esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), \(k\)-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models' verification. Root-mean-squared error (RMSE), determination coefficient (R\({}^{2}\)), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.  
titanium placer; beach placer; ilmenite content; artificial intelligence; applied soft computing 2020 10, 635; doi:10.3390/app10020635
## 4 Methodology
### Packets as Images  
In order to convert raw packet data into bitmap images for 2D-CNN analysis, we extract individual packets from packet traces and append the IP payload of each packet to a predefined bitmap file header, which can be edited independently of the packet data to accommodate different bitmap features such as changing the image dimensions or color depth. Research into ideal bitmap formats is ongoing, but presently we use a 24-bit image of 24 pixels wide. Thus, each row of the image can store 72 bytes of data.  
Due to the design specifications of the bitmap file format, image width must contain data in multiples of four bytes, but image height can be arbitrary. We leverage this to allow for the inclusion of several packets in the same image by tiling them vertically. This allows for packets in the same flow to be analyzed together by the model.  
For the commonly used Ethernet frame payloads of 1500-byte packets, the bitmaps are thus 24 \(\times\) 21 pixels (1512 bytes) for one packet, or 24 \(\times\) 42 pixels for two packets. Any pixels not filled with packet data are set to all zeroes.  
This method is particularly useful because the bitmap file header essentially provides a mapping to the pixel data. Any data can be appended to such a header, and it can be read as pixel data according to the specifications contained within the header. The data do not need to be processed in any way to allow for this conversion.
## 2 Related Works
### Mp-WFRFT  
MP-WFRFT, known as a variation of the Fourier transform, is essentially the weighted sum of four basis functions. MP-WFRFT can be regarded as the process of rotating the signal in the time-frequency plane to realize the time-frequency redistribution of signal power. Only when the users rotate the signal in the same angle but the opposite direction can the signal power be concentrated. The representation of the 4-WFRFT of discrete sequences was proposed in [27] and has been widely employed in communication systems. Therefore, the classical 4-MP-WFRFT approach is adopted in this paper. For an arbitrary complex sequence \(\mathbf{x}=[x_{1},x_{2},\cdots,x_{Q}]^{\mathrm{T}}\), the corresponding MP-WFRFT can be defined as  
\[\mathfrak{F}_{\alpha}^{(\mathbf{m},\mathbf{c})}(\mathbf{x})=\omega_{0} \mathbf{x}+\omega_{1}\mathcal{F}(\mathbf{x})+\omega_{2}\mathcal{F}^{2}( \mathbf{x})+\omega_{3}\mathcal{F}^{3}(\mathbf{x}), \tag{1}\]  
where \(\mathfrak{F}\) denotes the transform operator of MP-WFRFT; \(\mathcal{F}\), \(\mathcal{F}^{2}\), and \(\mathcal{F}^{3}\) denote 1-3 times discrete Fourier transform (DFT) of sequence \(\mathbf{x}\), respectively; \(\alpha\) is the MP-WFRFT modulation order; \(\mathbf{m}=[m_{0},m_{1},m_{2},m_{3}]^{\mathrm{T}}\) and \(\mathbf{c}=[c_{0},c_{1},c_{2},c_{3}]^{\mathrm{T}}\) are integer vectors; and the weighting coefficient \(\omega_{l}(l=0,1,2,3)\) is defined as  
\[\omega_{l}=\frac{1}{4}\sum_{k=0}^{3}\exp\{\frac{2\pi j}{4}[\alpha(4m_{k}+1)(4c _{k}+k)-lk]\}, \tag{2}\]  
Based on the principles of transform operator \(\mathcal{F}\), \(\mathcal{F}^{2}(\mathbf{x})=\mathbf{P}\mathbf{x}\), \(\mathcal{F}^{3}(\mathbf{x})=\mathbf{P}\mathcal{F}(\mathbf{x})\), where \(\mathbf{P}\) is the \(Q\times Q\) reverse matrix. Therefore, (2) can be expressed as  
\[\mathfrak{F}_{\alpha}^{(\mathbf{m},\mathbf{c})}(\mathbf{x})=\omega_{0} \mathbf{x}+\omega_{1}\mathcal{F}(\mathbf{x})+\omega_{2}\mathbf{P}\mathbf{x}+ \omega_{3}\mathbf{P}\mathcal{F}(\mathbf{x}), \tag{3}\]  
Furthermore, MP-WFRFT satisfies additive property, which can be expressed as  
\[\mathfrak{F}_{\alpha+\beta}^{(\mathbf{m},\mathbf{c})}(\mathbf{x})=\mathfrak{F }_{\alpha}^{(\mathbf{m},\mathbf{c})}[\mathfrak{F}_{\beta}^{(\mathbf{m}, \mathbf{c})}(\mathbf{x})], \tag{4}\]  
where \(\alpha,\beta\in\mathbb{R}\) are real numbers.  
