esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Detection Technology for SQL Injection Research and Implementation
## 6 Conclusions  
This paper proposes a deep learning-based approach for SQL injection attack detection, addressing the challenges of diverse attack patterns and the difficulty of extracting effective features. The proposed method leverages the fusion of TF-IDF and Word2 Vec embeddings to vectorize SQL samples while preserving maximum information. It employs an improved TextCNN to automatically extract important local information from the vectorized SQL  
Figure 10: Confusion matrix of our method on different datasets.  
samples. Additionally, a Bi-LSTM network is utilized to capture the sequential information in the samples. Finally, an ATTENTION mechanism is applied to enhance the weights of important features in the samples, reducing the impact of noise and improving the detection accuracy. Finally, a SQL injection attack detection system is designed according to the proposed scheme, and comparative experiments are carried out on multiple datasets. In comparison to the effectiveness of classical machine learning methods (such as SVM, KNN, Decision Tree, NB, and RF), the experimental results show that the network structure designed in this paper to detect SQL injection attacks improves accuracy and reduces the rate of false negatives and false positives. Additionally, the timeliness of discrimination also shows good performance, with excellent comprehensive detection results.  
The main focus of this paper is to detect and evaluate traditional first-order SQL injection attacks. Due to the complexity and specific characteristics of second-order SQL injection attacks, it is currently challenging to achieve automated detection or high detection accuracy. Therefore, the next step of our research will concentrate on developing effective detection techniques for novel second-order SQL injection attacks under complex conditions. Furthermore, there exist adversarial attack methods [33] that pose challenges to the robustness of deep learning detection models. Therefore, further research is needed to investigate the robustness of the proposed methods, aiming to enhance the model's adversarial resilience and stability. This will be a crucial evaluation criterion in the future for the field of SQL injection detection.  
Conceptualization, H.S. and Q.L.; methodology, Q.L.; software, H.S.; validation, H.S. and Q.L.; formal analysis, Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62172055, U20B2045), the Natural Science Foundation of Ningxia (2022 AAC03620), the Project (2021 XD-A09).  
Not applicable, this study did not require ethical approval.  
Not applicable, this study did not involve information about other people.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data source involves private or sensitive information.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Characterization
#### 2.3.2 IR Analysis  
Figure 6 showed the FTIR spectra of PDMAEMA-CPTCD and SPDMAEMA-8. As illustrated in Figure 6a, the FTIR spectrum of PDMAEMA-CPTCD contains the characteristic band for C=O stretching (\(v=1731.0\) cm\({}^{-1}\)), C-O stretching (\(v=1271.7\) cm\({}^{-1}\)), C-N stretching (\(v=1148.6\) cm\({}^{-1}\)) and C=S stretching (\(v=1063.6\) cm\({}^{-1}\)). Moreover, there are three strong absorption peaks around 2947.8 cm\({}^{-1}\) attributed to the stretching vibrations of C-CH\({}_{3}\) and -CH\({}_{2}-\) groups. The result of FTIR indicated that PDMAEMA-CPTCD was obtained and could be used as the linear macro RAFT agent. Compared to Figure 5A, we observed the out of plane bending vibration bands of C-H in carbon-carbon double bond of DVB unit in center of stars at \(v=902.2\) and 989 cm\({}^{-1}\), and the stretching vibration band of C=S at \(v=1063.6\) cm\({}^{-1}\) in Figure 6b. These facts further confirm the formation of the PDMAEMA stars with tightly cross-linked core.
