esa-sceva/satcom-chunk-collection huggingface dataset:
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 4 Simulation Experiments and Result Analysis
### Optimization Results  
To evaluate the optimization ability of various intelligent optimization algorithms on BP neural networks, we optimized the initial weights and biases of the networks using the eight comparison algorithms mentioned above. The population size, number of iterations, and limits of the solution space for all eight algorithms were controlled to be the same as ESO above. The fitness function of the optimization problem was selected as the RMSE of the BP neural network on the training set. The convergence curves of the nine algorithms are depicted in Figure 9.  
In Figure 9, all nine algorithms improved the accuracy of BP neural networks. However, BWO and SSA performed as the worst and the second worst, respectively, in the simulation test of the benchmark functions F18 and F30. Correspondingly, BWO performed the worst in optimizing the initial weights and biases in FD003 and FD004, while SSA performed the second worst in FD001 and FD003. Furthermore, ESO demonstrated a faster and more precise convergence than SO in all four sub-datasets. The performance indicators in simulation and practice were consistent with each other. These findings lead to three conclusions: 1. Using intelligent optimization to initialize the weights and biases of the BP neural network is an effective method for improving model prediction accuracy;  
Figure 9: Convergence curves of nine algorithms optimizing BP neural networks for all four sub-datasets. **(a)** Convergence curves for FD001; **(b)** convergence curves forFD002; **(c)** convergence curves for FD003; and **(d)** convergence curves for FD004. The \(x\)-axis in the figures corresponds to the number of algorithm iterations, while the \(y\)-axis represents the root mean square error.  
2. The more suitable the weights and biases found by initialization methods, the more accurate the prediction performed by BP neural networks; and 3. ESO, enhanced by the elite-guided strategy and reverse learning mechanism, has both theoretical and practical performance improvements.  
Figure 9 shows the convergence curves of DE, PSO, GWO, SSA, BWO, DBO, CSO, SO, and ESO in optimizing the fitting of BP neural networks to four different sub-datasets. After 100 iterations, ESO achieved a 7.356% reduction in the RMSE of the BP neural network on the FD001 sub-dataset. This result demonstrates that ESO can enhance the prediction accuracy of the BP neural network for the RHI prediction problem in turbofan engines within a relatively small number of iterations. Compared to the other eight mainstream optimization algorithms, ESO converged to smaller fitness values. This indicates that ESO is more suitable for optimizing the BP neural network on turbofan engine RHI prediction.
## 3 Proposed Iterative Fractional Doppler Shift and Channel Estimation Algorithm
Based on all the estimated coefficients and fractional Doppler shifts of \(\hat{P}\) paths, the estimated channel coefficient \(\hat{h}(\tau,v)\) can be reconstructed as follows:  
\[\hat{h}(\tau,v)=\sum_{i=1}^{\hat{p}}\hat{h}_{i}\delta(\tau-\frac{\hat{l}_{i}}{ M\Delta f})\delta(v-\frac{\hat{k}_{i}+\hat{k}_{i}}{NT}) \tag{18}\]  
where \(\delta(\cdot)\) represents the impulse function. The estimation process is described in Algorithm 1. ```
1:input: \((\hat{l}_{i},\hat{k}_{i})\), threshold \(\beta\), frequency compensation range \(v_{f}\in[-1,1]\), received time-domain signal \(\mathbf{r_{t}}(\hat{l}_{i})\)
2:output: \(\hat{\kappa_{i}}\)\(\hat{h}_{i}\)
3:initialize: \(E=\emptyset\)
4:for each \((\hat{l}_{i},\hat{k}_{i})\) do:
5:if\(\underset{(\hat{l}_{i},\hat{k}_{i})}{\arg\max}|\mathbf{Y}_{\text{D}}^{\textit{pilot (\textbf{a})}}(l,k)|<\beta\left(\frac{1}{MN}\sum_{l=0}^{M-1}\sum_{k=0}^{N-1}| Y_{dd}(l,k)|\right)\) then
6:for each \(v_{f}\), step=0.01, do
7:\(\hat{\gamma}_{dd}^{pilot}(\hat{l}_{i},\hat{k}_{i})=\sum_{l=0}^{N-1}\mathbf{r_{t }}(\hat{l}_{i})e^{i2\pi\frac{|\hat{l}_{i}-v_{f}|}{N}}\)
8:\(E=\left[E_{i}|\hat{\gamma}_{dd}^{pilot}(\hat{l}_{i},\hat{k}_{i})|\right]\)
9:end for
10:\(\hat{k}_{i}=\underset{v_{f}}{\arg\max}\,E\)
11:\(\hat{h}_{i}=\frac{\hat{\gamma}_{dd}^{pilot}(\hat{l}_{i},\hat{k}_{i})}{\chi_{dd} ^{pilot}(\hat{l}_{i},\hat{p}_{i})e^{i2\pi\frac{m_{p}\hat{k}_{i}}{MN}}}\)
12: Recalculate the interference-canceled signal through (16)
13:else
14: break
15: end if
16:end for
```  
**Algorithm 1** Proposed Channel Estimation  
The complexity of the proposed algorithm is mainly derived from the searching and modulus operations, assuming the estimated paths are \(\hat{P}\), and the number of search iterations is \(Q\). The complexity of converting time-domain signals into DD domain signals is \(O(MNlog_{2}(MN))\). The total number of complex addition and multiplication operations of the proposed algorithm is \(O(\hat{P}(QN+Q))\). Therefore, the overall complexity of the proposed algorithm is \(O(MNlog_{2}(MN)+\hat{P}(QN+Q))\).
