esa-sceva/satcom-chunk-collection huggingface dataset:
# BlackWatch: Increasing Attack Awareness within Web Applications
## 2 Background
### AppSensor  
Although several commercial and open source WAFs are available [22; 23], and self-healing WAFs have been explored [24], there are alternative methods of enhancing web applications thus increasing attack awareness. This research focuses on the approach of identifying malicious behavior from within the context of a web application.  
In 2008 the Open Web Application Security Project (OWASP) began working on a project called AppSensor [25]. AppSensor is an open source project originally developed by [PERSON]. The project was developed with the concept of being able to identify attacks from within the context of an application. If a developer is aware of the legitimate usage of their application, then they can confidently identify illegitimate and unintended usage. In 2017, OWASP stated that _\"The future of application defense is a system that can understand custom attacks against an application, correlate them against a malicious attacker, and react in real time to contain and eliminate the threat\"_[25]. While the AppSensor project provides its own solution that can be adopted within a security environment, the main focus of this project is more to broadcast the idea of this type of attack detection. It has been stated that _\"the idea behind OWASP AppSensor project is that it is not a product, like a WAF would be, but rather an idea with a reference implementation in a Java framework\"_[26].  
The objective of [PERSON]'s work was to provide a comparison of the detection capability offered by WAFs, intrusion detection systems, and AppSensor. Throughout the investigation,a variety of attacks based on the top ten most common web application vulnerabilities [27] were used to determine the effectiveness of each solution. The results demonstrated that the WAF used--ModSecurity--provided satisfactory attack detection coverage; however, AppSensor proved a greater rate of attack detection.  
With the concern for security growing throughout the cyber community, it was apparent there was a need for new security solutions and approaches to be introduced. In 2017 OWASP released an up-to-date list of the most common web application vulnerabilities, number ten on that list being insufficient logging and monitoring--_\"exploitation of insufficient logging and monitoring is the bedrock of nearly every major incident\"_[28]. With this issue being widely recognized within the industry, web application developers need to adopt more proactive methods for identifying attacks against their web applications.  
BlackWatch seeks to address this issue, exploring how to improve web application security by identifying malicious behavior from within the context of a web application. The following section will detail the methodology behind creating and evaluating the solution.
## Nomenclature  
\begin{tabular}{l l} \(i\), \(N_{i}\) & Index of BESSs and the neighbor set of BESS \(i\) \\ \(G\), \(D\) & The topology of communication and its degree matrix \\ \(L\), \(A\) & Laplacian matrix and adjacency matrix of the communication graph \\ \(a_{ij}\) & The \((i,j)\)-th element of \(A\) \\ \(\dfrac{g}{P_{i}}\), \(\overline{Q_{i}}\), \(\omega_{i}\), \(V_{i}\), \(E_{i}\) & The active/reactive power, frequency, voltage and energy level of BESS \(i\) \\ \(\omega_{i}^{0}\), \(V_{i}^{0}\) & The rated frequency and voltage of BESS \(i\) \\ \(V^{\prime}\), \(a^{\prime}\) & The reference frequency and voltage \\ \(u_{i}^{\omega}\), \(u_{i}^{p}\), \(u_{i}^{E}\), \(u_{i}^{V}\), \(u_{i}^{Q}\) & The designed input of BESS \(i\) \\ \(K_{i}^{p}\), \(K_{i}^{Q}\) & The coefficient of droop controller for BESS \(i\) \\ \(\tau_{ij}\) & The delay time from BESS \(j\) to \(i\) \\ \(A^{\tau}\) & The adjacency matrix of the communication graph with delay time \\ \end{tabular}