## 4 Dynamic Characteristics Analysis
### Principle of Modal Analysis  
The free vibration mode can be used to synthesize any motion of a multi-degree-of-freedom structural system. The essence of the analysis is to solve the natural frequency and mode shape of the free vibration of a discrete elastic continuum under the conditions of no damping and no external load with finite degrees of freedom. The modal coordinates of the principal modes of each order of the analytical body are made to replace the physical coordinates, the independent differential equations are decoupled, and the free vibration equations could be obtained. The basic equation of dynamics can be expressed as [21]:  
\[[M]\big{\{}\ddot{x}\big{\}}+[K]\big{\{}x\big{\}}=0 \tag{19}\]  
where \([M]\) is system quality matrix, \([K]\) is system stiffness matrix, \(\{x\}\) is node displacement matrix, \(\big{\{}\ddot{x}\big{\}}\) is the second derivative of node displacement with respect to time. Equation (19) can be regarded as the superposition of \(n\) different harmonic vibrations, and its solution is:  
\[\{x\}=\{\phi\}\sin\omega t \tag{20}\]  
where \(\{\phi\}\) is modal shape of nth order, \(\omega\) is natural frequency, and \(t\) is time variable. It can be seen from (20) that the degrees of freedom of all vibrating structures move synchronously, and only the amplitude of the analytical body structure changes, but its basic shape does not change. The characteristic equation can be obtained by substituting it into (19):  
\[([K]-\omega^{2}[M][\phi])=0 \tag{21}\]  
If there is a non-zero solution, then:  
\[\left\{\begin{array}{c}\det([K]-\omega^{2}[M])=0\\ \det([K]-\lambda[M])=0\end{array}\right.,\lambda=\omega^{2} \tag{22}\]  
where \(\lambda\) is the eigenvalue. Assuming that the elastic continuum is discretized into i groups, each group corresponds to an eigenvalue \(\lambda_{i},\omega_{i}{}^{2}\), and the determinant is 0 at only one set of eigenvalues, then (21) becomes:  
\[([K]-\omega_{i}{}^{2}[M])\{\phi_{i}\}=0,i=1,2\ldots n \tag{23}\]where \(n\) is the dimension of the stiffness matrix \([K]\), \(\lambda_{i}\) is the \(i\)th eigenvalue of the characteristic equation, \(\omega_{i}\) is the \(i\)th natural frequency corresponding to the eigenvalue, \(\{\phi_{i}\}\) is the \(i\)th modal shape corresponding to the eigenvalue. The stiffness matrix and the real number matrix are real symmetric matrices, according to the mathematical orthogonality [29]:  
\[\{\phi_{i}\}^{T}[M]\big{\{}\phi_{j}\}=0,i\
eq j \tag{24}\]  
\[\{\phi_{j}\}^{T}[M]\big{\{}\phi_{j}\}=m_{j} \tag{25}\]  
\[\{\phi_{j}\}^{T}[K]\big{\{}\phi_{j}\}=0,i\
eq j \tag{26}\]  
\[\{\phi_{j}\}^{T}[K]\{\phi_{j}\}=k_{j}=\omega^{2}m_{j} \tag{27}\]  
where \(m_{i}\) is generalized mass, and \(k_{i}\) generalized stiffness. From the above formula, it can be known that any modal shape cannot be obtained by linear combination of other modes. Each natural frequency corresponds to a mode shape. The higher the order of the mode shape is, the smaller the vibration amplitude is and the smaller the deformation is.
## 3 Data  
Our study is based on an extensive dataset comprising defect records of 400 220 KV oil-immersed main transformers operating in northern China from 1994 to September 2023. This comprehensive dataset encompasses 86 distinct defect patterns, totaling 7202 records. It is crucial to note that these transformers have been consistently operating under low-load conditions, typically between 20% and 30% of their capacity, due to regulatory policies. Consequently, the recorded data predominantly reflect defects rather than operational failures.  
Each defect record in our dataset is rich in detail, containing information such as the defective component, defect mode, defect number, converter number and model, defect discovery time, equipment debugging time, and defect severity. We consider the time of defect discovery as a close approximation of the actual defect occurrence time.  
To complement this defect data, we have also compiled a comprehensive cost database. This includes various maintenance and replacement costs for the main components and their subcomponents, encompassing labor costs, material costs, mechanical costs, and spare parts costs for the main body, cooling system, and other major components. These costs have been adjusted for inflation to reflect current maintenance costs, allowing us to calculate accurate preventive maintenance, corrective maintenance, and overhaul costs.  
