esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 5 Evaluation of the Tower Parameters for Machine Learning Estimation Model  
The constant monitoring and identification of damage risk in a transmission line tower are essential for long-term health. At present, the traditional methods to identify risks are to assess the features in a physical tower under harsh testing loads to carry out the reasons for likely damage. However, since these methods are expensive, it is economical and feasible to use long-term health monitoring data and more straightforward methods to identify tower damage risks directly. In addition, machine learning is an attractive method to analyze data and learn from it, making decisions and forecasts for events in the real world. An extra advantage comes when the fusion of input parameters can cover features of the natural phenomenon that simplified analytical methods fail to obtain.  
Among many machine learning algorithms, the developed neural network receives multiple correlated inputs for estimation of another variable of the system, a vital algorithm of estimation modeling machine learning. It is often used to solve classification and regression problems, and the learning and prediction speeds are good. The proposed method uses the multiple input variable fusion method, the transmission line tower displacement, and cable forces to improve the estimation accuracy. The variables collected from the system, such as acceleration, angular velocity, cable tension, temperature, atmospheric pressure, wind direction, and wind speed, are indicatives of changes in tower structural parameters. These parameters are necessary to detect structural failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture.  
Figure 16: Tower displacement simulation diagram. The tags 1, 2, 3, and 4 shows the location of the tensor and accelerometer sensors of each cable.
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.4 Based on Detection Methods  
Detection based on signatures depends on recognized attack patterns. It struggles to identify zero-day attacks but is good at spotting previously known vulnerabilities. Using signature-based ensemble models in centralized IDS, [PERSON] and [PERSON] [74] found recurrent IoV intrusions. RF and DTs were combined to improve detection accuracy for known hazards. Emphasizing the need for additional detection techniques, the study revealed the limits of signature-based methods in handling novel hazards. Using ensemble techniques for signature-based detection on aggregated traffic data, [PERSON] and [PERSON] [69] effectively found recurrent attacks; yet, because IoV environments are dynamic, they require regular updates to keep effectiveness.  
Anomaly Based  
Anomaly-based detection is especially effective in spotting unknown and zero-day attacks. Using classifiers including SVM and Neural Networks, [PERSON] et al. [57] created anomaly-based ensemble learning models. This solution used the adaptability of ensemble methods to detect abnormal patterns in vehicle traffic, therefore addressing the problem of spotting new hazards in IoV systems. Using ensemble techniques, [PERSON] et al. [75] investigated real-time traffic data with an eye toward anomaly detection in edge computing environments. An adaptive feature of anomaly-based detection improved the IDS's ability to handle changing attack strategies and reduced scalability problems.  
Specification Based  
Specification-based detection calls for exact criteria and limits to spot violations. It works especially well for spotting attacks targeted at IoV programs or protocols. [PERSON] et al. [77] improved IoV communication protocol security by using specification-based ensemble learning models. Their strategy reduced false positives and improved system accuracy in finding protocol-specific breaches by combining rule-based detection with ensemble techniques. By means of the integration of specification-based detection and federated learning inside a distributed architecture, [PERSON] et al. [61] addressed privacy issues while preserving exact detection for IoV applications. In distributed systems, this hybrid approach addresses the difficulty of maintaining accuracy while complying with protocol-specific criteria.