## 3 Optimization
### Optimization of the Soft Cascaded Classifier
Although the general performance of the largest cascade exceeds that of all others, the classifier consisting of 152 weak classifiers yields the lowest FPR up to a TPR of 96.2%. The findings in Figure 5(c) are also supported by the results in Figure 7. The evaluation up to 150 weak classifiers shows that, in general, the accuracy of the classifier increases. If the classifier contains 152 weak classifiers, it reaches an accuracy of 98 % on the validation set. Above 150 weak classifiers, the accuracy of the classifier decreases slightly on the validation set. In contrast, the accuracy increases slightly on the training set. This gives rise to suggesting a slight overfitting. Based on these findings, we conclusively train a soft cascaded classifier for the application in our approach to the monitoring of workers on construction sites. We provide a pool of feature descriptors consisting of Haar-like features and five-bin color histograms. We add feature descriptors beginning with a size corresponding to 10% of the samples' sizes and iteratively increase their sizes by further 10% up to a size of 100% of the samples' sizes. For training, we initially provide 800 samples, each positive and negative, randomly chosen from our training dataset. We then let AdaBoost choose 152 weak classifiers during training which classify the set of training samples best. This halves the time required for the later classification process while retaining an acceptable detection rate. After calibrating this soft cascadedclassifier and integrating it into our classical detection setup, we are able to process images at about 28 fps, which even exceeds the frame rate of the camera used for our experiments. Figure 6: ROC curves indicating the effect of different hyperparameters on the detection quality.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 3 Results
### Temporal Analysis of the Post-Fire Vegetation Trajectory
Dwarf shrubs reached biomass saturation within a relatively short period (5-7 years). Therefore, the rapid initial EVI increase was mainly caused by the regeneration of dwarf shrubs and understory grasses that survived the fire. The differences in the EVI were very large between the different fire damage intensities. We estimated the mean EVI recovery period for different fire intensities (approximately 10, 12 and 16 years). The recovery time of the LST was one year earlier than that of the EVI under different fire intensities. Figure 5 shows that the three fire intensity areas had temperature differences during the fire-occurrence years of 6.9, 3.2 and 1.5 \({}^{\circ}\)C, while the recovery period for different fire intensities  
Figure 4: Temporal trajectories of the post-fire vegetation recovery and seasonal dynamics as defined by the EVI, LST and LSWI between the control area and fire area at different burn severity levels. EVI’s annual dynamics in the control and focal pixels (**a**); EVI’s dynamics under different fire intensities (**d**); EVI’s differences between the control and focal pixels under different fire intensities (**g**); LST’s annual dynamics in the control and fire pixels (**b**); LST under different fire intensities (**e**); LST’s difference between the control and focal pixels (**h**); LSWI’s annual dynamics in the control and focal pixels (**c**); LSWI under different fire intensities annually (**f**); and LSWI’s differences between the control and focal pixels (**i**). was approximately 8, 11 and 15 years. The LSWI values in the fire-affected areas decreased by 0.13, 0.19 and 0.34. The effects of light and moderate fire intensities on the LSWI lasted 14 and 15 years, while the severe-fire area did not reach the reference area's value at the end of the study period.
## 3 Fault Classification
### Probabilistic Neural Network: PNN  
PNN is a feedforward neural network and is applied in classification and pattern recognition problems. PNN is derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. It includes three layers (input layer, radial basis layer, and competitive layer). The first layer computes the distances from the input vector to the training input vectors and produces a vector whose element indicates how close the input is to the training input. The second layer sums these contributions for each class of input to produce its net output of the second layer, which picks the maximum of these probabilities and produces a 1 for that class and a 0 for the other classes. The training process begins with a random initial weight and the spread is increased in the radial basis layer, which corresponds to a bias value (\(b=\frac{0.8326}{\textit{spread}}\)) from 0.0001 to 0.1. An increase step of 0.0001 is used to calculate the number of minimum errors. This loop process is repeated until the maximum value (0.1) is obtained or until the minimum errors are equal to zero, after which training is stopped. The training process can be summarized as a flowchart illustrated in Figure 5. The answers are shown in the form of numbers from 1 to 10, which means 10 types of fault are present. The training time for the process is approximately 97 s.