In our analysis, we observed that for the same defective component, the cost of corrective maintenance remains relatively constant across different defect modes. To align with the actual maintenance policies and requirements of power grid companies, we have integrated different defect modes for each defective component. Furthermore, recognizing the significant impact of defect severity on maintenance costs, we have further categorized and statistically analyzed these integrated defect patterns based on the severity of each defective component. This severity classification follows a three-tier system: \"normal\", \"severe\", and \"critical\" [36]. This refined approach to data analysis and categorization allows for a more nuanced and practical application of our maintenance optimization model, taking into account both the technical aspects of defects and the economic realities of maintenance operations.  
While our analysis is based on data from power transformers in northern China, it is important to emphasize that this approach serves primarily to validate the effectiveness of our method and provide a more intuitive description of the calculation steps. The data we have utilized represents the fundamental information typically collected by power companies of various scales across different countries. Therefore, the methods and findings presented in this paper have broad applicability for optimizing maintenance strategies of power transformers globally.
## 2 Active Suspension Systems and Control Systems
### Genetic Algorithm  
Due to the orders of magnitude and units of the three performance indexes of suspension being different, it needs a normalization comparison. The root mean square (RMS) values of three active suspension performance indexes are divided by their corresponding RMS value of the passive suspension and then added together as a fitness function of GA:  
\[\text{min}L=\frac{rmsBA_{a}}{rmsBA_{p}}+\frac{rmsSWS_{a}}{rmsSWS_{p}}+\frac{ rmsDTD_{a}}{rmsDTD_{p}} \tag{13}\]  
where \(rmsBA_{a}\), \(rmsSWS_{a}\), \(rmsDTD_{a}\) are the RMS values of BA, SWS, DTD of active suspension; \(rmsBA_{p}\), \(rmsSWS_{p}\), \(rmsDTD_{p}\) are the RMS values of BA, SWS, DTD of passive suspension.  
The constraint conditions are:  
\[s.t.\left\{\begin{array}{l}rmsBA_{a}<rmsBA_{p}\\ rmsSWS_{a}<rmsSWS_{p}\\ rmsDTD_{a}<rmsDTD_{p}\end{array}\right. \tag{14}\]  
The optimization process of GA is shown in Figure 4. Additionally, the steps are as follows:  
1. Generate initial population of weighting coefficients;
2. The values of individuals in the population are assigned to the weighting coefficients \(q_{1}\), \(q_{2}\), and \(q_{3}\) of the LQR controller in turn. The optimal gain feedback matrix \(K\) is solved by Riccati Equation (10), and then the optimal control force \(U_{a}(t)\) is solved by Formula (12). Then the control force is acted on the active suspension model, and the RMS values of the three performance indexes are calculated;
3. On the premise that the constraint conditions (14) are satisfied, the fitness function value of each individual in the population is calculated by the Formula (13). If the termination condition is met, the GA is exited, and the optimal individual value is returned; if not, it is transferred to step 4;
4. GA continues with selection, crossover, and mutation to produce a new population and then proceeds to step 2 to repeat the cycle until the termination conditions are met and exit the cycle.  
Figure 4: GA optimization for the LQR active suspension.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 5 Model Application
### Effect of the Inclination Angle of the Anchor Cable  
Figure 18 presents the ultimate pullout capacity, lateral resistance, and end resistance variation curves according to an increase in the inclination angle of the anchor cable. It can be seen from this figure that initially, the lateral resistance gradually increased, before gradually decreasing with the increase in the inclination angle of the anchor cable, reaching a maximum of about 610.1 kN when the inclination angle was 30 degrees. However, in general, the change in the inclination angle had a relatively small impact on the ultimate lateral resistance. With the increase in the inclination angle of the anchor cable, the end resistance and ultimate pullout capacity decreased. Specifically, when the inclination angle increased from 10\({}^{\circ}\) to 50\({}^{\circ}\), the end resistance and ultimate pullout capacity decreased from 290.6 kN to 120.1 kN by about 58.7% and from 872.5 kN to 684.4 kN by about 21.6%, respectively.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 4 Ccrlb  
The CRLB gives the theoretical lower bound that unbiased estimation can achieve, and the CRLB can be given by the inverse of the Fisher information matrix (FIM).