## 3 Results
### Power Input Correlation
#### 3.1.5 Validation and Application of Newton Number Models  
The correlation of the power input in Equation (9) is established by experiment as following:  
\[Ne=0.01117(\mathrm{Re}\cdot Fr)^{-0.887}(\frac{V_{L}}{D^{3}})^{-1.048}\mathrm{ at\ water}\left(8.94\,\times\,10^{-4}\mathrm{\ Pa\cdot s}\right) \tag{15}\]  
\[Ne=0.0074(\mathrm{Re}\cdot Fr)^{-0.887}(\frac{V_{L}}{D^{3}})^{-1.116}\mathrm{ at\ 5\%\ glycerin\ }\left(1.35\,\times\,10^{-3}\mathrm{\ Pa\cdot s}\right) \tag{16}\]  
\[Ne=0.0048(\mathrm{Re}\cdot Fr)^{-0.887}(\frac{V_{L}}{D^{3}})^{-1.14}\mathrm{ at\ 10\%\ glycerin\ }\left(2.05\,\times\,10^{-3}\mathrm{\ Pa\cdot s}\right) \tag{17}\]  
\[Ne=0.004(\mathrm{Re}\cdot Fr)^{-0.886}(\frac{V_{L}}{D^{3}})^{-1.186}\mathrm{ at\ 20\%\ glycerin\ }\left(2.4\,\times\,10^{-3}\mathrm{\ Pa\cdot s}\right) \tag{18}\]  
These equations were demonstrated to be valid in the following range:  
\[4.61\times 10^{4}<\mathrm{Re}<1.75\times 10^{5}\]  
\[1<\mathrm{Fr}<2.3\]  
\[0.18<\frac{V_{L}}{D^{3}}<0.46\]  
and when liquid motion was induced in the OSB that can be considered as being fully validated on scale-up.
## 5 Simulation-Based Studies  
This section provides an overview of simulation-based methods in lighting design studies, summarizing key results and methodologies employed. The focus is on the use of simulation tools to assess lighting conditions in healthcare environments (Table 9). Many of these simulations were based on default parameters regarding surface spectral properties. The parameters included visible transmission through windows and reflectance through walls, ceilings, and floors. In the simulation-based research, different software packages were used, and two main approaches were identified: retrospective evaluation, which measures light received at a sensor point to assess effectiveness, and prospective evaluation, which predicts future lighting conditions. Retrospective evaluation is more straightforward because it measures light based on how the human eye responds to light and introduces the concept of \"circadian efficacy\" to assess light quality. This approach is also based on the measuring spectral distribution of light received at a sensor point. However, prospective evaluation is more challenging since few simulation tools can accurately predict how light affects our body's circadian rhythms [72; 73]. In order to fill this gap, a methodology that incorporates existing technology with novel calculations was proposed. In this method, light sources with known spectra were used to calculate their circadian effectiveness. Despite challenges in obtaining spectral information for specific lamps used over their lifespan, the paper relied on standard CIE illuminants and specific lamp spectra from Philips Lighting. To determine the circadian weighting, the radiometric spectra were analyzed for their short-wavelength content. Applying this method to a range of illumination sources resulted in a chart that can be used in a temporally neutral application to assess the circadian potential of a light source [72; 73]. Using a patient-centered approach, researchers estimated whether lighting conditions would be sufficient for circadian entrainment [73]. In one study, circadian stimulus (CS) was calculated at the eye level of a patient, and CS autonomy was calculated. It was calculated by counting days in which CS was greater than 0.35 for at least one hour in the morning, which was hypothesized to be sufficient to entrain circadian rhythms. Using Daysim, dynamic daylighting was calculated and melanopic lux values were converted. Data from this study were used to analyze non-visual effects in healthcare settings throughout the year [74]. In the latest study, visual and non-visual lighting performance in nursing homes was simulated using a methodological framework. ALFA (Adaptive Lighting for Alertness) is an innovative software tool that measures the adequacy of lighting strategies for providing residents with adequate luminous conditions and circadian stimulation to occupants. Testing of proposed improvement strategies demonstrated that robust methodologies and tools can be used to comprehensively address visual comfort, task performance, and cir
## 2 Methods
### Classification Algorithm
The number of trees
2. Maximum depth of each tree
3. Minimum number of samples that must be present in a leaf node
4. Minimum number of samples required to split an internal node
5. Whether to bootstrap samples or not  
For Support Vector Machine:  
1. The regularization strength
2. Decision boundaries
3. The type of kernel function  
For CatBoost:  
1. The maximum depth of the individual decision trees
2. The number of boosting iterations
3. Regularization term that prevents overfitting
4. The step size for updating the model during each iteration  
For TabNet:  
1. Width of the decision prediction layer
2. Width of the attention embedding for each mask
3. Number of steps in the architecture  