The implementation of the MP-WFRFT operation is demonstrated in Figure 1. It is obvious that the MP-WFRFT can be quickly realized by means of an inversion module and DFT module. Furthermore, from (4), the original sequence for 4-MP-WFRFT can be easily recovered only under the premise that the WFRFT parameter \(\alpha\) is substituted with \(-\alpha\), i.e., \(\mathbf{x}=\mathfrak{F}_{-\alpha}^{(\mathbf{m},\mathbf{c})}[\mathfrak{F}_{ \alpha}^{(\mathbf{m},\mathbf{c})}(\mathbf{x})]\). Therefore, the MP-WFRFT technique provides encryption security with parameters \((\alpha,\mathbf{m},\mathbf{c})\). LUs can decode confidential messages by using perfect MP-WFRFT parameters, and Eves only receive a distorted signal that is equivalent to noise.  
Figure 1: The implementation of the MP-WFRFT.
## 2 Results and Discussion
### Molecular Docking Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**IJXA**} & \multicolumn{2}{c}{**1 PFV**} \\ \cline{2-5}  & **Ligand Interactions** & **Score Function** & **Ligand Interactions** & **Score Function** \\ \hline \multirow{2}{*}{**5a**} & ASP 192 (H-bonding) & \multirow{2}{*}{\(-15.33\)} & TYR 15 (Hydrophobic bonding and \\  & GLU 351 (H-bonding) & & H-bonding through solvent) & \(-12.09\) \\  & & HIS 24 (H-bonding through solvent) & & \\ \hline
**5a enantiomer** & & Failed & & Failed \\ \hline
**5i** & PRO 377 (H-bonding) & \(-13.40\) & HIS 24 (Hydrophobic bonding) & \(-7.45\) \\  & & HIS 21 (hydrophobic bonding) & & \\ \hline
**5i enantiomer** & ASP 192 (H-bonding) & \(-15.11\) & TYR 15 (H-bonding through solvent) & \(-12.51\) \\ \hline \multirow{2}{*}{**Ampicillin**} & ASP 192 (H-bonding) & \multirow{2}{*}{\(-14.83\)} & TYR 15 (H-bonding through solvent) & \multirow{2}{*}{\(-9.99\)} \\  & SER 191 (H-bonding through solvent) & & HIS 24 (H-bonding through solvent) & \\  & through solvent) & & HIS 21 (H-bonding) & \\  & ARG 173 (H-bonding) & & LYS 335 (H-bonding and salt bridge) & \\ \hline \multirow{6}{*}{**Tetracycline**} & PRO 377 (H-bonding) & \multirow{6}{*}{\(-19.71\)} & \multirow{6}{*}{\(-15.11\)} \\  & ASP 192 (H-bonding) & & & \\ \cline{1-1}  & SER 191 (H-bonding through solvent) & & & \\ \cline{1-1}  & ARG 173 (H-bonding and salt bridge) & & & \\ \cline{1-1}  & SER 379 (H-bonding) & & & \\ \cline{1-1}  & GLY 378 (H-bonding) & & & \\ \cline{1-1}  & ARG 383 (Salt bridge) & & & \\ \cline{1-1}  & ARG 26 (Salt bridge) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scoring function of docking poses and the interactions of the docked compounds **5a**, **5i**, **5i** enantiomer, Ampicillin and Tetracycline with amino acid residues on glucosamine 6-phosphate synthase (1 jxa) and methionyl-trnas-synthease (1 PFV). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **lip\_acc** & **lip\_don** & **lip\_druglike** & **Weight** \\ \cline{2-5}  & \(\leq\)**10** & \(\leq\)**5** & **=1** & \(\leq\)**500** & **lip\_violation** \\ \hline
**5a** & 6 & 3 & 1 & 438.94 & 0 \\
**enantiomer** & 6 & 3 & 1 & 422.48 & 0 \\
**5i** & 6 & 3 & 1 & 422.48 & 0 \\
**enantiomer** & 6 & 3 & 1 & 422.48 & 0 \\
**Ampicillin** & 7 & 4 & 1 & 349.41 & 0 \\
**Tetracycline** & 10 & 6 & 1 & 443.43 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the Lipinski parameters of compounds **5a**, **5i**, **5i** enantiomer, Ampicillin and Tetracycline. Figure 9: 2D-interactions of compounds **5a**, **5i**, **5i** **enantiomer**, Ampicillin and Tetracycline with amino acid residues on methionyl-trnasynthease (1 PFV).