## Materials and Methods
### Preparation of Standard Solutions  
Individual primary stock solutions were prepared from certified standard material at a concentration of 4 mg mL\({}^{-1}\) in MeCN for EPRINO, in MeOH for BITH, CLOR, CLOS, MOR, NITROX and OXY and in DMSO for ABZ, ABZ-SO, ABZ-SO\({}_{2}\), ABZ-NH\({}_{2}\)-SO\({}_{2}\), FBZ, FBZ-SO\({}_{2}\), MONE, MONE-SO\({}_{2}\) and OXF. Stock solutions at a concentration of 2 mg mL\({}^{-1}\) were prepared in MeCN for ABA, DORA, EMA, IVER, MOXI and SEL, in MeOH for CAM, COUMA, COUMA-O, HALOX, LEV, NITROX, RAFOX, TBZ, TCB, TCB-SO, TCB-SO\({}_{2}\) and TCB-NH\({}_{2}\) and in DMSO for FLU, FLU-OH, FLU-NH\({}_{2}\), MBZ, MNZ-OH, MBZ-NH\({}_{2}\), OXI and TBZ-OH. Ten mixed intermediate solutions were prepared in MeOH as follows: WS-A containing 50 \(\upmu\) mL\({}^{-1}\) CLOR, WS-B containing 50 \(\upmu\) mL\({}^{-1}\) of ABA, EPRINO, IVER and MOXI, WS-C containing 25 \(\upmu\) mL\({}^{-1}\) DORA and NITROX, WS-D containing 25 \(\upmu\)g mL\({}^{-1}\) of EMA, MONE-SO\({}_{2}\), NICLOS and TBZ-OH, WS-E containing 25 \(\upmu\)g mL\({}^{-1}\) of CLOS, FLU-OH, FLU-NH\({}_{2}\) and RAFOX, WS-F containing 25 \(\upmu\)g mL\({}^{-1}\) of ABZ, ABZ-SO, ABZ-SO\({}_{2}\), ABZ-NH\({}_{2}\)-SO\({}_{2}\), CAM, COUMA-O, FBZ, FBZ-SO\({}_{2}\), FLU, LEV, MBZ, MBZ-OH, MBZ-NH\({}_{2}\), MOR, OXF, OXI, TBZ and TCB, WS-G containing 10\(\upmu\)g mL\({}^{-1}\) of TCB-SO, WS-H containing 10 \(\upmu\)g mL\({}^{-1}\) of TCB-SO2, WS-I containing 25 \(\upmu\)g mL\({}^{-1}\) of BITH, HALOX and OXY, and WS-J containing 25 \(\upmu\)g mL\({}^{-1}\) of MONE and COUMA.  
