learning
## 6 Ablation Analysis
### Comparison of Algorithms  
Random Forest demonstrates the best overall performance, achieving the highest accuracy and MCC while maintaining the fastest execution time across all configurations. It excels in the DGP-DPCAP and DGP-DOLIP combinations, significantly outperforming other models.  
XGBoost performs well but remains slightly behind Random Forest in accuracy and MCC. Additionally, it is somewhat slower, making it a strong contender but not the most optimal choice.  
Gradient Boosting exhibits weaker performance, especially in precision and MCC. It is also slower, making it less competitive than Random Forest and XGBoost. However, it can still be viable in specific configurations where its strengths may be beneficial.  
In conclusion, Random Forest is the most robust and efficient algorithm, with consistent and strong performance across all dataset combinations, particularly for accuracy, MCC, and running time.
## 4 Discussion
The results indicate that the image contrast of iron oxide is superior to Gd-DTPA-BMEA, and iron oxide is not only suitable for the liver, but also applicable to other organs and tissues [33; 34; 35; 36]. According to the recommendations of the Food and Drug Administration (FDA) of the United States, the clinical injection doses are 0.2 mL/kg for Gd-DTPA-BMEA and 0.08 mL/kg for ferucarbotran, which are 0.2 \(\mu\)L/g and 0.08 \(\mu\)L/g, respectively. If injected into mice with 20 g weights according to the ratios, 4 \(\mu\)L and 1.6 \(\mu\)L should be injected according to the recommended doses for clinical injection. If the total injection dose for mice is 100 \(\mu\)L, the dilution ratios are 4% and 1.6%, respectively. Compared with the experimental dilution ratios of 0.6% and 0.05%, the clinically recommended injection doses are 6.7 times and 32 times the experimental injection doses. This result represents a balance between using a lower injection dose of contrast agent and achieving good diagnostic image quality. According to this result, the injection dose of MRI contrast agent can still be adjusted, and there is still room to reduce the injection dose of MRI contrast agent. It can be seen from the results that the two types of contrast agents with different properties also show different outcomes in the organism characteristics.
## 7 Conclusions  
In this review, the most notable outcomes were obtained for fruit-based bioadsorbents, with removal efficiencies of 100% or higher (predicted efficiency using response surface methodology) for adsorption of fluoride ions and Congo red dye, respectively. Regarding adsorbents derived from seeds, AC from _Acacia eridoba_ seed pods and _Argania Spinosa_ tree nutshells-TiO\({}_{2}\) achieved the removal of 100% of MB and diclofenac. Forage and herbage adsorbents presented lower removal efficiencies; however, tea waste and SCB were suitable alternatives for the adsorption of Zn, Mn, and MB, with efficiencies around 99%. In most of these treatments, the effects of the initial adsorbate concentration, adsorbent dosage, contact time, and pH were critical for evaluation of biomaterial efficiency, in most cases involving a change in the ionic charges that govern the interaction between contaminants and adsorbents. However, it is necessary to continue studying these conditions in order to optimize the use and yield of bioadsorbents that could help solve the most challenging environmental problems involved in water pollution. Another aspect to consider is the use of recycled AC activators to reduce both environmental impacts and manufacturing costs. Moreover, in some cases the application of chemical activators is not required (e.g., in morning biochar), avoiding the cost of chemical pretreatment.  
In conclusion, this review highlights the wide availability of agricultural materials, reporting more than 60 bioadsorbents and the conditions under which they can most effectively adsorb heavy metals, dyes, pharmaceuticals, nutrients, and other contaminants that alter water sources. Further multidisciplinary studies are needed in order to apply the reviewed bioadsorbents on industrial scales while abiding by the relevant environmental policies of each sector. Lastly, it is imperative to enforce the regulations concerning industrial discharge, water safety, and waste control in order to reduce environmental impacts of anthropogenic origin.  
Writing--original draft preparation, J.A.-R., M.E.U.-L., B.E.R.-G. and S.X.L.-V.; writing--review and editing, I.E.L.-C. and D.L.C.-C.; supervision, D.L.C.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by Consejo Nacional de Ciencia y Tecnologia of Mexico (Scholarship No. 1103630).  
The authors declare no conflict of interest.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Algorithms Deployment  
After developing models that are useful and give value to the end user, it is time to make them work in a production environment. Data are stored in Microsoft Azure Cloud as described in Section 2.1. To provide an ecosystem for models that is maintenance-free and convenient in implementation, it was decided to use Microsoft Azure Cloud. Using such an approach, it is possible to deliver value from models to the end user.  
According to Figure 7, the architecture of cloud services does not involve only analytical tools per se. It also contains API that is used for acquiring data from cloud storage. In addition, it contains MOLOS.CLOUD web SCADA by REDNT S.A., where the end user can see measurements and alarms emerging from developed analytical hypotheses.  
