esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Overall Vision  
Cyber-technical systems (CPS) use wireless technologies, sensing and distributed decisions based on ubiquitous computing and networks with potential benefits in very diverse areas of human activity, such as [1] the environment [2], security [3], transport systems [4], emergency management [5], discovery of bombs, mines and unexploded ordnance [6,7] and leisure and tourism [8].  
Recent survey articles [9, 10] have reviewed research on sensor-aided CPSs that enable intelligent and fast response to emergencies, such as fires, earthquakes or terrorist attacks. The present paper will focus on projecting past and recent research in emergency management systems towards research opportunities.  
Real-time monitoring and quick response are inherent requirements for emergency response. During a fire, different types of sensors can cooperate and interact with the evacuees and the environment. Temperature and gas sensors can help monitor the spread of hazards. Rotatable cameras track the spread of the fire and the movement of civilians. Ultrasonic sensors can range the distance to obstacles in the environment and monitor dynamic changes of maps, due to sudden changes in some built structures through destruction and debris. Smart evacuation schemes can help evacuees using the cooperation between first-aid decision nodes, sensors and civilians with mobile devices. Evacuees with mobile devices can follow personalised navigation paths with distributed decisions that mitigate congestion. Those without mobile devices may follow audio or visible LED directions. Grid/cloud-supported computing can gather sensing information and dynamically predict the movement of evacuees and hazards to make the best decisions regarding resource allocation and response. A search may also be conducted with the help of robotic devices, despite their limited autonomy [11].
###### Abstract  
This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities.  
offshore maintenance; corrosion prediction; maintenance optimisation; machine learning; multi-criteria decision making; reliability centred maintenance  
29 October 202417 November 202  
time with the right equipment is essential to ensure that the facility remains in a reliable operational condition [2].
## 3 Improved AEGG-FD Structure
### SloU Loss  
In the target detection task, the intersection over union (IoU) loss function is used to measure the degree of overlap between the prediction and real boxes. It avoids the interference of bounding box sizes using a proportional form, which allows the model to efficiently learn various objects of different sizes when the IoU is used as a boundary regression loss. The Complete-IOU (CIoU) loss functions used in YOLOv5 add consideration of the aspect ratio based on Distance-IoU (DIoU), however they both ignore the mismatch in orientation between the real and prediction boxes. This leads to slower convergence and less efficient models. Based on CIoU, new penalty metrics are defined by importing the vector angle, which produces the SCYLLA-IoU (SIoU). This operation is performed to accelerate the regression of the prediction box to the closest axis of the real box. In brief, the total degrees of freedom of the loss are effectively reduced by adding the angle penalty cost, which makes the model converge faster and more accurate.  
The SIoU loss function consists of four functions: angle loss, distance loss, shape loss, and IoU loss. The calculation formula is as follows:  
\[\Lambda=1-2\sin^{2}\Bigl{(}\arcsin(x)-\frac{\pi}{4}\Bigr{)}, \tag{3}\]  
\[\Delta=\sum_{t=x,y}\bigl{(}1-\mathrm{e}^{-\gamma\rho_{t}}\bigr{)},\gamma=2-\Lambda, \tag{4}\]  
\[\Omega=\sum_{t=w,h}\bigl{(}1-\mathrm{e}^{-w_{t}}\bigr{)}^{\Theta}, \tag{5}\]  
\[\mathrm{IoU}=\frac{\left|\mathrm{B}\cap\mathrm{B}^{\mathrm{GT}}\right|}{ \left|\mathrm{B}\cup\mathrm{B}^{\mathrm{GT}}\right|}, \tag{6}\]  
\[\mathrm{Loss}_{\mathrm{SIoU}}=1-\mathrm{IoU}+\frac{\Delta+\Omega}{2}, \tag{7}\]  
where \(\Lambda\) is the angular loss that allows the prediction frame to reach the horizontal or vertical position of the real box faster, \(\Delta\) is the distance loss, and \(\Omega\) is the shape loss. The calculation process of angular loss is shown in Figure 12, where A is the prediction box, B is the real box, \(C_{w}\), \(C_{h}\) are the horizontal and vertical distances between the prediction box and the real box, and \(\sigma\) is the distance between the center points of these two boxes.  