## 3 Results and Discussion
### In Silico Design of a Protein against Metabolic Syndrome
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline  & **Uniprot Entry** & **Sequence Length** & **IW** & **VW** & **YY** & **YY** & **EY** & **DG** & **IPI** & **GW** & **CG** & **DW** & **DE** & **Total** & \(A\) \\ \hline
**Glutelin Type-A 2-Like** & A0A152Y/V5 & 200 & 0 & 1 & 1 & 0 & 0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 6 & 0.030 \\
**Lenti** & & & & & & & & & & & & & & & & & & \\ Albumin S & P86782 & 37 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.000 \\ Broad Bean & & & & & & & & & & & & & & & & & \\ Leggium type B & P5190 & 462 & 0 & 0 & 0 & 2 & 2 & 1 & 0 & 0 & 0 & 0 & 1 & 6 & 0.013 \\ Vicilin & P08438 & 436 & 0 & 0 & 0 & 1 & 2 & 0 & 0 & 0 & 0 & 0 & 2 & 5 & 0.011 \\ Convicillin & B005L8 & 469 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 4 & 5 & 0.011 \\ Wheat & & & & & & & & & & & & & & & & & \\ Glutelin subunit DXS & P10388 & 827 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.000 \\ Avenin-like B1 & Q2A783 & 267 & 0 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0.007 \\ Alpha/Beta Gladina & P02863 & 266 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0.004 \\ Alpha/Beta Gladina A-I & P04721 & 242 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.000 \\ Alpha/Beta Gladina A-II & P04722 & 271 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0.004 \\ Alpha/Beta Gladina A-III & P04723 & 262 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0.004 \\ Alpha/Beta Gladina A-IV & P04724 & 277 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0.004 \\ Alpha/Beta Gladina A-V & P04725 & 299 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0.003 \\ Glutelin Type A-I & M72Y/V6 & 299 & 0 & 1 & 0 & 0 & 0 & 3 & 0 & 0 & 1 & 1 & 0 & 6 & 0.020 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
Figure 1: Sequences of (**A**) rice glutelin Q6T725, (**B**) chickpea legumin Q9 SMJ4, (**C**) chickpea glutelin type B-2 Q02897, (**D**) chickpea gutelin type A-2 A0A1S2 YJV5, and (**E**) maize prolamin PPROL 17 B6 UH22 and their modifications. The gray squares indicate selected bioactive peptides, and the arrows indicate modifications in the amino acid sequences. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Protein**} & \multicolumn{2}{c}{**Number of Cleavage Sites**} & \multicolumn{2}{c}{**Fragments**} & \multicolumn{2}{c}{**Selected Peptides Released**} \\ \cline{2-7}  & **Native** & **Modified** & **Native** & **Modified** & **Native** & **Modified** \\ \hline
**Glutelin Q6T725** & 160 & 182 & 144 & 160 & 1 & 8 \\
**Legumin Q9 SMJ4** & 192 & 206 & 167 & 180 & 0 & 10 \\
**Glutelin type-A** & & & & & & \\
**A0A1S2 YJV5** & 143 & 154 & 123 & 134 & 1 & 6 \\
**Prolamin PPROL 17 B6 UH22** & 42 & 49 & 39 & 47 & 0 & 4 \\
**Glutelin type-B** & **2 Q02897** & 165 & 179 & 144 & 159 & 2 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of peptides released after modifications.
## 3 Materials and Methods
### Presented CAD
#### 3.3.3 Multi-Layer Feature Extraction  
Upon the retraining of the three deep neural networks, the extraction of deep features commences. The present research extracts attributes from three separate layers of each deep neural network to assess their impact on the effectiveness of classification, contrary to relying on just one layer. In MobileNet, the layers consist of the final convolutional layer (Layer 1), the pooling layer (Layer 2), and the fully connected layer (Layer 3). For ShuffleNet, the chosen layers include the final ReLU activation layer (Layer 1), the pooling layer (Layer 2), and the ultimate fully connected layer (Layer 3). For EfficientNetB0, the selected layers include the final element-wise multiplication layer (Layer 1), the last pooling layer (Layer 2), and the fully connected layer (Layer 3). The feature lengths for such layers are specified in Table 1. Given the considerable size of the attributes from Layer 1, as demonstrated in Table 1, it is imperative to diminish their dimensionality. The DWT is used for this objective, as it efficiently diminishes the amount of features yet offers a time-frequency depiction of the inputs, thereby improving the effectiveness of classification [48; 59]. The Haar wavelet serves as the mother wavelet, employing six levels of decomposition. The approximation coefficients from the sixth level are the finalised feature vectors following the decrease in dimensionality.