The core of the system is Azure Functions [56] paired with analytical models. As described in Section 2.2, two approaches have been developed. Each approach has a different method of deployment. However, they are embedded in the cloud in the same way.  
Embedding algorithms in the cloud involves Microsoft Azure Functions. This cloud service allows us to run programs and scripts written in JavaScript, C#, F#, Java, PowerShell, Python, and TypeScript in the cloud, i.e., serverless [57]. The code developed within the project was written in Python, so the language was supported by Microsoft Azure Functions. For project purposes, the execution of functions was scheduled. However, it is not the only mechanism to trigger the execution of the Azure Function [58]. Each developed algorithm has its own timer-triggered Azure Function that is executed once an hour. Therefore, two Azure Functions were developed in total.
## 3 RRT-Based Guideline Planner
### Guideline Simplification  
The guideline \(\mathbf{P}_{R}\) is a basic guideline, it is not smooth and even contains some unnecessary nodes. \(\mathbf{P}_{R}\) should be optimized into a guideline with fewer nodes and be smoother. An example of a simplified guideline \(\mathbf{P}_{S}\) is shown in Figure 6, it is smoother, has fewer nodes, and is collision-free, marking a smaller passable area for the vehicle to travel. For a given path sequence \(\mathbf{P}_{R}=\left[\mathbf{p}_{1},\,\mathbf{p}_{2},\mathbf{p}_{3},\ldots,\mathbf{p}_{n-1},\,\mathbf{p }_{n}\right]\), a simplified guideline \(\mathbf{P}_{S}=\left[\mathbf{p}_{p1},\,\mathbf{p}_{p2},\mathbf{p}_{p3},\ldots,\mathbf{p}_{pn_{p}-1 },\,\mathbf{p}_{pn_{p}}\right]\) contains \(n_{p}\) nodes can be obtained through the process represented in Algorithm 2.  
```
Input:\(\mathbf{P}_{R}=\left[\mathbf{p}_{1},\,\mathbf{p}_{2},\mathbf{p}_{3},\ldots,\mathbf{p}_{n-1},\,\mathbf{p }_{n}\right]\), \(\mathbf{M}_{E}\) Output:\(\mathbf{P}_{S}\)
1. \(\mathbf{p}_{nou}\)\(\leftarrow\)\(\mathbf{p}_{n}\);
2. while\(\mathbf{p}_{nou}\)\(\
eq\)\(\mathbf{p}_{1}\)
3. \(\mathbf{p}_{next}\)\(\leftarrow\)\(\mathbf{p}_{nou-1}\);
4. \(M_{V}\)\(\leftarrow\)\(\mathbf{p}_{next}\), \(\mathbf{p}_{nou}\); //Update \(M_{V}\)
5. if\(\parallel\)\(M_{E}\)\({}^{\circ}M_{V}\parallel\) \(\geq\) 1 //Identify collision
6. \(\mathbf{p}_{next}\)\(\leftarrow\)\(\mathbf{p}_{next-1}\); //Update the current node
7. else
8. \(\mathbf{p}_{nou}\)\(\leftarrow\)\(\mathbf{p}_{next}\); //Update the current node
9. \(\mathbf{P}_{S}\)\(\leftarrow\)\(\mathbf{p}_{nou}\); //Update simplified path
10. end if
11. return\(\mathbf{P}_{S}\)
```  
**Algorithm 2** Path simplification  
Figure 5: An example of \(\mathbf{\Gamma}\): (a) RRT-Tree; (b) \(\mathbf{P}_{R}\).  
Figure 6: An example of \(\mathbf{P}_{S}\).
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 5 Results
### Individual Action Rules
#### 5.2.1 Illustrating Vertical Correlation Method Combinations
It shows that if our division is in parts, the accuracy of order processing (OA) changes from high to very high, the communication effectiveness within the dealer network (DC) changes from high to very high, and the likelihood of customer repeat business (RC) changes from high to very high, it implies a change in our promoter status from a passive to a promoter. This rule had a support of 11 and a confidence of 0.755._  
\[ \begin{split}(Division,\text{parts})\wedge(OA,\text{high} \rightarrow\text{very high})\wedge(DC,\text{high}\rightarrow\text{very high})\\ \wedge(RC,\text{high}\rightarrow\text{very high})\\ \Rightarrow(PromoterStatus,\text{Passive}& \rightarrow\text{Promoter})\end{split}  \tag{19}\]  
The rules that were combined to form the above one are shown in Equations (20) and (21). The first one describes that if our division is in parts and we change both OA and DC from high to very high, we then infer an increase in promoter status from passive to promoter. This rule had a support of 17 and a confidence of 0.507. The second rule states that if our division is in parts and we increase RC from high to very high, we also imply an improvement in promoter status from passive to promoter, with a support of 39 and confidence of 0.721. \[ \begin{split}(Division,\text{parts})\wedge(OA,\text{high} \rightarrow\text{very high})\wedge(DC,\text{high}\rightarrow\text{very high})\\ \Rightarrow(PromoterStatus,\text{Passive}& \rightarrow\text{Promoter})\end{split}  \tag{20}\]  
\[ \begin{split}(Division,\text{parts})\wedge(RC,\text{high} \rightarrow\text{very high})\\ \Rightarrow(PromoterStatus,\text{Passive}& \rightarrow\text{Promoter})\end{split}  \tag{21}\]  
Here, we delved into the application of the Vertical Correlation Method to generate actionable insights through synthesis of action rules. By using a confidence threshold of 50% and support threshold of two, we specifically explored the patterns emerging at level 5 of the dendrogram within Dataset C. Through this, we provided specific instances of how action rules were generated for different clusters of flexible attributes and then combined together. These insights help contribute to a deeper understanding of the interconnections within the dataset, revealing potential strategies for enhancing the PromoterStatus transitions.
