esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Network Model Assumptions  
The nodes are static in nature as well as homogeneous. The residual energy allocated to each CM node is 0.5 Joules. The structure of the \(S_{node}\) is centralized. At the initial stage, all the nodes are localized randomly. Then, groups are divided and CH is chosen, which helps transfer the data packets to the \(S_{node}\) using a single or multi-hop communication model. The node with maximum residual energy is chosen as a CH for better performance. Mobility model selection is given in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Descriptions** \\ \hline CH & Cluster Head \\ NRenergy & Normalized Residual Energy \\ OF\({}^{\text{velocity}}\) & Objective Function for Velocity \\ OF\({}^{\text{LC}}\) & Objective Function for Coverage Area \\ O\({}^{\text{latency}}\) & Objective Function for Latency \\ RE & Residual Energy \\ IE & Initial Energy \\ A\({}_{\text{speed}}\) & Average Speed \\ T\({}_{\text{o}}\) & Transmission Time \\ \(S_{node}\) & Sink Node \\ AR & Anchor Node \\ E & Event \\ \(\delta_{\text{th}}\) & Threshold Value of the Event \\ K & Probability Factor \\ BR & Bypass Rider \\ F & Follower \\ O & Overtaker \\ AT & Attacker \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication Model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Communication Type** & **Communication Items** & **Model Type** \\ \hline Short-Distance & Between CM and CH & Free Space Mobility Model \\ Communication & Between CH and \(S_{node}\) & Multi-Path Fading Model \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication Model.
# Body Area Networks in Healthcare: A Brief State of the Art
## 1 Introduction
Moreover, the authors analyze security, resource management, quality of service (QoS), real-time monitoring and other problematic factors in WBAN-based healthcare systems. Another paper surveys provides a taxonomy on QoS-based routing in WBANs [13]. Very recently, researchers conducted a review of WBAN applications and network architecture, routing protocols and data analysis techniques and how these aspects influence ubiquitous health monitoring [14]. In our humble opinion, this paper fills a current gap in BAN reviews. The focus of this paper is to offer a comprehensive brief state of the art to the overall readership interested in healthcare applications based on BANs. The remainder of the paper is organized as follows. Section 2 provides a short tutorial of BAN technology showing general features and requirements of these networks. Afterwards, the explanation is decomposed in Section 3, where physical layer aspects are treated, Section 4, in which data link requirements are afforded, possible network layer configurations and middleware layer issues in Section 5. Finally, in Section 6, important cross-layer properties are detailed. These sections take into consideration different approaches according to IEEE 802.15.16 standard, safety issues, privacy and security, and sustainability. Moreover, included in the physical layer, a variety of wearable sensors (mechanical, chemical, electrical and optical) are explored, explaining their pros and cons, and including a compilation of their utility in BAN healthcare applications. Finally, Section 7 shows current challenges in this field, and Section 8 presents the conclusions of this investigation as well as some future trends.
# A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment
## 4 Research Methodology
### From the Unified Mind Map to a Dependency Model  
The unified mind map went through a rigorous transformation process to be converted to a dependency model and to ensure that all rules of dependency modelling are obeyed.  
At the very core of our dependency model is _the safe and secure operation of a SCADA system_. Setting this as the major objective reflects the fact that safety and security, as well as risks, are the properties of the overall system rather than of any sub-component on its own. While in the unified mind map, there were 610 elements, in the process of transformation, the number of elements was reduced to 452 elements due to the following reasons:  
* Elements from the unified mind map were combined together to form one paragon when an entry in a mind map referred to the characteristics of another entity, e.g., a participant had drawn an element such as \"Operating System\" and then child elements depicting its characteristics such as \"Secure\" and \"Up-To-Date\".
* The entities which outlined various concrete implementations of higher-level entities were not included; e.g., we only kept a paragon for an Operating System (OS), but we did not include all the different types of OSs listed by the participants (e.g., Linux, Windows, MAC OS), similarly for communication protocols. As the model is configurable, it is expected that a user will enrich the model with the paragons for every operating system that is in use by the organisation and paragons for every communication protocol that is relevant to the system being modelled.