# Coloured Petri Nets-Based Modeling and Validation of Insulin Infusion Pump Systems
## 6 Empirical Evaluation
### Scoping, Modelers, and Variables  
The goal-question-metric methodology [30] guided the definition of the empirical evaluation through analyzing the usage of the reference model into two aspects: (i) evaluation concerning productivity from the viewpoint of the modelers by instantiating the model; and (ii) evaluation for reusability from the viewpoint of the modelers by instantiating and extending the model. Therefore, we defined the following secondary RQs: does the reference model increase modelers' productivity? (RQ1), and is the reference model reusable? (RQ2). These RQs guided the specification of the following hypotheses: productivity is not increased (H0-1), productivity is increased (HA-1), the reference model is not reusable (H0-2), and the reference model is reusable (HA-2). H0-1 and HA-1 are related to RQ1 and H0-2 and HA-2 to RQ2.  
We selected 12 modelers using the convenience sampling technique and evaluated them at a federal university located in Brazil. Table 2 presents the profiles of the modelers who participated in the study, including questions about age, knowledge about formal methods, opinion about the training phase, resolution of the list of exercises, knowledge about CPN, and knowledge about the present work.  
There are two dependent variables defined based on the research goals: modelers' productivity and reference model's reusability. There are four independent variables: the reference model, the modelers' experience, the tool support, and the environment. These variables are controlled at a fixed level, meaning that each group of modelers (i.e., control and treatment) has the same reference model, experience, tool holder, and environment.  
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Id** & **Description** \\ \hline
1 & The pump runs correctly (standard infusion mode). \\ \hline
2 & The pump runs correctly (personalized infusion mode). \\ \hline
3 & The pump finished executing due to a software critical failure when the insulin doses were selected in the standard infusion mode (no dosage selected). \\ \hline
4 & The pump finished executing due to a critical software failure when the insulin doses were selected in the standard infusion mode (at least one selected). \\ \hline
5 & The pump finished executing due to a critical software failure when insulin doses are being applied in the standard infusion mode. \\ \hline
6 & The pump finished executing due to a software critical failure when the insulin doses were selected in the personalized infusion mode (no dosage selected). \\ \hline
7 & The pump finished executing due to a critical software failure when the insulin doses are being selected in the personalized infusion mode (at least one selected). \\ \hline
8 & The pump finished executing due to a critical software failure when insulin doses were being applied in the personalized infusion mode. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of simulations conducted based on abstract tests.
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 1 Introduction
### Research Contributions  
A novel segmentation method based on new deep learning technology is proposed in this article. This segmentation is performed to detect leukemia cells and categorize these cells into their suitable types, ALL and AML, due to the availability of these two types in the utilized datasets. A newly implemented UNET model is used to extract the required features for classification purposes. The major contributions are:  
1. Developing a novel segmentation process to detect leukemia based on a deep learning architecture according to a U-shaped architecture.
2. Implementing the UNET model to extract various characteristics for the categorization of the main two categories.  
* Using four datasets to evaluate the proposed approach.
* Calculating several performance quantities to evaluate the correctness and robustness of the presented algorithm.
* The rest of the article is organized as follows: a literature review is presented in Sections 1.3 and 2 provides a description of the proposed method. Section 3 contains the evaluated performance metrics, their results, and the details of the conducted comparative analysis with some developed methods, and Section 4 provides a discussion. The conclusion is given in Section 5.
# Design of a Reinforcement Learning-Based Lane Keeping Planning Agent for Automated Vehicles
## 3 Methodology
### Operation of the Algorithms  
For clarification, MCTS can be considered as a model-based search algorithm that operates as follows. In every given state, a search tree is built with a fixed depth, which is determined by the number of executed iterations. After the iterations, the search tree suggests an action according to the utilized action selection policy that seems to be the best over the limited horizon, then the suggested action is executed by the environment, and the process repeats itself in the same way in every step.  
The new algorithms operation is very similar to pure MCTS. The difference is that these new algorithms utilize the trained PG agent inside the selection procedure through the reformulation of the UCT-t algorithm. As it makes the selection more effective, it enables the back scaling of the planning time, which results in an algorithm that maintains real-time applicability on an enhanced performance level.  