## 3 Results and Discussion
### Gold Adsorption from Chloride Media
#### 3.3.2 Influence of Temperature  
Experiments were performed to investigate the effect of this variable on gold(III) adsorption with an aqueous solution containing 0.005 g L\({}^{-1}\) gold(III) at 0.1 M HCl and a carbon nanotubes dosage of 0.25 g L\({}^{-1}\). From the results obtained, it is shown that the increase of the temperature from 20 to 60 \({}^{\circ}\)C had no effect on gold adsorption, reaching 92 % after 30 min, in any case. However, the time to reach this maximum adsorption is temperature dependent (Figure 5).  
Figure 4: Influence of the stirring speed on gold(III) adsorption. Aqueous solution: 0.005 g L\({}^{-1}\) Au(III) in 0.1 M HCl. Adsorbent dosage: 0.25 g L\({}^{-1}\). Time: 2 h.  
Figure 5: Influence of temperature on gold(III) adsorption. The F values were calculated as in Equation (3).
## 4 Simulation Results
### Evaluation of Engineering Design Problems
#### 4.3.1 Engineering Design 1: WBD Problem
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cost** & **CBOA [62]** & **BWOA [62]** & **ATOA [62]** & **AOSA [62]** & **AEO [62]** & **FAEO [62]** & **MGO** &
\begin{tabular}{c} **Proposed** \\ **IMGO** \\ \end{tabular} \\ \hline Best & 1.659462 & 1.819376 & 1.844511 & 1.647761 & 1.63528 & 1.63482 & 1.636145 & 1.634443 \\ \hline Mean & \(5.42\times 10^{19}\) & 3.924915 & \(4.63\times 10^{19}\) & \(1.94\times 10^{21}\) & 1.912441 & 1.642185 & 1.666251 & 1.634564 \\ \hline Worst & \(1.19\times 10^{21}\) & 8.354898 & \(8.14\times 10^{20}\) & \(2.03\times 10^{22}\) & 3.536838 & 1.664656 & 1.713294 & 1.638706 \\ \hline Standard deviation & \(2.03\times 10^{20}\) & 1.526337 & \(1.46\times 10^{20}\) & \(4.73\times 10^{21}\) & 0.415916 & 0.006752 & 0.017604 & 0.000606 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical results of IMGO, MGO, and the other reported results for the WBD problem. achieving the lowest best cost for the WBD problem. This positions IMGO as the most effective method among the compared algorithms, showcasing its ability to find highly optimized solutions where other methods fall short.
## 7 Current Challenges and Future Research Directions
### Controller Overhead  
Due to the lack of a secure and stable SDN controller, implementing an LDDoS attack detection method based on machine learning using a single controller may overwhelm the controller, reducing its working efficiency. However, this single controller can be a point of failure for the entire network. On the other hand, the implementation of multiple controllers and distributed defense solutions can be a better choice for future defense plans as it can distribute the load between different devices and load balancing can be achieved according to the requirements. There is an urgent need from the research community to provide a light machine based on machine learning that can be implemented in multiple controller scenarios while keeping synchronization and communication to a minimum.
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results  
### Dataset  
For this study, we used the publicly available data provided by [PERSON] et al. [14], which contains information on drug-protein and protein-protein interactions as well as drug-drug and single drug side effects. We selected 284 drugs that had at least one target protein and 19,089 proteins that were either related to these drugs or interacted with at least one other protein; this is because information on the relationship between each drug and protein is essential for learning our proposed model. After preprocessing to remove duplicates from the data, we obtained 18,690 drug-protein and 715,612 protein-protein interactions. Drug-drug interactions were not utilized for constructing the graph. Thus, the resulting knowledge graph included single drugs and proteins as nodes and protein-protein and drug-protein interactions as edges. For the labeled data of multidrug combinations, we used 14,247 pairs of previously selected 284 drugs, in which each drug pair was labeled with 1308 side effects. For single drug data, only 244 of 284 drugs were labeled with 1308 side effects.