A set of seven mixed working calibration solutions (Calibrants 1-7) with concentration ranges of 100-2500 ng mL\({}^{-1}\), 50-2500 ng mL\({}^{-1}\), 25-1250 ng mL\({}^{-1}\), 2.5-500 ng mL\({}^{-1}\), 5-1250 ng mL\({}^{-1}\), 2.5-1250 ng mL\({}^{-1}\), 12.5-1250 ng mL\({}^{-1}\) and 12.5-500 ng mL\({}^{-1}\) were prepared in MeOH by dilution of the respective intermediate mixed working solution; WS-A to WS-F and WS-I to WS-J. For TCB-SO and TCB-SO\({}_{2}\), an intermediate calibration solution (INT-A) at a concentration of 20 ng mL\({}^{-1}\) was prepared by dilution of WS-G and WS-H. Primary stock of all deuterated and labelled internal standards, in addition to SAL, were prepared at a concentration of 1 mg mL\({}^{-1}\). These single stocks were subsequently used to prepare an intermediate IS solution in deuterated MeOH containing 200 \(\upmu\)g mL\({}^{-1}\) SEL and TCB-NH\({}_{2}\), 40 \(\upmu\)g mL\({}^{-1}\) LEVA-d\({}_{5}\), TBZ-\({}^{13}\)C\({}_{6}\) and SAL, and 20 \(\upmu\)g mL\({}^{-1}\) of all other deuterated/labelled internal standards. This intermediate IS solution was diluted 1 in 10 to give a 20/4/2 \(\upmu\)g mL\({}^{-1}\) working IS solution. All working solutions were stored at \(-\)18 \({}^{\circ}\)C or, below in glass amber vials, with equilibration to room temperature before use.
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Power Allocation in D2D Networks
#### 3.2.3 Path-Loss-Based Power Allocation  
Under the path-loss-based allocation scheme, the transmit power of the D2D transmitter is allocated based on the large-scale path-loss to its intended receiver. More precisely, the D2D pair, having larger path-loss, will be allocated a higher transmit power to complement the long transmission distance. The transmit power of the \(k\)-th D2D transmitter under this method (denoted by \(P_{D_{k}}^{pl}\)) is then computed as  
\[P_{D_{k}}^{pl}=\frac{\Omega_{d_{D_{k}}d_{k}}}{\sum\limits_{i=1}^{N}\Omega_{d_{ tri},d_{i}}}P_{\text{tot}},\forall k. \tag{4}\]  
Direct inspection of (2)-(4) makes it apparent that the transmit power of the \(k\)-th D2D transmitter can be represented as [6]  
\[P_{D_{k}}^{s}=\omega_{k}^{s}P_{\text{tot}},\ \forall k,\ s\in\{equ,ran, pl\}, \tag{5}\]  
where \(\omega_{k}^{equ}=\frac{1}{N}\), \(\omega_{k}^{ran}=\alpha_{k}\), and \(\omega_{k}^{pl}=\frac{\Omega_{d_{D_{k}}d_{k}}}{\sum\limits_{i=1}^{N}\Omega_{d_{ tri},d_{i}}}\).  
It is noted that both the centralized and decentralized power allocation can be adopted in wireless communications. For the equal power allocation, it is easy to implement in a distributed manner. As we consider a single-cell D2D underlaying cellular networks, thus the two remaining power allocation schemes can be effectively centralized implemented at the core network; then, the power will be assigned to each D2D transmitter.
## 4 Proposed Lightweight Authentication Based on Multi-Factor Hash Chain with T-OTP in VMF
### Design Principle  
The DSA authentication used with the existing VMF standard faces severe challenges, such as collision vulnerabilities of the SHA-1-based cryptographic hash function. In the CNR networks, any confidential tactical messages, such as situation reports, location reports, and reconnaissance reports, are exchanged in real-time according to the VMF standards. Therefore, VMF standards should be robust against cryptographic problems using strong cryptographic hash functions such as SHA-2, SHA-3, and LSH. In addition, it is also vital to secure inter-message operability for low-spec connected wireless terminals, such as position reporting equipment based on infantry communication devices, armored terminals in tank and helicopter types, and limited networks used in NCW-based tactical operations that change in real-time as well as adding a re-authentication procedure based on sudden unexpected military operational scenarios. The concepts of our proposed processes were improved based on the multi-factor hash chain and T-OTP proposed by [PERSON] and [PERSON]. The related authentication scheme, with multi-factor hash chain and T-OTP values that satisfy the overall requirements of the existing VMF standards, is illustrated in Figure 1.  
1. Initialization and registration phases: The OTP seed hash generation in an LSH-based multi-factor hash chain between the administrator (the military authority or the commander of squad) and the user (the connected wireless device or squad member) in CNRs.
2. Authentication and verification phases: Rapid authentication and verification with time-stamp-based clock synchronization values for T-OTP, and the limitation of the lifetime of arbitrary hash values in a hash chain that have not been used for a long time.
3. Re-authentication and revocation phases: Tactical scenario-based network regularization of authentication status and exception handling for CNRs, including wireless terminals.  