Compared to that, the original RL and SL algorithms do not use any additional techniques; therefore, the action selection is made by the neural network's feedforward function called prediction.  
Algorithm 1 presents the actual operation of the proposed Planning agents. There are only two differences between the proposed algorithms. The first is the value assignment, which is a prediction with PG agent for Planner-I and a rollout for Planner-II, and the second is the calculation of the node's UCT value. For further clarification Figure 5 displays the operation of the new algorithms from another aspect.  
Figure 5: The operation of the planning agents.  
```
1:procedureControl(RootNode, Agent, IterationCnt)
2:for i in IterationCnt do
3: Iteration(RootNode, Agent) \(\triangleright\) Run modified MCTS
4:endfor
5:Action=\(argmax\)(RootNode.Children.Visit) \(\triangleright\) Choose the most visited node
6:endprocedureIteration(Node, Agent)
7: Node.Visit += 1
8:ifNode.Reward == -1 Or Node.Depth == DepthLimit then\(\triangleright\) Terminal node Case
9:return Node.Reward
10:else if Node.Visit == 0 then\(\triangleright\) Unvisited node Case
11:for i in Actions do
12: Node.Children[i].Create(Node, Action)
13:endfor
14:if Planner-I then\(\triangleright\) Predict, if using Planner-I
15: Node.Value=Agent.Predict(Node)
16:else
17: Node.Value=MCTS Rollout(Node) \(\triangleright\) Rollout, if using Planner-II
18:endif
19:return Node.Value
20:else\(\triangleright\) Visited node Case
21:for child in Node.Children do
22: child.UCT0;
23:endfor
24: BestChild=Node.Children(\(argmax\)(Node.Children.UCT))
25:v=Iteration(\(BestChild, Agent\)) \(\triangleright\) Recursive Call
26: Node.Value+=v
27:return v
28:endif
29:endprocedure
```  
**Algorithm 1** Planning agent-based control.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 2 Narrowband IoT Fundamentals
### Receiver Sensitivity, ECL, MCL, and Transmission Power Control in NB-IoT
The MCL is thus calculated as:  
\[MCL=P_{TX}-P_{RX,min} \tag{19}\]  
Table 2 shows a reference link budget configuration with the uplink \(N_{Rep,UL}\) = 32 and the downlink \(N_{Rep,DL}\) = 128 achieving the minimum requirement of MCL = 164 dB as defined by the International Mobile Telecommunications 2020 (IMT-2020) standard [19], which specifies the requirements for upcoming 5G systems.
## 2 Materials and Methods
### Information Fusion in Tree-Based IOT Platform Using Proposed Method
#### 2.9.2 Method of Performance Evaluation  
The proposed statistical information fusion approach is tested on a tree network using OMNeT++ simulations. Simulation results were exported to MATLAB, as in the case of chain topology for graphical analysis. In these simulations, we analyze the improvements in the data accuracy and the network life of the proposed method compared to AVG and EBDKF data fusion methods. We consider a 300 m \(\times\) 300 m square region where sensor nodes are randomly deployed. Here, we use a protocol in which the tree-based WSN is built by using the distance between the neighbors as the cost function to ultimately join to the fusion center. That is a given node will connect to another node which has the least distance with the given node. This protocol will maximize the inter-node communication than Minimum Spanning Tree \((MST)\) protocol which minimize the total distance between a given node and the fusion center rather than minimizing inter-node distance. Therefore, this protocol will be more reliable even though the total cost of transmission might be expensive than the MST protocol. This protocol will enhance more child nodes to be connected to an aggregating node than the MST protocol where the average number of child nodes per aggregating node is one. As our intention is to study the accuracy, complexity, and network lifetime when aggregating nodes have more children, i.e., effect of parallel data aggregation along with serial data aggregation, we use the previously said minimum neighbor distance tree without using MST protocol. Therefore this network structure is a more practical and generalized structure due to random placement of sensor nodes and having different inter-node distances than the previously discussed chain network. Here, the Euclidean distance among sensor node is used as the cost function. The sink node is placed at the (150, 150) coordinate which is the center of the square region. Unless otherwise specified, all the other parameters of the network are similar to the parameters specified in the chain-based network simulations except the link length and BER. In chain-based network, we assumed that the nodes are placed in equal distances so that the BER s of all the wireless links are the same. However, in this tree structure resembling a more practical sensor network used in SG environment, since a minimum neighbor distance connection method was used, the link distance varies between different node pairs making the BER of links different according to Equation (29).  
