esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### MISO-NOMA Environment
The received downlink signal at a far device in the MISO-NOMA cell can be shown as:  
\[\mathbf{y}_{f}=\mathbf{x}\mathbf{h}_{f1}+\mathbf{x}\mathbf{h}_{f2}+z_{f} \tag{11}\]  
where \(\mathbf{h}_{f1}\) represents the channel coefficients among a far device and the 1 st antenna at BS, \(\mathbf{h}_{f2}\) represents the channel coefficients among the far device and 2 nd antenna at BS and \(z_{f}\) is AWGN noise component at the far device with mean zero and variance \(\sigma^{2}\). The far user is signified by weak link condition, and signal \(\mathbf{x}_{f}\) is usually given further power percentage by BS where \(\eta_{f}>\eta_{m}>\eta_{n}\). The obtained signal at a far device can be formulated as:  
\[\mathbf{y}_{f}=\sqrt{P_{t}\eta_{f}}\mathbf{x}_{f}\Big{(}\mathbf{h}_{f1}+\mathbf{h}_{f2}\Big{)} +\Big{(}\sqrt{P_{t}\eta_{m}}\mathbf{x}_{m}+\sqrt{P_{t}\eta_{n}}\mathbf{x}_{n}\Big{)} \Big{(}\mathbf{h}_{f1}+\mathbf{h}_{f2}\Big{)}+z_{f} \tag{12}\]  
The 1 st term in (12) implies the target signal for far device and the 2 nd term indicates the interference term from other user devices. The possible bit rate for a far device could be shown as [3; 21]:  
\[R_{f}=\log_{2}\Bigg{(}1+\frac{\Big{|}\mathbf{h}_{f1}+\mathbf{h}_{f2}\Big{|}^{2}P_{t} \eta_{f}}{\Big{|}\mathbf{h}_{f1}+\mathbf{h}_{f2}\Big{|}^{2}P_{t}(\eta_{n}+\eta_{m})+ \sigma^{2}}\Bigg{)} \tag{13}\]  
Typically, the near user device has a good link status alongside BS, therefore, a low power factor can be assigned to \(\mathbf{x}_{n}\), and the near user received signal can be stated as  
\[\mathbf{y}_{n}=\sqrt{P_{t}\eta_{n}}\mathbf{x}_{n}(\mathbf{h}_{n1}+\mathbf{h}_{n2})+\Big{(} \sqrt{P_{t}\eta_{m}}\mathbf{x}_{m}+\sqrt{P_{t}\eta_{f}}\mathbf{x}_{f}\Big{)}(\mathbf{h}_{ n1}+\mathbf{h}_{n2})+z_{n} \tag{14}\]  
In Equation (14), the 1 st term represents the anticipated signal, and the 2 nd term implies interference from other devices. It can be noted from Equation (14), that the interference can be principal since the far user may be assigned a further power percentage. Thus, at a near device, SIC is accomplished, where direct decoding for the far user signal \(\mathbf{x}_{f}\) is implemented first, then eliminated from the aggregate signal. After that, the middle device signal \(\mathbf{x}_{m}\) is decoded and gets rid of it from the resultant signal and the possible rate for a near user \(R_{n}\) can be shown as:  
\[R_{n}=\log_{2}\Bigg{(}1+\frac{|\mathbf{h}_{n1}+\mathbf{h}_{n2}|^{2}P_{t}\eta_{n}}{ \sigma^{2}}\Bigg{)} \tag{15}\]
## 4 Numerical Results and Discussion
### Parametric Study  
The non-dimensional displacements, \(w^{*}\), and stresses, \(\sigma_{x}{}^{*}\), \(\sigma_{y}{}^{*}\), of symmetric ([\(0^{\circ}/90^{\circ}/0^{\circ}\)] and [\(0^{\circ}/90^{\circ}/90^{\circ}\)/0^{\circ}\)]) and antisymmetric ([\(0^{\circ}/90^{\circ}\)]) laminated cross-ply plates and shells with different \(a/h\) and R\(/a\) ratios under uniformly distributed load are presented in Table 3. It can be seen that there is a difference between the range 11.28% (R/\(a\) = Plate) and 18.75% (R/\(a\) = 10) and for moderately thick (\(a/h\) = 20) antisymmetric ([\(0^{\circ}/90^{\circ}\)]) plates and shell between the GDQ and BDM methods. However very close results were obtained for symmetric ([\(0^{\circ}/90^{\circ}/0^{\circ}\)] and [\(0^{\circ}/90^{\circ}/90^{\circ}\)/0^{\circ}\)]) plates and shells for all R\(/a\) and \(a/h\) ratios. Tables 4 and 5 present the effects of the lamina material orthotropy (\(E_{1}/E_{2}\)) on the non-dimensional displacements, \(w^{*}\), and stresses, \(\sigma_{x}{}^{*}\), \(\sigma_{y}{}^{*}\), of symmetric [\(0^{\circ}/90^{\circ}\)/0^{\circ}\)] and antisymmetric [\(0^{\circ}/90^{\circ}\)] laminated cross-ply plates and shells. The results are in good agreement for symmetric [\(0^{\circ}/90^{\circ}\)/0^{\circ}\)], whereas BDM underestimates the non-dimensional \begin{table}