The proposed processes included three specific phases: initialization and registration, authentication and verification, and re-authentication and revocation. The scheme proposed in this study used the hash value of the LSH-based hash chain with T-OTP in the CNR networks, including wireless military devices and internal terminals. After the initial authentication, the status of the current authentication progress was reflected in a real-time value, such as the serialization of a QR code, and the participating tactical equipment nodes were updated based on static transmission rules in CNR networks in cell-planning areas. Subsequently, if an internal or external malfunctioning node or hostile node was detected and re-authentication was required, an additional authentication scheme could be easily executed at any time through clock synchronization of the hash functions in the LSH-based multi-factor hash chain of combined operation.  
Figure 1: Proposed overall authentication scheme with multi-factor hash chain and T-OTP in VMF.
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## Security and Performance Analysis
### Performance Analysis  
**Computation Cost.** The major computation in the scheme are as follows: encoding and decoding watermarks, encrypting and decrypting areas in images, and one-way function computation. However,encoding watermarks can be conducted only one time. Encryption is conducted one time for each image, and decryption is conducted one time for each instance of image browsing. Note that encryption and decryption cannot be avoided for image access control, as some contents must be encrypted for confidentiality. One-way function computation is lightweight (e.g., cryptographically secure hash function).  
**Higher Access Throughput and Less Access Delay.** The access control policies are embedded into watermarks and distributed with images, thus it is not mandatory to consult servers for corresponding areas that can be accessed. This improves the scalability of data access. Besides, the access delay is decreased due to the absence of consulting communications latency between servers and clients.  
**Efficiency.** A balance between servers and clients is preferred, instead of only relying on servers. Servers only need to attach a watermark to an image and encrypt designated areas upon data publication, which can be accomplished in a batch. Client tools only need to decode a watermark and decrypt corresponding areas. The decryption is conducted at the client side, which is much more lightweight than at the server side. The encryption and decryption are mandatory because some areas are confidential.  
**Convenience.** The deployment is convenient. Particular client tools can be deployed as middle-ware over normal image browsers. Besides, communication channels and networks are not required, which brings more convenience for accessors.  
QR codes can be used for fast generation and decoding of watermarks. It presents the advantages of large capacity, fault tolerance, easy generation, and fast decoding. Thus, the overhead of attaching and decoding watermarks is manageable.  
Table 1 compares the advantages and disadvantage between ours and existing schemes.
## 5 Results
### Energy Consumption  
While a network is running, the remaining energy of nodes will decrease over time until it becomes zero (i.e., all nodes die) because nodes need to perform various communication tasks and consume energy. The residual network energy could visually reflect the energy consumption of routing protocols. Under the same initial energy of the network, the more residual energy, the lower the energy consumption and the better the performance of the algorithm.  
In scenario 1, as shown in Figure 6a, when the residual energy is half of the initial energy, the rounds of LEACH, IEECHS-WSN, and RCH-LEACH are about 200 because of randomness when clustering. Their performance is not better than the other two algorithms, K-LEACH and DCK-LEACH. K-LEACH carries half of the total energy about 250 rounds, and the DCK-LEACH algorithm is about 300 rounds, which has the minimum energy consumption among the five algorithms.  
This is because both K-LEACH and DCK-LEACH algorithms use the K-means algorithm for clustering, so the number of nodes among clusters varies little. In addition, the nodes are evenly distributed among clusters, and as a result, their energy consumption within clusters is more uniform, and the network lifetime is significantly extended. This is shown in Figure 3.  
Based on the K-means algorithm, DCK-LEACH optimized the K-value to improve the clustering effect in randomness. Therefore, the DCK-LEACH algorithm is the best in terms of energy consumption. We can obtain the same conclusion from Figure 6b,c in scenarios 2 and 3.  
Figure 6: _Cont._
## Data processing Flow Chart
Four different approaches for correction were compared: correction using Equation (2) [24] (green), correction using Equation (3) [25] (red), correction using Equation (4) [26] (yellow) and correction based solely on vessel attitude variations (blue). The misfit histogram resulting from Equation (3) demonstrates the lowest misfit for both the horizontal components (combined x and y-components) and the vertical component (z-component).
## 3 Literature Review
### Malware Detection through Machine Learning  
In 2009, [PERSON] et al. [26] proposed PE-Miner (Portable Executable-Miner), a framework for detecting malware following the PE format (Portable Executable Format) in RAID (Redundant Array of Independent Disks). PE Miner extracts information from PE files and creates vector values. Then, a classification model was proposed through a preprocessing process. For more than 1000 malware, the classification time was reduced to 0.244 s, and a high performance of 99% detection rate was shown.  