* A small number of elements did not find a place in the final model as the elements either were not fit with the main structure of the model or were specific to a particular sub-domain rather than being relevant to SCADA systems in general.
* In a small number of cases, it was not possible to establish the meaning of the entity based on the information provided by the participants. This is one of the limitations of the chosen approach to data collection where we did not have an opportunity to clarify information provided during the workshops at a later stage.  
The names of the paragons have been formulated generically to ensure that when configured, they could be replaced with characteristics relevant to the scenario. For example, we named one of the paragons \"Software is Ok\". This may have different interpretations for different organisations; some may interpret it to mean that \"Software is effective in fulfilling its purpose\", others as \"Software is protected from external attacks\", or both of the above. While identifying the important dependencies, we do not prescribe the characteristics and exact interpretations of paragons, leaving the exact wording to the end users of the configurable SCADA model to be defined to suit each particular context.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 4 Results and Discussion
### Generalization to Other IoT Attacks  
The proposed machine learning-based approach is not limited to detecting Cross-Site Scripting (XSS) attacks and demonstrates potential for generalizing to other IoT attack types. The feature selection techniques employed--wrapper and filter methods--are inherently adaptable, as they optimize feature subsets to identify the most relevant attributes for classification. This flexibility allows the framework to handle various attack scenarios, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), SQL injection, and malware propagation. By tailoring the input datasets to represent the characteristics of these attack types, the proposed approach can effectively address a broader range of IoT security challenges.  
The machine learning algorithms used in this study, including Random Forest, Decision Tree, and Naive Bayes, further enhance the generalizability of the model. These algorithms can be retrained with datasets containing features specific to other attacks. For instance, DoS and DDoS attacks exhibit distinct traffic patterns, such as abnormally high packet rates or repetitive requests, which can be extracted as features for training. Similarly, SQL injection attacks share similarities with XSS in that they exploit input validation vulnerabilities, making the proposed feature selection and classification methods adaptable to detect anomalous database queries or malicious payload structures. Additionally, malware detection in IoT devices often relies on identifying abnormal device behaviors, unauthorized communications, or suspicious API calls.  
The wrapper method's iterative optimization of feature subsets is particularly valuable for enhancing classification performance, regardless of the attack type. Furthermore, the ensemble-learning capabilities of Random Forest provide robust generalization across diverse datasets, while the interpretability of Decision Tree models enables a clearer understanding of the features contributing to effective attack detection. Together, these characteristics make the proposed system scalable and adaptable to evolving IoT threats.  
Future research will explore extending this approach to handle multi-class attack detection, where multiple attack types can be classified within a single framework. Such advancements would further demonstrate the model's ability to generalize and its practical applicability in securing real-world IoT ecosystems against a wide variety of cybersecurity threats.
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 2 Mixed Signal Dereverberation
### Cost Function
By weighting the elements in the non-negative matrix \(\mathbf{V}\) and summing the weighted elements, the penalty function for \(\mathbf{V}\) can be expressed as:  
\[G_{\mathrm{v}}(\mathbf{V})=\sum_{l=1}^{L}\sum_{n=1}^{T}\eta_{n}^{\mathrm{v}}V _{l,n} \tag{7}\]  
where \(\eta_{n}^{\mathrm{v}}\) is a non-negative parameter for \(\mathbf{V}\). The penalty function for \(\mathbf{V}\) can speed up the convergence of the cost function \(f(\mathbf{W},\mathbf{V},\mathbf{M})\) and make the estimated spectrogram closer to the spectrogram of the sound source signal \(\mathbf{S}\). Next, the penalty function for \(\mathbf{M}\) is determined according to the characteristics of the spectrum of RIR. The logarithmic spectrum of a simulated RIR with \(T_{60}=600\) ms presents a smooth attenuation structure, which is exhibited in Figure 3. In Figure 3, the  
Figure 2: The TF spectrums of different signals: (**a**) the original speech signal; (**b**) the recorded signal with \(T_{60}=300\) ms.  