\begin{tabular}{c c c c c} \hline  & R/\(a\) & BDM & GDQ & FEM \\ \hline \multirow{2}{*}{\(w^{*}\)} & 10 & 14.880 & 18.315 & 20.900 \\  & 20 & 20.763 & 20.796 & 22.165 \\  & Plate & 22.152 & 19.901 & 22.603 \\ \hline \multirow{2}{*}{\(\sigma_{x}{}^{*}\)} & 10 & 154.778 & 194.918 & 142.100 \\  & 20 & 172.361 & 176.382 & 129.650 \\  & Plate & 136.668 & 118.459 & 105.135 \\ \hline \multirow{2}{*}{\(\sigma_{y}{}^{*}\)} & 10 & 1850.1 & 2323.4 & 2569.2 \\  & 20 & 2612.8 & 2614.2 & 2684.3 \\ \cline{1-1}  & Plate & 2785.6 & 2466.0 & 2690.4 \\ \hline \end{tabular}
\end{table} 
Table 2: Normalized central deflections of cross-ply [\(0^{\circ}/90^{\circ}\)] laminated shells under uniform load. With SS–RS boundary conditions (\(a/h=20\)). Figure 2: Convergence of displacements, \(w^{*}\), and stresses, \(\sigma_{x}{}^{*}\), of a moderately thick (\(a/h=20\)) and symmetric cross-ply (\(0^{\circ}/90^{\circ}/0^{\circ}\)) spherical panel (R\(/a=10\)). displacements and stresses for the R/_a_ = 10 ratio. The difference between BDM and GDQ for the ratio of R/_a_ = 10 antisymmetric [0\({}^{\circ}\)/90\({}^{\circ}\)] shell increases with the increase of (\(E_{1}/E_{2}\)). The reason for this may be that discontinues being dominant in the bending-stretching matrix (B matrix presence) in antisymmetric lamination.
###### Abstract  
The methodology based on reflected guided-wave by a reflector associated with an increase or a decrease in the cross-sectional area has the challenge of determining their location and identity. This paper presents a numerical investigation of a method for locating and identifying the reflector based on guided-wave circumferential scanning and phase characteristics. To determine the axial and circumferential positions of the reflector within the pipeline, the procedures of the guided wave-based circumferential scanning were presented, including data preprocessing, median filter, image smoothing and binary processing. Through theoretical analysis, we obtained the phase relationship between the guided-wave excitation signal and reflection signals generated by a reflector, such as corrosion, crack, weld and support, which caused the change in the cross-sectional area. Consequently, an algorithm based on the phase characteristics was proposed to determine the change and type of reflector. The spatial distances were calculated between the guided wave excitation signals with different phases and the concerned reflection signals, subsequently identifying the change and type of the reflector by comparing the distance values. An identification index named the reliable index for the character of the reflector (_RICR_) was defined to evaluate the reliability of the predicted results. Numerical and finite element simulation validations of the proposed method were performed. It has been found that if _RICR_ was larger than 1.05, the results predicting the reflector type were reliable. The proposed method was found to be superior relative to the conventional correlation coefficient method according to the numerical results. Finally, the simulation results demonstrated that the proposed method could be potentially applied for locating and identifying reflectors in pipelines.  