## 5 Discussion
### Benefits and Challenges of Federated Learning  
Federated Learning has several benefits for IoV applications, but some challenges also have to be addressed. The enhancement of data privacy is one of the most appropriate reasons for the use of Federated Learning. Since only model updates are transferred and the data remain on the device, data without including sensitive information can be shared. This is very important for IoV applications as there is a lot of focus on protecting data privacy [47]. Scalability as a key feature of Federated Learning helps eliminate the challenge of moving the operation to a central location with data storage and processing. In such an environment where there is a wide coverage of IoV networks and many devices have to be integrated and controlled, this feature proves to be useful [48]. Local data processing minimizes the burden of transferring huge amounts of data to the main server for processing. This is very important in instances such as real-time DDoS detection where the need to respond to attacks is very high. A substantial amount of Federated Learning applications require online clients to update the global model through a central server. The downside of most communication-intensive tasks is bandwidth waste. Communication strategy standards need to be improved, and there needs to be time to lessen the burden of the majority of these issues [49]. Different types of IoV devices may be characterized by different levels of computing infrastructure, availability of networks, and distribution of data. This diversity may add more complications to the effort of Federated Learning and, in turn, lead to incomplete model updating. Weighted averaging and adaptive learning rates are examples of solutions to these challenges.  
In our approach, we improve the detection of DDoS in IoV with the Gini index and Federated Learning. However, the proposed approach is bound to other cybersecurity threats. The present work has focused on typical IP/UDP-based DDoS attacks. Advanced methods may be difficult to trace, involving, for example, emulation dictionaries. Most importantly, it will be interesting to adapt the approach to detect more complex variants of DDoS and other IoV-specific threats for spoofing or jamming attacks by evaluating its performance across different configurations of IoV. Another challenge in the implementation or realization of FL in real-time IoV includes the resource limitation of the devices and possible communication overhead that might reduce performance. In this regard, future research will provide a study on lightweight model optimization for FL, reduction in communication updates, and edge-based aggregations for enhancing real-time detection and allowing FL viability at deployment in IoV.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 3 Proposed Algorithm
### The Relationship between MSE and Delay Unit  
The FM converts the amplitude compensation system of the reference signal into an amplitude/phase compensation system. Therefore, we need to understand the relationship between the steady-state MSE and the value of \(k\) in order to determine the optimal \(k\) value.  
As an index of accuracy, we use the MSE \(E(e(n)^{2})\). The lower the MSE value, the better the \(k\) value.  
In Figure 4, it can be seen that the delay unit \(k\) can affect the stability under different FMs. When the delay unit \(k\) is changed, the convergence rate and steady-state performance of the proposed system are also affected. The symbol of the red triangle represents that the \(k\) value will lead to an optimal MSE of the system, but the black circle is the opposite. When \(k\) is set to 3, 11, 17, 24 or 31, it is easy to cause the decline of the system performance in dealing with 15% FM, as shown in Figure 4d; \(k=9\), 10 or 27 will result in system divergence, and \(k=8\), 14, 20 or 32 will obtain a better performance. Hence, it is essential to place the above analysis in the first consideration to choose an appropriate value. Based on the existing results, it is clear that \(k=14\) is able to achieve the best MSE over the others under different FMs, as shown in Table 1. We therefore set the value of \(k\) to 14 for obtaining a good system performance in different cases.
# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 4 Experimental Results
### Performance Analysis
The proposed OHA considers whether the signal is good or bad and its judgment considers various conditions such as \(HM^{Good}\), \(HM^{Bad}\), \(T\)S-\(H\)O, \(T^{U\_HO}\), etc., and so the performance of the number of handovers in Scenario 1 was the best. In Scenarios 2 and 3, because APs were deployed with a low density or sparsely, sometimes the UE could not obtain suitable candidate APs, resulting in a slight decrease in performance. OHAQR is an integrated algorithm based on the OHA algorithm and considering QoS requirements. Therefore, in order to meet the requirements of QoS, the performance of throughput and PLR will be maintained, and the number of handovers will increase slightly.
## 4 Results and Discussion  
As observed, each model excelled according to the feature selection used and the architecture of the model trained. The traditional ML models (SVM, Naive Bayes, Random Forest, and Logistic Regression) achieved an accuracy score that varied depending on whether unigrams, bigrams, trigrams, or the combinations (uni + bi + tri) were used for n-gram representation. Models that performed better were those based on deep learning, specifically CNN and Bi-LSTM, which allowed for contextual dependencies to be captured. The transformer-based models (LLaMA 2 and M-BERT) provided the best results, significantly outperforming other models after fine-tuning with LoRA. The results show that contextual embeddings and pre-trained transformer architecture outperform in this task for Roman Urdu text for similar tasks. The follow-up sections include a thorough discussion of these results and performance variations.