Figure 12: Schematic diagram of SIoU.  
Consequently, the training and inference capabilities of the detection model are improved using the SIoU loss function. Meanwhile, the NMS operation becomes more rational and effective due to the usage of SIoU.
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
Recent achievements have finished in Japan [35], China [36], Korean Peninsula [37], African Region [38], Antarctic Region [39], the low latitude region [2], the sea region [31], etc. For example, a European TEC prediction model [40] based on thin-plate splines (TPS) interpolation was self-calibrated, making the model better accurate during geomagnetic storms. [PERSON] [41] combined a neural network (NN) algorithm and wavelet decomposition algorithm to establish a short-term forecast model of TEC, which can adapt to various ionospheric environments more flexibly and has good robustness. A regional TEC model was established using the single-layer model and proved superior to the IRI model [42]. Constantly updated data measurement methods, data processing techniques, and modeling methods are designed to improve TEC prediction accuracy for military and civilian applications. The TEC estimating methods can divide into the Long-term Prediction Method (LTPM) and the Short-term Forecast Method (STFM). LTPM is often used to predict the monthly statistics of VTEC, and STFM refers to forecasting TEC for several hours or days in the future. Unlike STFM, which pays more attention to the diurnal variation of TEC, LTPM pays more attention to the periodic variation trend of TEC monthly median value [43]. Considering the previous investigation, the prediction accuracy of the IRI model in some parts of Europe was low due to the complex dynamic change characteristics of the ionosphere in Europe, which needed to be urgently improved [42]. Compared with previously used ML methods such as RNN [21], LSTM networks [26], traditional NN [35], etc., the proposed ML-based modeling method is to make the model interpretable and simple. To improve the TEC long-term prediction accuracy in Europe, we propose an ML-based modeling method and use this method to establish an empirical prediction model of TEC for parts of Europe. This paper is organized as follows. First, Section 2 will specifically describe model creation and parameters determination. Then, the validation of the model and comparison with other models will be presented in Section 3. Finally, Section 4 will summarize the article and propose the direction for further improvement in the follow-up work.
## 1 Introduction
With this new and effective way of order fulfillment and product configuration for the new manufacturing demand of mass customization for high-complexity and low-volume production, there is a new research gap that needs to be bridged [8; 33]. The key to success is quickly and effectively reacting to diverse and dynamic customer requirements, and bringing the customized products to market in a shorter period of time. Hence, the objective of this research is to develop a novel model of order placement with product configuration to dynamically respond to customer order requirements. To meet this aim, several challenges have to be addressed: (1) There is no effective communication channel between manufacturers and customers. Currently, manufacturers only provide fixed assembled parts without direct communication with customers. As a result, a manufacturer may fail to swiftly respond and fulfill customer requirements. The disadvantage may even accumulate in the future. (2) Customers may not express their requirements clearly and thoroughly. (3) There are many constraints, such as budget and manufacturing capacity, in meeting user requirements. To address the above challenges, the following is considered: (1) Engineering characteristics (ECs) are defined and selection pools are constructed to consider constraints of manufacturing capacity: (2) A mathematical programming model is applied to maximize the overall customer satisfaction level and recommend optimal product configuration. (3) Sensitivity analysis is conducted to provide customers with more selections. These methods are embedded in the proposed novel and adapted configuration system. The rest of the paper is organized as follows: Section 2 gives an introduction to the related studies. Section 3 introduces the framework of customer requirement modelling with an optimization approach and the key phases to integrate ECs, customer requirements (CRs), and customer satisfaction (CS) into the optimization model. A practical case study is illustrated in Section 4 with the case of laptop customized production. In addition, a new scenario with a product configuration recommendation system design is described. The authors conclude this work with a discussion of the industrial implications and contributions in Section 5.
# Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint
###### Abstract  
Transmit beamforming (TBF) provides the capability of focusing illuminating power in the desired directions while reducing the emitting power in undesired directions. It is significantly important in low-altitude and slow-speed small (LSS) radar, which usually suffers from heavy clutter and rapidly changing interference on the near-ground side. Due to nonideal factors such as an inaccurate target direction and array gain-phase error, the robustness of TBF is also necessary to consider in practical applications. In this paper, we provide a robust TBF method that enables sidelobe control in preset regions and possesses high transmit efficiency in virtue of the peak-to-average-power ratio (PAPR) constraint on transmit weights. To achieve robustness, a norm upper bound is introduced to limit the fluctuation of transmit weights, and the steering vector mismatch is also considered by using a spherical uncertainty set surrounding the nominal steering vector. As the proposed robust TBF is nonconvex because of the nonconvexity of both the objective function and constraints, we translate it into a series of convex subproblems via several kinds of convex relaxation schemes. In particular, based on the special structure of the objective function and constraints, the translation of the nonconvex problem into a tractable SOCP problem is realized by using the combination of the triangle inequality and Cauchy-Schwartz inequality. Numerical results demonstrate the improvement in the efficiency and robustness of the proposed TBF method in comparison with traditional TBF methods.  
robust transmit beamforming: peak-to-average power ratio; sidelobe control
## 2 Geometric Noise Analysis
### Piston Noise  
After geometric calculation, the piston noise is  
\[\Delta b=|\overrightarrow{P_{1}P_{2}}|+|\overrightarrow{P_{2}P_{3}}|=\frac{2 \cos(\alpha+\varphi_{1}-\varphi_{2})}{\cos(2\varphi_{1}-\varphi_{2})}[\Delta x \sin\varphi_{1}-\Delta y+\Delta y\cos\varphi_{1}] \tag{4}\]  
When \(\varphi_{2}\) approaches 0 and \(\alpha=\pi/4\), then \(\Delta b=\frac{2\cos(\pi/4+\varphi_{1})}{\cos(2\varphi_{1})}[\Delta x\sin \varphi_{1}-\Delta y+\Delta y\cos\varphi_{1}]\). We can examine Equation (4) in two cases: when \(\Delta x=0\) and when \(\Delta y=0\).  
When \(\Delta x\) approaches 0, then \(\Delta b=-\Delta y\frac{2\cos(\pi/4+\varphi_{1})(1-\cos\varphi_{1})}{\cos(2 \varphi_{1})}\).  
Perform numerical fitting on the trigonometric function part, as shown in Figure 3 below.  
It can be determined that a part of the piston noise is approximately  
\[\Delta b_{y}\approx-\frac{\sqrt{2}}{2}{\varphi_{1}}^{2}\Delta y. \tag{5}\]  
When \(\Delta y\) approaches 0, then \(\Delta b=\Delta x\frac{2\cos(\pi/4+\varphi_{1})\sin\varphi_{1}}{\cos(2\varphi_ {1})}\).  
In the same way, its numerical fitting is performed, as shown in Figure 4 below.  
It can be determined that another part of the piston noise is approximately  
\[\Delta b_{x}\approx\sqrt{2}\varphi_{1}\Delta x. \tag{6}\]  
By combining Equations (5) and (6), the piston noise is  
\[\Delta b\approx\sqrt{2}\varphi_{1}\Delta x-\frac{\sqrt{2}}{2}{\varphi_{1}}^{2 }\Delta y \tag{7}\]  
Figure 3: Numerical fitting graph of partial piston noise.  