ultrasonic-guided wave; phase characteristics; circumferential scanning; steel pipes; spatial distance +
Footnote †: journal: Applied Mechanics and Physics Letters
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
## 2 Semi-Active Tuneable Clamping Table: Concept and Benefits
The viscous damping of the table is provided by the eddy current damping modules under the table, whose damping level can be adjusted by modifying the immersion of the conductive plates inside the magnetic field created by permanent magnets. Further details on its mechanical design and conception can be found in [18]. Finally, once the design and construction of the TCT has been defined, the objective is to develop a control strategy seeking for the maximization the chatter suppression capabilities of the TCT. Similar to the aforementioned strategies for TMDs [29; 40], an iterative self-tuning algorithm for the in-series architecture based on the measured chatter frequency is proposed here. The development and implementation of said algorithm is explained in the following sections. Figure 2: Passive damping concepts based on mode tuning for blade manufacturing and repair and their associated 2-degree of freedom (DoF) lumped-mass models. (**a**) Traditional tuned mass damper (TMD). (**b**) Tuneable clamping table (TCT).
## 4 Deformation Force Monitoring Based on Adaptive Adjustment of Clamping Force
### Correlation among a Deformation Force Sequence  
The machining deformation of aero-engine casing falls into the three-dimensional elasticity category. However, the existing analytical methods are mostly 2D and hence unable to solve the problem. Our solution is to simplify the problem into a plane stress problem [40,41], as shown in Figure 1b-e.  
As shown in Figure 1e, the rebalancing of residual stress can be equivalent to a uniform external pressure. According to the stress function method [40,41] of elasticity, the stress distribution of a rotating body subjected to a uniform external pressure is given as in (9):  
\[\begin{array}{l}\sigma_{rr}=\frac{\sigma_{rr}^{00}}{b^{2}-a^{2}}\left(\frac{ \sigma_{2}^{2}b^{2}}{r^{2}}-b^{2}\right)\\ \sigma_{\theta\theta}=\frac{-\sigma_{rr}^{00}}{b^{2}-a^{2}}\left(\frac{\sigma _{2}^{2}b^{2}}{r^{2}}+b^{2}\right)\\ \tau_{r\theta}=0\end{array} \tag{9}\]  
where \(\sigma_{rr}^{0}\) is the residual stress distribution, \(\sigma_{rr}^{00}\) is the residual stress at \(r=b\), and \(\sigma_{rr}\), \(\sigma_{\theta\theta}\) and \(\tau_{r\theta}\) are the internal stress distribution of the part after machining.  
Based on the generalized [PERSON]'s law, the radial strain can be calculated as in (10):  
\[\epsilon_{rr}=\frac{(\sigma_{rr}-\upsilon\sigma_{\theta\theta})}{E} \tag{10}\]  
where \(E\) is the Young's modulus and \(\upsilon\) the [PERSON]'s ratio.  
Thus, the radial strain at \(r=a\) can be calculated according to (11):  
\[\epsilon_{rr}=\frac{2\upsilon b^{2}\sigma_{rr}^{0b}}{(b^{2}-a^{2})E} \tag{11}\]  
Figure 2: The proposed method for monitoring aero-engine casing’s deformation force.  
By definition of displacement and strain in elasticity theory, the radial displacement (deformation) can be calculated according to (12):  
\[u_{rr}=\int_{a}^{b}\varepsilon_{rr}dr \tag{12}\]  
When the thickness of the part is small, it can be assumed that the strain of the part is constant; then (12) can be simplified as (13):  
\[u_{rr}=\varepsilon_{rr}\times b-a \tag{13}\]  
The radial deformation force can be calculated according to (14):  
\[F_{b}=\frac{u_{rr}}{C} \tag{14}\]  
where \(C\) is the equivalent flexibility at the clamping point.  
Since the stress distribution is not planar during the actual machining process, the strain of the part is not constant, and the part is also influenced by the positioning and clamping elements, which make the relationship between the deformation force and residual stress more complex. Nevertheless, the above equations show that the part deformation force after material removal is related to the part residual stress distribution, the part material, and the part geometry. In summary, there is a correlation among the deformation force sequence, and the subsequent deformation force can be predicted by the preceding deformation force.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Machine Learning in WSNs
#### 1.2.4 Deep Learning  
Deep learning, a subset of ML, automatically utilises deep neural networks to extract hierarchical features from raw data. In WSNs, deep learning approaches are being increasingly adopted for complex tasks such as feature extraction from raw sensor signals, temporal data analysis, and the processing of high-dimensional data types, including images and video streams. The ability of deep learning models to handle large volumes of heterogeneous data positions them as a promising solution for advanced WSN applications [21; 22; 23; 24; 25]. For instance, convolutional neural networks (CNNs) have been used for event recognition in multimedia sensor networks. However, deep learning models are typically resource-intensive, requiring substantial memory, computation, and energy, which limits their deployment on typical sensor nodes. For clustering, deep learning approaches (e.g., autoencoders for feature extraction) may offer benefits in high-dimensional data scenarios but are generally not suitable for lightweight, distributed clustering in WSNs.