Information Fusion in Randomly Placed Non-Hierarchical Bidirectional Graph Based IOT Platform Using Proposed Method
## 5 Results and Discussion
### Results of 33-Bus Radial Distribution System
The VSI without DG is 0.7181 which is improved to be 0.7861 with DG integration. For IPSO algorithm (case 3), the total power loss is 131.3 kW with DG compared to be 158.8 kW without DG with 27.5 kW differences, with 13.57% TPL reduction. The VSI without DG is 0.7216 which is improved to be 0.8133 with DG integration. Finally, For IEPSO algorithm (case 4), the total power loss is 129 kW with DG compared to 155.5 kW without DG with 26.5 kW differences, with 13.08% TPL reduction. The VSI without DG is 0.7333 which is improved to be 0.8236 with DG integration. As for heavy load in 33-bus test system, the total power loss obtained from the PSO algorithm (case 2) with DG is 384.5 kW compared to 391 kW without DG with 6.5 kW differences, resulted in 1.31% TPL reduction. The VSI without DG is 0.6618 which is improved to be 0.6687 with DG integration. For IPSO algorithm (case 3), the total power loss is 309.8 kW with DG compared to 342.9 kW without DG with 33.1 kW differences, with 6.67% TPL reduction. The VSI without DG is 0.6718 which is improved to be 0.67% TPL reduction. The VSI without DG is 0.6718 which is improved to be 0.67% TPL reduction. The VSI without DG is 0.6718 which is improved to be 0.67159 with DG integration.
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 4 Conclusions  
There are 13,324 aircraft in the test set, and 12,215 aircraft can be detected by using the proposed method. The recall is 91.68%, the precision is 89.25%, and the accuracy is 82.56%. Experiments show that the proposed method is simple and effective for detecting aircraft objects. Especially for small samples learning, the proposed algorithm can accurately detect aircraft objects, which greatly saves costs for the research. The research on aircraft object detection has significant value for improving early warning capability and counterattack capability. However, about 9% aircraft can't be detected by using our algorithm, mainly because the center of the circle on the aircraft don't have the characteristics of four peaks and four valleys. To address this problem, in the next research, a new algorithm will be proposed to detect the remaining aircraft.  
Conceptualization, T.W.; Formal Analysis, X.Z. and Y.Z.; Investigation, C.C. and W.L.; Methodology, T.W. and B.W.; Software, Z.F.; Validation, T.W. and J.S.; Visualization, X.Y.; Writing--Review & Editing, T.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities (K5051399208) and Major Instruments of the Ministry of Science and Technology (2012 YQ12004702) and the 111 Project (B17035).  
The authors declare no conflict of interest.
## 3 Proposed Anonymization Method, Iterative _k_-Anonymous Clustering Method (I\(k\)-ACM)
### Data Representation  
Suppose input data is a table \(T\) having \(m\) attributes, \(r\) records. \(T_{ij}\), represents the \(j\)'th attribute of the \(i\)'th record where, \(\{i:1\leq i\leq r\}\) and \(\{j:1\leq j\leq m\}\). Table \(T\) is represented by a set of bit strings \(B\), where \(B_{ij}\) is bit string representation of \(j\)'th attribute of \(i\)'th record. \(k\)'th bit of \(B_{ij}\) is shown as \(B_{ij}(k)\). Suppose that \(j\)'th attribute of table is categorical and there are \(d_{j}\) distinct values. These values are indexed by \(k\) and shown as \(V_{j}(k)\)where \(\{k:1\leq k\leq d_{j}\}\). Bit string of this categorical attribute has a size of \(d_{j}\) and formed as follows:\(If\)\(T_{ij}=V_{j}(k)\)\(then\)\(B_{ij}(k)=1\)\(else\)\(B_{ij}(k)=0\)\(as\)\(\forall k:\ 0\leq k\leq d_{j}\),  
If attribute is numerical, the range of attribute is divided into equal-sized intervals. Assume that \(j\)'th attribute is numeric and attribute range is divided into \(d_{j}\) number of intervals. Each interval is indexed by \(k\). Bit string representation of this numeric attribute has a size of \(d_{j}\) and formed as follows:  
\(If\)\(T_{ij}\)\(intersects\)\(with\)\(k^{\prime}th\)\(interval,\ then\)\(B_{ij}(k)=1\)\(else\)\(B_{ij}(k)=0\)\(as\)\(\forall k:\ 0\leq k\leq d_{j}\)  
Any attribute of original data before anonymization process has only one true bit in its bit string. However, the number of true bits increase due to the information loss during anonymization.