The piston noise is first-order related to the lateral and longitudinal jitter of the mirror. Improving laser alignment accuracy can effectively reduce piston noise. The piston noise exhibits a first-order and a second-order relationship with the angle jitter.  
By combining Equations (3) and (7), the TTL noise is  
\[\Delta=\Delta\mathbf{a}+\Delta b\approx\sqrt{2}\Delta x\varphi_{1}-\left(\frac{ \sqrt{2}}{2}\Delta y-2d\right)\times{\varphi_{1}}^{2} \tag{8}\]  
The noise model comprises two second-order terms and one first-order term. When the angle jitter is very small, the second-order term is much smaller than the first-order term and can be neglected.
## 4 Results and Discussion  
### Determination of \(\kappa_{\eta}\) and \(k_{\eta}\)  
The parameters of \(\kappa_{\eta}\) and \(k_{\eta}\) are involved in the Green's and the scalar functions, respectively. The relationship between these parameters can be found in Equation (49). From Equation (49), we can obtain the following equation:  
\[f\big{(}\kappa_{\eta},k_{\eta}\big{)}=\frac{b_{1z}}{a_{11z}}-\frac{b_{3z}}{a_{3 1z}}=0. \tag{52}\]  
From the first root of the solution of Equation (52), we obtained the following relationship [20]:  
\[k_{\eta}=\begin{cases}1.555-0.076\;\kappa_{\eta}+7.09\times 10^{-2}\kappa_{\eta}^ {2}-4.20\times 10^{-3}\kappa_{\eta}^{3}&\big{(}\kappa_{\eta}\leq 15\big{)}\\ 0.652-9.95\times 10^{-3}\;\kappa_{\eta}&\big{(}\kappa_{\eta}>15\big{)}\end{cases}.\]  
In Reference [22], the value of \(\kappa_{\eta}\) was determined by analyzing the angular dependence of the AE amplitude generated by the leak. However, it is desirable to predetermine the value of \(\kappa_{\eta}\) before the leak occurs in order to apply it to the actual leak source localization. For the determination of the \(\kappa_{\eta}\) value by applying an external excitation to the surface of the cylinder, twelve sensors were mounted on the surface of the cylinder at 30-degree intervals from 0 to 270 degrees at a point 10 cm away from the actuator position. Figure 2a shows the mean amplitude with the \(\sigma\) value of each sensor signal produced by an external excitation (1.000 V\({}_{\text{PP}}\) and \(\
u=150\) kHz). It can be seen that the angular dependence of the observed signals on the external excitation is clear. Two minima appeared close to 90 and 270 degrees. The dependence shows \(\pi\) symmetry. If the external excitation is treated as the Green's function, the angular dependence of the signal amplitude can be related to the Bessel function \(J_{0}\big{(}\kappa_{\eta}\xi\big{)}\) in Equation (3). From Equation (50), the angular dependence of \(u_{\text{leak}}\) was simulated for different values of \(\kappa_{\eta}\) and \(\
u=150\) kHz.
## 7 Conclusions and Future Work  
This research presents a new automated and intelligent intrusion detection system which was modeled, implemented, and evaluated. The proposed predictive IDS utilizes machine learning techniques to detect and classify network activity in an IoT system. Particularly, five supervised learning models have been used, including shallow neural networks (SNNs), decision trees (DT), bagged trees (BT), support vector machine (SVM), and k-nearest neighbor (kNN). The developed models were evaluated on a recent broad dataset known as the IoTID20. Additionally, the features' engineering approach was used with the dataset to increase the accuracy of the machine learning models. We used the confusion matrix metric to evaluate our models. As a result, our detection models recorded 100% for all machine learning models mentioned above. Furthermore, our classification models recorded 100% for the SNNs, DT, and BT, while KNN and SVM recorded 99.80% and 99.40%, respectively.  