## 2 Materials and Methods
### Framework of the CEEMDAN-SC-LSTM Prediction Method  
The structure of the proposed CEEMDAN-SC-LSTM prediction model is illustrated in Figure 4. The specific steps are as follows:  
Step 1: Use the CEEMDAN algorithm to decompose the monitoring data into a series of IMF components and a residual term.  
Step 2: Perform spectral analysis on each decomposed modal component. Cluster and aggregate the IMF components with similar frequency domain characteristics, reconstructing them into structural response signals of different scales and amplitudes.  
Figure 4: Prediction Framework Diagram.  
Step 3: The reconstructed sub-signals were divided into a predetermined proportion of training and testing sets. These sets were then fed into the trained LSTM model for prediction.  
Step 4: The predicted results were combined to form the final prediction outcome.
## 3 Results
The graphs for the other two models showed similar trends, but the model trained for 600 epochs achieved the lowest values of the location loss function on the training dataset (Figure 7c). The values presented for the validation set converged, both for the model trained for 217 epochs (Figure 7b) and for 600 epochs. Figure 6: Confusion matrix for a model with 10 epochs (**a**), 217 epochs (**b**), and 600 epochs (**c**). Analysis of the graphs of the classification loss function confirmed the presence of underclassification occurrence in the model trained for 10 epochs (Figure 8a). The values of classification errors were significantly higher compared to the other models. The characteristics of the other graphs were very similar, both for the model trained for 217 epochs (Figure 8b) and for the model trained for 600 epochs (Figure 8c), which allowed them to be considered correct, due to the convergence of the classification loss function values to zero in both cases. Figure 7: Graphs showing the dependence of the classification loss function values on the epoch implemented for each model: model trained for 10 epochs (**a**), model trained for 217 epochs (**b**), and model trained for 600 epochs (**c**). Based on the analysis, it was decided to exclude the model trained for 10 epochs from further consideration. The model did not meet the anticipated criteria and demonstrated ineffectiveness in fulfilling the assigned task. The primary issue stemmed from the limited number of epochs, leading to inadequate model fitting. The subsequent phase in the results analysis involved comparing individual images from the test set to assess the performance of two models. Specifically, the evaluation focused on images used in the detection process by models trained for 217 epochs and 600 epochs. Variations between the two models were identified during the analysis. The model trained for 217 epochs demonstrated notably superior capability in accurately identifying traces associated with recoater streaking. Across the majority of images scrutinized, this model exhibited higher confidence levels in detecting the specified defects attributed to all analyzed defects. Figure 9 shows the results of prediction (probability of detecting analyzed defects). Figure 8: Graphs showing the dependence of the value of the location loss function on the epoch realized for each model: (**a**) model trained for 10 epochs, (**b**) model trained for 217 epochs, and (**c**) model trained for 600 epochs. Figure 9: Probability of detecting analyzed defects: recoater vibration (**a**), super-elevation (**b**), and recoater streaking (**c**).
## 10 Challenges and Open Research Problems
### Regulations  
Due to public safety, privacy, and data protection concerns, there are strict restrictions on use of UAVs by governments around the globe. Both international and national regulations are put in place to ensure that UAVs pose minimal risks to other users of the airspace and to protect people and property on the ground [352]. For UAVs to gain wider acceptability, the public needs to trust that their deployment is in their best interests and will be used safely. For instance, there are valid concerns arising from the knowledge that UAVs have been used for illegal activities such as surveilling or tracking people. In addition, there are concerns over data protection since UAVs can be equipped with cameras and other sensors that can collect data from areas where they do not have authorization. There are also serious concerns regarding public safety, as UAVs can lose control during flight and collide with other aircraft, buildings, etc., or crash into people on the ground [19]. Moreover, UAVs can be equipped with weapons and used to perform remote attacks.  