energy decays over time. The penalty function for \(\mathbf{M}\) is added to promote the attenuation structure [27], which can be expressed as:  
\[G_{\mathrm{m}}(\mathbf{M})=\sum_{k=1}^{K}\eta_{k}^{\mathrm{m}}\parallel\mathbf{ U}\mathbf{M}_{k}^{\prime}\parallel_{2}^{2} \tag{8}\]  
where \(\eta_{k}^{\mathrm{m}}\) is a non-negative parameter for \(\mathbf{M}\); \(\mathbf{M}_{k}\in\mathbb{R}_{0,+}^{H}\) is the \(k_{th}\) row of \(\mathbf{M}\), and \(\mathbf{U}\in\mathbb{R}^{(H-1)\times H}\) represents a finite difference matrix. In addition, \"\((\cdot)\)\" means the transpose of matrix, and \"\(\parallel\cdot\parallel_{2}^{2}\)\" is the square of the \(\ell_{2}\) norm. Therefore, the cost function (i.e., Formula (6)) can be rewritten as:  
\[f(\mathbf{W},\mathbf{V},\mathbf{M})=Q_{\beta}(\mathbf{Y}\parallel\mathbf{X})+ \sum_{l=1}^{L}\sum_{n=1}^{T}\eta_{n}^{\
u}V_{l,n}+\sum_{k=1}^{K}\eta_{k}^{ \mathrm{m}}\parallel\mathbf{U}\mathbf{M}_{k}^{\prime}\parallel_{2}^{2} \tag{9}\]  
In Section 2.3, the optimization for \(\mathbf{W}\), \(\mathbf{V}\) and \(\mathbf{M}\) is introduced to minimize the cost function \(f(\mathbf{W},\mathbf{V},\mathbf{M})\) and obtain the optimal estimation for \(\mathbf{S}\).
# A Pipeline Approach to Context-Aware Handwritten Text Recognition
## 5 Discussion  
Some interesting findings have been discovered in this study. Real-life documents contain a substantial amount of noise (the noise can occur before and after the digitization process). Thus, the document layout should be properly analyzed, and data pre-processing plays an important role in treating different types of documents. In contrast to the line segmentation technique [3] and the full-page document recognition method [4], we applied an object detection approach for implicit ROI localization. The proposed approach was able to perform text type classification at the same time. There generally exist different text types in handwritten receipts, such as printed, handwritten, and non-text. Different text types should be treated individually to ensure optimal performance, rather than considering all text types in one go, which might result in inferior performance. Thus, explicitly segmenting different ROIs according to the text types would help to increase recognition accuracy and is more suitable for real-life applications.  
Along this line, we find that a single HTR model cannot cope with the different text types. For example, a model that is good at recognizing printed texts does not necessarily work well with handwritten texts. Moreover, a separate model needs to be developed to recognize non-text attributes such as signature, which is considered vital for documentation. Therefore, a pipeline approach was proposed by training different models to deal with different text types. The transcribed texts are not directly useful for practical applications. Hence, a NER module was introduced to assign the transcribed texts into their corresponding entities/groups (e.g., name, date, address, phone number). The pipeline approach ensures a fully automated workflow with better efficiency, recognition accuracy, and data management ability in a practical application.  
We also wish to highlight that the ResNet-101T model is proposed due to the following reasons. First, ResNet-101 has the advantage of being able to minimize negative effects  
Figure 12: Sample input and output I: (**a**) input; (**b**) output.  
Figure 13: Sample input and output II: (**a**) input; (**b**) output.  
when the depth of the network is increased. Second, the Transformer model can better model the words input with the attention mechanism compared to the other RNN variations. Moreover, it also has a relatively low inductive bias compared to its RNN counterpart. In addition, the experimental result suggests the application of ResNet can effectively extract the 2D representative features, such as the shape/outline and positional information of the words for training the Transformer model. The result of feeding ResNet output to the Transformer model is better than using raw text input for the Transformer model. Empirical results show that the proposed model has clearly outperformed LSTM and ViT in terms of CER and WER. Although LSTM takes less time for training due to its relatively simpler architecture, its accuracy is far inferior to the proposed ResNet-101T method.