Moreover, we will evaluate our predictive models with multiple IoT system datasets. In the future, we will seek to incorporate more datasets to develop a comparative study that compares the selected ML algorithms using several datasets. This will enrich the detection ability to detect more attack vectors in addition to those mentioned in this paper. Additionally, we believe that real-world deployment of the proposed IDS and ICS in various IoT or CPS networks (such as the internet of autonomous vehicles) is essential for more precise implementation representation and practical investigations. Furthermore, one can employ the deep neural networks or the log-linear neural networks [48]-based intrusion detection system to provide deeper detection for the sub-categories of the stated attack vectors.  
Conceptualization, A.A.A. and Q.A.A.-H.; Methodology, A.A.A. and Q.A.A.-H.; Software, A.A.A.; Validation, Q.A.A.-H., A.T. and A.A.; Formal analysis, A.A.A., Q.A.A.-H. and A.T.; Investigation, Q.A.A.-H. and A.T.; Resources, A.T.; Writing--original draft, A.A.A., Q.A.A.-H. and A.T.; Writing--review & editing, A.A.A., Q.A.A.-H., A.T. and A.A.; Visualization, A.A.; Funding acquisition, A.A.A. and A.A. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant No. IFIP: 701-611-1443. The authors, therefore, acknowledge with thanks DSR for technical and financial support.  
Not applicable.  
Figure 15: MRMR algorithm.  
Data Availability Statement: The dataset employed in this research can be retrieved online from: [[https://sites.google.com/view/iot-network-intrusion-dataset/home](https://sites.google.com/view/iot-network-intrusion-dataset/home)]([https://sites.google.com/view/iot-network-intrusion-dataset/home](https://sites.google.com/view/iot-network-intrusion-dataset/home)) (accessed on 11 June 2022).
## 4 Results and Discussion
### Consensus Degree of the Probability Scores  
We better analyzed the behavior of the classification schemes on the independent hold-out test set, by inspecting the correlation between the probability scores resulting from each couple of schemes as shown in Figure 5. It is important to underline that the RF algorithm exhibits the lowest correlations between the probability scores for the different training strategies, showing an inherent instability with respect to the sampling methods;  
Figure 4: (**A**) Importance scores of the features averaged across the rounds to obtain a single feature importance vector for each classification scheme. (**B**) Variability across rounds of each feature for each classification scheme expressed as the quartile coefficient of dispersion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Scheme** & **SENS** & **SPEC** & **ACC** & **AUC** \\ \hline RF & \(0.34\pm 0.03\) & \(0.87\pm 0.02\) & \(0.69\pm 0.01\) & \(0.66\pm 0.01\) \\ RF SUB & \(0.62\pm 0.003\) & \(0.58\pm 0.03\) & \(0.60\pm 0.01\) & \(0.66\pm 0.01\) \\ RF SMOTE & \(0.49\pm 0.03\) & \(0.83\pm 0.01\) & \(0.71\pm 0.01\) & \(0.73\pm 0.01\) \\ Logit Lasso & \(0.23\pm 0.02\) & \(0.84\pm 0.02\) & \(0.62\pm 0.01\) & \(0.66\pm 0.006\) \\ Logit Lasso SUB & \(0.65\pm 0.08\) & \(0.57\pm 0.04\) & \(0.60\pm 0.01\) & \(0.64\pm 0.01\) \\ Logit Lasso SMOTE & \(0.65\pm 0.03\) & \(0.65\pm 0.01\) & \(0.65\pm 0.01\) & \(0.67\pm 0.004\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the ML models for for the hold-out set (mean \(\pm\) std).  
on the contrary, the Logit Lasso algorithm shows the highest correlations between the probability scores, underlining better stability of performance and less variability among the sampling strategies. This issue is of paramount importance in the clinical setting, where stability and generalization of algorithms supporting diagnosis are highly recommended. These two aspects are closely linked to each other since we need to evaluate how changes in the composition of the learning set (i.e., sampling randomness) influence the function produced by the algorithm (i.e., the probability scores) [45; 46; 47].