There are many regulatory barriers that need to be addressed before the full potential of UAVs as part of wireless networks can be realized. One of the commonest issues is restrictions on areas where UAVs can operate. Due to some of the concerns highlighted above, UAVs serving as cellular BSs are not yet approved to operate in many public areas, especially in areas with large crowds. In addition, there are strict restrictions on how far UAVs can fly. Even when a UAV is capable of operating autonomously, many municipalities and cities still require that there be a licensed UAV pilot present before a UAV can be deployed, which increases the operating costs of deploying such UAVs and limits their use. The red tape in the approval of the use of UAVs has also been highlighted as a challenge mitigating against their widespread use. Regulatory and policy issues are usually slow and far behind the advances in UAV development, which in turn curtail the research, development and deployment efforts by both the telecommunication industry and the research community [352].  
Despite some of the current limitations, significant progress has been made in UAV regulations. In Europe, for example, roadmaps have been created on how to integrate UAV operations into the civilian aviation industry [352]. There are similar efforts around the globe to enact regulations that will both promote the wide adoption of UAVs in civilian applications as well as ensure privacy and public safety [352].
# A Systematic Review of Radio Frequency Threats in IoMT
### Radiofrequency Attacks  
An RF attack is a type of hacking that does not require physical contact with the target. Electronic devices are disrupted, damaged, or interfered with by radio waves sent by the attacker. In addition to disrupting internet-connected devices, they can also affect computers, routers, printers, and other IoT devices. Software-defined radios (SDRs) are powerful tools for monitoring, intercepting, and manipulating digital communications. Additionally, they open the door to the internet of things. As SDRs become more prevalent, the threat of IoT hacks will increase. Figure 1 shows the possibilities of physical layer attacks and system attacks by SDR devices. As shown in Figure 1, spoofing, eavesdropping, and man-in-the-middle attacks have a combined 50% coverage when compared to the other attacks. Additionally, spoofing and eavesdropping are more significant than man-in-the-middle attacks in that range.
## 5 Experimental Results
### Test Instances Generation  
To further evaluate the performance of the formulated MILP model, a series of test instances are generated by using the combinatorial nomenclature method. For convenience, the test instances are named J[n]-S[s]-M[u]-A[r]. Recall that n, s and r represent the counts of jobs, processing stages and AGVs, respectively. u represents the maximum count of UPMs in each processing stage, i.e., \(u=\max\limits_{j}\{l_{j}\}\). For example, J3-S2-M3-A2 means that there are three jobs to be scheduled (Job), two processing stages (Stage), at most three UPMs (Machine) in each stage and two AGVs. Moreover, the job processing time \(PT_{i\!jk}\) obeys a uniform distribution on [6; 29], and the job transportation time \(TT_{k}^{\
u}\) obeys a uniform distribution on [1; 7]. Finally, a set of 25 instances are randomly generated.
# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 1 Introduction
Besides, when using ML-based methods, their hyper-parameters, in most cases, require optimization for a better result. The optimization of hyper-parameters and feature selection will generally make the ML-based techniques run more efficiently. The necessity to minimize risk and potential threats to IIoT systems has recently attracted academic interest. Effective IDSs specifically designed for IoT applications must be created. For training and evaluating such IDSs, a current and comprehensive IIoT dataset is needed. For assessing IDS-enabled IIoT systems, however, there are insufficient benchmark IIoT datasets that can be easily accessed or obtained from the internet freely [9,10]. This study uses brand-new, data-driven IoT/IIoT real-world datasets to solve these issues. It contains a label feature that separates the attack and normal classes and a feature that categorizes the threat subclasses that attack IoT/IIoT network nodes for issues with several classifications [11]. In addition, the TON_IoT dataset contains telemetry information for IoT and IIoT services [12]. With various IIoT-based IDS datasets, this study intends to evaluate the generalizability of feature selection techniques and ensemble classifier combinations. The following summarizes the main contributions of the study:  
* To create the best cyberattack multiclass classification model in IIoT systems, a thorough approach is proposed. * This study suggests a feature selection strategy for IDS in the IIoT, utilizing ranked features from the Chi-Square Statistical Model and analyzing the link between feature variance and detection accuracy. * Seven (7) ToN-IoT-based Telemetry datasets were employed to evaluate how well the model performed. In addition, extensive investigations have assessed the performances of an ensemble of ML-based models using these seven datasets. * The performance of the ensemble models was verified by comparing them with the baseline research, which used the datasets and other existing approaches that used the same datasets. The remaining sections of this study are structured as follows: Section 2 presents some related work in IoT/IIoT-based IDS studies. The materials and methods used for the study are covered in Section 3 of the study. Section 4 outlines the outcomes of the experiment that was conducted. Finally, Section 5 concludes the study and provides future perspectives.