## 4 Experimental Analysis
### Experimental Setup  
This study presents a novel epileptic seizure classification model that utilizes RNN and is tested on both single-channel and multi-channel datasets (BONN and CHB-MIT), which are publicly accessible online. The BONN dataset contains single-channel EEG data while CHB-MIT consists of 23 EEG channels. The RNN-based model was trained and tested in five scenarios with different sets of parameters [53] such as learning rate, training, testing ratios, and frequency analysis methods, i.e., DWT and FFT as illustrated in Algorithm 1.  
```
Input: \(\bullet\) dataset (CHB-MIT/BONN) \(\bullet\) Preprocessing method (DWT/FFT) \(\bullet\) Train/test ratio \(\bullet\) Learning rate \(\bullet\) k-fold cross-validation value Output: BestModel (model)--The RNN model with the highest validation accuracy fortrain_test_ratio indo forlearning_rate indo forfold indo  Train the RNN model using the training data with train_test_ratio and learning_rate;  Validate the RNN model using the validation data with train_test_ratio and learning_rate;  BestModel \(\leftarrow\) RNN model with the highest validation accuracy;
```  
**Algorithm 1**RNN-based epileptic seizure classification.  
A commonly used method for improving the performance and evaluating the accuracy of machine learning classifiers is known as \(k\)-fold cross-validation. This method divides  
Figure 4: Raw EEG data from CHB-MIT dataset.  
the dataset into \(k\) subsets, with each subset serving as validation data for one model and the remaining \(k-1\) subsets serving as training data. This method is repeated \(k\) times, with a different validation subset for each model. In this study, for instance, a 10-fold cross-validation is used, resulting in the dataset being divided into 10 subsets. It means that 10 unique RNN models have been trained using the following procedure: model 1 used the first subset for validation and trained on the subsequent nine subsets. Similarly, model 2 reserved the second subset for validation and trained on subsets 1, 3-9. This method was used for all 10 models. Finally, the model with the highest validation accuracy, indicating stronger generalization capabilities, was chosen for further testing.  
In scenarios 2 and 3, there are a total of 320 simulations each in which the RNN-based epilepsy detection model is trained and tested on the BONN dataset using DWT and FFT. Similarly, the same two scenarios are replicated in scenarios 4 and 5 using the CHB-MIT dataset, as illustrated in Table 3.  
In scenario 1, the RNN-based classification of EEG data is performed five frequency sub-bands (Delta Ac-4 0-4.0 Hz, Theta Dc-4 4.0-8.0 Hz, Alpha Dc-3 8.0-15.0 Hz, Beta Dc-2 15.0-30.0 Hz, and Gamma Dc-1 30.0-60.0 Hz) extracted from the CHB-MIT dataset using DWT, as described in Table 4.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 2 General Principles of the Two-Phase Volumetric Expanders
### Performance Indicators of Two-Phase Volumetric Expanders  
In the actual operation, the pressure-volume diagram deviates from the theoretical one mainly because of under-expansion or over-expansion losses, internal fluid leakages, pressure losses introduced in the valves or ports, heat transfer losses, and friction losses. In particular, these losses decrease the area of the pressure-volume diagram, thus, suggesting a reduction in the delivered power by the expander [10; 16; 17].  
The isentropic efficiency is a performance indicator considering these losses. It is defined as the measured power delivered by the expander divided by the isentropic power that the real mass flow rate through the expander can provide [18].  
On the other hand, a different performance indicator is the adiabatic efficiency. It is defined as the measured specific enthalpy difference between the inlet and outlet sections of the expander divided by the homologous isentropic specific enthalpy difference [18].  
An additional performance indicator is the volumetric efficiency (otherwise called filling factor) that considers the internal fluid leakages. In particular, the volumetric efficiency estimates how the incoming fluid fills the working chamber, and it is determined as the ratio of the actual volumetric flow rate at the inlet section to the theoretical one displaced by the expander. Values of the volumetric efficiency higher than one are possible because of the internal fluid leakages [18; 19].