## 3 Fault Feature Extraction Technique  
Periodic impacts between the bearing defect and the rolling element will excite the structure system resonance. The faulty bearing vibration signals present quasi-periodic or periodic damped oscillation waveform, which is filled with rich feature information [23; 24]. However, the fault-related impulse is usually covered in a wide frequency range and is disrupted by background noise and low-frequency effects. Then the typical rolling bearing fault signals can be seen as the composition of the damped oscillation component which is excited by the fault-related impulse and the noise component. As mentioned in Section 1, entropy can effectively measure and assess the signal's complexity. If the short sliding signals contain the damped oscillation segment, the corresponding entropy value will be relatively small.  
Therefore, a new method based on sparse reconstruction and MDE_rms_ is put forward to extract fault signature and damped oscillation component from noisy signal. The framework of the proposed method is displayed in Figure 1 and the detail description of the main steps are shown in the following sections.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 2 Method
### Techno-Economic Analysis of Hydrogen Production from Natural Gas  
Following the simulation of hydrogen production from natural gas using Aspen HYSYS V14, it is essential to evaluate the financial implications of the hydrogen production initiative, particularly in relation to gas flaring management and its benefits to the Nigerian government. The economic evaluation revealed the levelized cost of hydrogen (LCOH) and the project's cost-benefit ratio, with the calculations based on capital investment andoperational expenses. Currently, Nigeria has no active hydrogen projects, resulting in inadequate data on cost and installation information for equipment within the country, as well as limited research on this subject in the Nigerian context. Given the absence of data, a cost estimation method for chemical plants, created by [60], was utilized to compute the capital and operational costs. All costs, including direct, indirect, and operational expenses, were adjusted to 2025 using the Chemical Engineering Plant Index (CEPCI) and the equipment scaling formula by [61], as outlined in Equation (7).  
The CEPCI values for 2022 and 2025 are 797.6 and 800, respectively. Table 3 outlines the fundamental assumptions used in this economic analysis for research purposes.  
\[C_{A}=\left(\frac{CI_{A}}{CI_{B}}\right)\times C_{B}\times\left(\frac{SI_{A}} {SI_{B}}\right)^{n} \tag{7}\]  
where  
\(C_{A}=\) new scaled equipment cost, \(C_{B}=\) base equipment cost, \(CI_{A}=\) CEPCI current year, \(CI_{B}=\) CEPCI given year, \(SI_{A}=\) new equipment capacity, \(SI_{B}=\) old equipment capacity, and \(n=\) scaling exponent  
This research assumes that a hydrogen distribution network is available; therefore, the shipping and distribution costs of the hydrogen were not considered in the calculation.
## 3 Envelope Demodulation
### The Practical Application and Existing Problems of the Envelope Demodulation Algorithm  
The envelope spectrum analysis of vibration signals typically involves two key steps: envelope demodulation and subsequent envelope spectrum analysis [26]. Taking practical application into account, this article uses the dataset from the Bearing Data Center at Western Reserve University (CWRU) as a case study.  