## 3 Experimental Results and Discussion
### \(E_{50}\)  
The deformation modulus \(E_{50}\) was an important indicator for evaluating the deformation properties of concrete materials. In this study, the ratio of 50% peak stress in the stress-strain curve to its corresponding strain was selected as the deformation modulus [42]:  
\[E_{50} =\frac{\sigma_{0.5}}{\epsilon_{0.5}} \tag{2}\]  
According to the above calculation method, the values of \(E_{50}\) with various conditions were obtained, as shown in Figure 4. It can be observed that the deformation modulus \(E_{50}\) declined from 1567 MPa to 1285 MPa after undergoing 80 freeze-thaw cycles, with a reduction rate of 18%, while the \(E_{50}\) of the repaired specimens significantly increased after the application of the surface reinforcer. Specifically, after undergoing four applications and a 7 d action duration, the \(E_{50}\) of the repaired specimens coated with surface reinforcer types A, B, and C were 1707 MPa, 1615 MPa, and 1657 MPa, respectively, which was 33%, 26%,  
Figure 3: Compressive strength of concrete specimens under different conditions. (**a**) Compressive strength changes under different test conditions of surface reinforcer type A. (**b**) Compressive strength changes under different test conditions of surface reinforcer type B. (**c**) Compressive strength changes under different test conditions of surface reinforcer type C.  
and 29% higher than that of the deteriorated specimens. Moreover, after four applications, when the action duration increased from 3 d to 14 d, the \(E_{50}\) of the specimens repaired by reinforcer type A increased from 1595 MPa to 1881 MPa, with a larger-scale increase of 17.93%. After applying surface reinforcer type A and with 7 d action duration, the deformation modulus of repaired specimens subjected to two, four, and eight applications could be improved 1.25, 1.33, and 1.26 times compared with the deteriorated one. This phenomenon illustrates that there exists a threshold for the increase in the \(E_{50}\) with the number of applications.
## 4 The Proposed Group Authentication Framework
### Phase 2: Group Authentication
#### 4.2.1 Step 1: Sharing of Chebyshev Degree  
The first step of the authentication process is to share the Chebyshev degree to be used in the Chebyshev polynomial. The smart contract will use a different Chebyshev degree for each IoT device; usually it is a large number. The smart contract will securely share the Chebyshev degree with corresponding IoT devices. To facilitate this secure sharing, the Chebyshev polynomial-based public key cryptosystem is used, as shown in Figure 5.  
The process shown in the figure is used by each TA (\(TA_{i}\), where \(i\) is from 1 to the total number of TAs) and smart contract to share the Chebyshev polynomial degree (\(deg_{ij}\), for the \(i\)th TA and \(j\)th IoT device). The \(i\)th TA will initiate the process for the \(j\)th IoT device by generating a large number \(sdij\) and another number \(x_{ij}\) (where \(x\in(-1,1)\)). The TA will compute _Tdevij_ using the Chebyshev polynomial degree \(sdij\) and the variable \(x_{ij}\). The public key of the \(j\)th IoT device will be (\(x_{ij}\), _Tdevij_), whereas the private key will be \(sdij\). The smart contract will use this public key to securely communicate with the IoT device. The smart contract will randomly create two large numbers, \(sbij\) and \(deg_{ij}\). The smart contract will compute _Tblockij_ and _Tdevblockij_ using \(sbij\) and the IoT device's public key (\(x_{ij}\), _Tdevij_), as shown in the figure. The smart contract will then compute the cipher message \(M_{ij}\) by multiplying \(deg_{ij}\) with _Tdevblockij_. Once the cipher message is computed, the smart contract will send \(M_{ij}\) to the corresponding IoT device along with _Tblockij_. The IoT device can recover \(deg_{ij}\) from \(M_{ij}\) by multiplying it with \(T_{sdij}(Tblockij)\), which is a Chebyshev value computed using the IoT device's private key and _Tblockij_. The recovery of \(deg_{ij}\) from \(M_{ij}\) is possible because of the _semigroup_ property of the Chebyshev polynomial, which is shown in Equation (5) corresponding to the proposed authentication framework.  