To prevent overfitting of the model, cross-validation was also performed. It is an iterative process that divides the train data into k (k = 5) partitions. Each iteration keeps one partition for testing and the remaining k-1 partitions for training the model. The next iteration will set the next partition as test data and the remaining k-1 as train data and so on. In each iteration, it will record the performance of the model and at the end give the average of all the performance. The SVM, Random Forest, and boosting algorithms are well known to provide good results for classification problems based on tabular datasets. In this study, ensemble methods like voting and stack generalization were evaluated to maximize the strength of selected algorithms. A voting ensemble is a machine-learning technique that merges predictions from several models to enhance overall performance. This approach aims to outperform any individual model within the ensemble by leveraging the strengths of multiple models. It has proved to be a useful technique to enhance better results in medical diagnosis like heart disease detection [27] or tuberculosis prediction [28]. Another ensemble-learning technique that was used was stacked generalization (stacking). It involves training a new model to synthesize predictions from multiple pre-trained models. This method enhances predictive performance by leveraging the strengths of individual models, often referred to as base or submodels. Like voting, it was also widely used to enhance the results in medical applications like drug dose estimation [29] or retinopathy diagnosis [30]. The software utilized for this research was Python 3.9.13, the algorithms were implemented using consecutive libraries: pytorch-tabnet 4.1.0, scikit-learn 1.5.2, xgboost 2.0.3, catboost 0.26.1, pandas 1.4.4, numpy 1.23.4, seaborn 0.12.1. The calculations and training that required GPU acceleration were performed on two NVIDIA GeForce GTX 1070 cards (Nvidia, Santa Clara, CA, USA).
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Technical
#### 4.1.4 Complicated Interfacing and Operating System  
Without having a suitable user interface, it is difficult to present the monitored parameters to the end-users. Interfacing between smart clothing and a local cloud-based system via Wi-Fi, Bluetooth or mobile network needs to be developed for storing and analysing continuous monitored health-related data to take preventive steps [148]. A customized operating system (OS) specific to smart clothing can be developed for universal application.
# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
###### Abstract
[PERSON] [51] proposes the statistical region merging (SRM) and a shape context similarity model for Light Detection and Ranging (LiDAR) data. Inspired by the methods above, we propose a novel building segmentation network, which combines semantic segmentation and super-resolution for accurate building segmentation in remote sensing images. Our main contributions can be summarized as follows:  
1. Detail Enhancement (DE) module based on SISR is proposed to enhance the boundaries of buildings. Deep Residual Block is designed to improve the learning ability of the network. This design is to reduce the local information loss caused by the use of convolutional layers and down-sampling in a single Deep CNN which will make negatively impact on the details of the image around the small object. 2. Widely Adapted Spatial Pyramid (WASP) is designed to enlarge the receptive fields for extracting more context information. This design is to multiple scale feature maps with pooling layer and dilated convolution, and integrates feature of different scales step-by-step, which can incorporate neighbor scales of context features more precisely and introducing better feature learning and fitting capabilities. We further apply the improved ResNet50 and Global Feature Guidance to improve the segmentation accuracy. The paper is organized as follows: Section 2 presents our building segmentation network. Section 3 presents the experimental results on two public datasets along with the discussion. Our conclusions are presented in Section 4.
# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 2 Materials and Methods
### Deep Learning Model Architecture  
The FR-CNN is a two-stage object detection network model proposed by [PERSON] et al. [11] in 2015. This model also can work as a single, end-to-end deep convolutional neural network for object detection and evolved from Region CNN (R-CNN) [20] and Fast Region CNN (Fast R-CNN) [21] network models. However, R-CNN relies on the Selective Search algorithm to generate a region proposal network, which takes a lot of time and cost; Fast R-CNN has made a series of improvements to the R-CNN model, the most important of which is to propose the Region of Interest (ROI) Pooling layer. It can extract feature vectors of equal length from all regions of interest (i.e., ROI) in the same image, which can effectively avoid the need for R-CNN to segment and deform the image to meet the input size of the image before performing the convolution operation. Concerning the feature loss, FR-CNN retains the advantages of the previous two methods (R-CNN, Fast R-CNN) and introduces the region proposal generation network Region Proposal Network (RPN) based on Fast R-CNN. In addition, it should be noted that R-CNN and Fast R-CNN models rely on selective search algorithms to generate proposal regions. While Region Proposal Network (RPN) in Faster R-CNN can use the same convolutional layers to process images, Faster R-CNN does not need to spend extra time generating proposals. Then, FR-CNN detection time is greatly shortened, and the accuracy rate is also improved to a certain extent. The structure of the FR-CNN model is shown in Figure 1.  