In 2018, [PERSON] and [PERSON] [27]. proposed Ember, a machine learning model based on machine learning static analysis. Ember extracts information from PE files through static analysis and then creates vector values. A detection model was created using a LightGBM (Light Gradient Boosting Machine) model, which utilizes a boosting technique, a kind of ensemble technique. Ember showed a high detection performance of 92% for more than 800,000 malware.  
In 2020, [PERSON] et al. [28]. published a study on classifying packed malware using static analysis-based features and machine learning-based classifiers. They then created a classification model using a random forest. The study utilized a real dataset consisting of 4396 unpacked normal files, 12,647 packed normal files, and 33,681 packed malicious files. The results showed 41.84% false negatives and 7.27% false positives for the packed dataset. However, it was found to be difficult to accurately classify packed files based solely on static analysis features.
## 4 Experimental Results  
A \(k\)-fold cross-validation technique was employed to obtain the experimental results to allow the training data set to be sufficiently representative. This technique works by randomly dividing the dataset into \(k\) subsets of the same size, and in each iteration, \(k-1\) subsets are used for training the classifiers, and the remaining subset is used for validation. For each test, the incorrect classification index of the validation data set is calculated. When all the \(k\) tests are completed, the classifier's performance is estimated according to the mean error. The following results were obtained with \(k=10\), according to studies of [PERSON] [38] and [PERSON] et al. [39].  
The experimental tests were performed on the Waikato Environment for Knowledge Analysis (WEKA) software, which provides several intelligent algorithms, allowing the resolution of pattern classification problems [40]. The works of [PERSON] et al. [41], [PERSON] et al. [42], [PERSON] and [PERSON] [43] and [PERSON] et al. [44] employed this tool for pattern classification, clustering, and feature selection in problems related to motor failure diagnosis, which demonstrates the acceptance of this tool in the scientific community. In the selection of the most relevant attributes, it was used the ABC algorithm aforementioned. The following parameters were defined: an initial population of 30 bees, a maximum number of 20 iterations, bit-flip mutation type, and mutation probability of 0.01, which are default values for the computational tool WEKA.  
The MLP ANN and the C4.5 DT described in Section 2 were used in the pattern classification stage. The MLP ANN was configured with a training learning rate of 0.3, a momentum of 0.2, and a maximum number of epochs for the training of 500, which is the standard configuration of the MLP ANN available in the computational tool WEKA. For the hidden and output layers, the logistics and linear activation function were used, respectively. The number of neurons was also defined as 10 neurons for cases where all 150 DMI attributes are used as the input matrix. On the other hand, an MLP ANN with 5 neurons was used when the ABC algorithm was applied to select the most relevant characteristics. In the construction of the C4.5 DT model, binary divisions in the nominal attributes were not considered. A seed was defined to randomize the samples, and a minimum number of attributes per leaf of 2 was considered. However, to build a more simplified and generalist decision tree, the pruning method was used, with a pruning confidence factor of 0.25 and an amount of data used to reduce pruning errors of 3. Moreover, it is worth mentioning that the results presented in this section were separated by frequency inverters used as supply source of the motors since each inverter has a specific switching characteristic, according to [PERSON] et al. [45].
## 2 Antenna Design and Analysis
### Design of CP Antenna Unit  
Figure 2 gives the evolution process of the antenna design, which can be divided into four stages. Antenna 1 is a straightforward microstrip line-fed circular wide-slot antenna, and the simulation results are in Figure 3 show that antenna 1 has good impedance matching. However, the antenna has only line polarization radiation. As the L-shaped branch increases, antenna 2 and antenna 3 have CP radiation characteristics. The AR bandwidth also spreads with the increase of L-shaped branches. However, the impedance bandwidth decreases instead due to poor impedance matching. The structure proposed in this paper is shown as antenna 4, which has good impedance matching. The AR bandwidth and impedance bandwidth of the antenna are better than those of the first three antennas. The current distribution of antenna 4 is exhibited in Figure 4. The right-hand polarized  
Figure 1: Configuration of the presented antenna. (**a**) 3D view. (**b**) Top view and detail. (**c**) Side view. (**d**) Feeding network.  
radiation will appear along the +z direction, while the left-hand polarized radiation will travel in the \(-\)z direction.