\[Tdevblockij=T_{sbij}(Tdevij)=T_{sdij}(Tblockij)=T_{sdij}(T_{sbij}(x_{ij}))=T_{sbij }(T_{sdij}(x_{ij})) \tag{5}\]  
The smart contract follows this process with each IoT device of different TAs to securely share the associated \(deg_{ij}\). At the end of this step, each IoT device will have its own \(deg_{ij}\)  
Figure 5: Chebyshev degree sharing process.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Wavelet Packet Energy Analysis of AE Signals  
Traditional analysis methods such as the FFT are not suitable to analyze typical non-stationary signals such as AE signals owing to the defects of the algorithm itself. By contrast, wavelet analysis is a localized analysis in the time and frequency domains, and it can not only present the frequency-domain analysis of the signal in local time but also describe the time-domain information corresponding to the frequency-domain information. Based on wavelet analysis theory, wavelet packet analysis introduces the concept of optimal basis selection, which has higher time-frequency resolution than wavelet analysis [37]. Therefore, wavelet packet analysis has greater advantages in the time-frequency domain analysis, feature extraction, and AE signal energy analysis.  
The concept of the frequency centroid described in the previous section clarifies that the frequency centroid will be close to the dominant energy band of the signal. Therefore, in this section, we use wavelet packet analysis to further analyze the energy of the P-signals to obtain the energy distribution characteristics of the P-signals in each band. More importantly, we verify the practicability of peak stress prediction by the P-signals.
## 3 Results and Discussion
### Stress Intensity Factor Calculation  
After a fatigue crack initiates in the outer web at its anchor side connection with the upper support plate, the crack expands in a direction approximately perpendicular to the support plate, as shown in detail in Figure 16.  
As seen in Figure 16, web stiffeners will effectively prevent the crack from extending further. Crack propagation is hence assumed to be bounded by upper and lower web stiffeners. The allowable propagation length of the upper and lower crack tip in the bridge segment is 238.1 mm and 408.8 mm, respectively. In this paper, SIFs for through-thickness cracks of different sizes were calculated by an XFEM contour integral. Within the range of the crack tip propagation, a total of 34 different crack length combinations were considered--17 cases for the upper crack tip SIF calculation and 17 for the lower crack tip.  
It has been verified that the numerical stability of the SIF for cracks in a complex structure such as a cable-girder anchorage zone can be guaranteed when there are ten contour integrals and the mesh density is sufficiently fine. The \(J\)-integral values of the first two contours are usually not stable and should be omitted. Calculated SIFs from the 3\(\sim\)10 contours tend to be stable, with fluctuations of less than 5%, the average of which is thus taken as the calculated SIF at a given crack front location. The SIF on the inner surface of the web was obtained by fitting SIF values at other crack front locations.  
In LEFM, the basic formula of the SIF can be expressed as follows [5]:  
\[K=Y\sigma\sqrt{\pi a} \tag{5}\]  
where shape function \(Y\) depends on location, the shape and size of the crack, and stress condition, as well as structural detail; \(\sigma\) stands for nominal stress, which was computed as the cable force divided by the cross-sectional area of two girder webs and \(a\) is the half crack size.  
With available SIFs of different crack lengths, the corresponding \(Y\) values were calculated from Equation (5), and an expression of shape function \(Y(a)\) for the upper and lower crack tip was obtained by data fitting. The determination coefficient \(R^{2}\) for the curve  
Figure 16: Crack location and propagation boundary.  
fitting of the upper and lower crack tip shape function is 0.7425 and 0.7272, respectively. Among 34 crack cases calculated, the average value of \(Y\) is 0.90, and the maximum is 1.48. To simplify calculations, \(Y\) is conservatively taken as a constant of 1.5 in the subsequent evaluation of fatigue crack propagation performance.