In terms of extracting envelope signals, to verify the effectiveness of detecting faults in the inner ring of the bearing drive end, this article takes 105.mat in the dataset as an example at a sampling rate of 12 KHz. The waveform, spectrum, and envelope of the signal are shown in Figure 2. Figure 2a shows the waveform of the signal. Figure 2b shows the envelope waveform of the signal. Figure 2c shows the spectrum of the signal. It can be seen that the spectral components of the vibration signal are rather complex, and the intensity differences of each frequency component are relatively small, making it difficult to clearly and effectively reflect the characteristic frequencies of the fault. Therefore, accurate fault diagnosis cannot be carried out based on this spectrum. Figure 2b,d show that after envelope demodulation of the signal, its envelope spectrum presents a clear and identifiable pattern. Figure 2d reflects the characteristic frequency distribution law of the fault, providing a reliable basis for fault diagnosis.  
In terms of the spectral transformation of envelope signals, under the condition that the bearings are normal and without faults, this article takes the 97.mat file in the dataset as an example. The waveform, spectrum, and envelope of the signal are shown in Figure 3. Figure 3a shows the waveform of the signal, Figure 3b presents the envelope waveform of the signal, while Figure 3c indicates that the spectral components of the vibration signal are rather complex and have small amplitude differences, making it difficult to reflect the characteristic frequency of the fault. Fault diagnosis cannot be directly based on this result. However, if fault diagnosis is carried out based on Figure 3d, misjudgment may occur.  
Figure 1: Verification of envelope demodulation.  
A comprehensive analysis of Figures 2 and 3 reveals that the spectrum of the vibration signal cannot effectively reflect the characteristic frequency of the fault, while the envelope spectrum of the vibration signal can more accurately represent the characteristic frequency of the fault. Envelope demodulation of the vibration signal is an indispensable step. However, relying solely on envelope spectra for fault diagnosis may have limitations and is prone to misjudgment, thereby affecting the reliability of the diagnosis results.  
Figure 3: Waveform, spectrum, waveform, and envelope spectrum of envelope signal when the bearing is normal without fault (97.mat file).  
Figure 2: Waveform, spectrum of the inner circle fault at the drive end, waveform and envelope spectrum of the envelope signal (105.mat file).
## 4 Current Evidence and Applications
### Potential Applications in Fracture Oregrowth  
Applying AI to predict fracture overgrowth involves the following (Table 1):  
Predictive Modeling: Developing ML models incorporating fracture characteristics, patient-specific factors like age, sex, genetic markers, and hormonal influences to enhance predictive power [44].  
Imaging Analysis: Utilizing DL algorithms to analyze imaging studies for early signs of altered growth plate activity, such as changes in thickness or signal intensity on MRI [45].  
Monitoring Tools: Implementing AI-driven applications or wearable devices that monitor limb growth over time, detecting deviations from expected patterns [46].  
Treatment Optimization: AI assists in selecting appropriate intervention strategies based on predicted risk, guiding clinicians in tailoring monitoring and interventions [36].
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 3 Results
### Ablation Experiments  
To assess the contribution of each core module in our bird species recognition model, we performed ablation experiments. This involved removing each module individually and retraining the modified model. Table 1 summarizes the results.  
We first conducted an experiment to assess the impact of multi-scale feature fusion by removing this module from the proposed model and training the modified model from scratch. The results showed that the accuracy decreased from 91.3% to 89.6% without this module, indicating the crucial role of capturing multi-scale information in bird images. Additionally, we observed a decline in F1 score from 0.906 to 0.871, further highlighting the importance of multi-scale feature fusion in the proposed model.  
Next, we evaluated the effectiveness of attention feature enhancement by removing this module and training the modified model from scratch. As shown in Table 1, the removal of attention feature enhancement resulted in a decrease in both accuracy, from 91.3% to 85.2%, and F1 score, from 0.906 to 0.848. These results indicate that the feature enhancement module enhances important features for bird classification.  
Finally, we conducted an experiment to evaluate the importance of siamese network training by training a modified model from scratch without this module. The results in Table 1 indicate a significant decrease in both accuracy, from 91.3% to 86.9%, and F1 score, from 0.906 to 0.862, upon removing the siamese network training module. These findings underscore the importance of exploring the interaction between features and optimizing the model during training through the use of the siamese network.