The initial step in the processing pipeline involves reshaping the raw graphite ore image to conform to the input size specifications of the backbone extraction network residing in the Convolutional Layers. The backbone extraction network, subsequently, engages in the extraction of essential features from the reshaped image through a series of computational operations. Once the feature map has been generat  
Figure 1: FR-CNN model structure.  
to the Regional Proposal Generation Network (RPN), which is responsible for generating a proposal frame that encapsulates the target object within the image. Subsequently, the proposal frame and the feature map are transmitted to RoI Pooling, which performs a pooling operation on the feature map to crop out the proposal frame, thus generating a fixed-size rectangular box. Finally, the feature map within the rectangular box undergoes classification and regression predictions through full connection to yield the desired output.
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 3 State-of-the-Art Review
### Security  
Security is one of the main requirements in real-world IoT deployments [56]. Most of the IoT devices share a simple design that is based on the premise that they can be operated remotely and integrated with third-party applications through simple mechanisms [57]. The pressure of releasing a device quickly can, in some cases, lead to skipping non-visible aspects like security and reliability. It is obvious that security concerns are not always considered as part of the IoT device production life cycle, such as hardware and firmware in the bottom layers, but also in higher layers, such as frameworks and applications. Many IoT devices are not supported with the ability to update the firmware/software (i.e., typically cable-based or over-the-air updates), turning them extremely exposed and vulnerable to eventual exploits and attacks [58]. Security must protect services, devices, information, and data, not only during communication but also data storage [45].  
To protect privacy, it must be ensured that communication and collected data met the following requirements, as defined in [59; 60; 61]:  
1. Confidentiality: transmitted data, communication between endpoints, sensors, and readers are secured and encrypted;
2. Integrity: transmitted data is accurate and cannot be modified or utilized, by unauthorized users and objects;
3. Authenticity: transmitted data is genuine, and come from authorized sensors, end-points, and readers;
4. Availability: computing resources and information are available when requested by a service.
# Classification of Malicious URLs by CNN Model Based on Genetic Algorithm
## 3 Results
### Convolutional Neural Network
The loss rate of the training set is 0.170, and the loss rate of the test set is 0.169, indicating that the network is moderately fitted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Convolution Kernel** & **Pooling Kernel** & **Stride** & **Channel Size** \\ \hline Model A & \(1\times 5\) & \(1\times 2\) & \(1\) & \(32\) \\ Model B & \(1\times 5\), \(1\times 5\) & \(1\times 2\), \(1\times 2\) & \(1\) & \(32,32\) \\ Model C & \(1\times 5\), \(1\times 5\) & \(1\times 2\), \(1\times 2\) & \(1\) & \(64,64\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different parameter settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Model A** & **Model B** & **Model C** \\ \hline Accuracy & 93.64 & 93.72 & 93.99 \\ Loss & 0.191 & 0.180 & 0.169 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results of different network models. As shown in Figure 7, the accuracy of the prediction results is 93.99%, indicating that the model has a good effect on malicious URL classification. Table 6 shows the running results of the model ( CNN ( GA ) ) and various traditional machine learning models in this data set. The first five models are classified based on the 34 features extracted in this paper. KNN selects the optimal K value 10 through grid search, and the network structure of CNN is the same as that of CNN ( GA ). It can be seen from the results that the CNN model based on genetic algorithm has improved the accuracy of malicious URL recognition. The ROC curve of the model is shown in Figure 8, obtained by  
\[\text{TPR}=\frac{\text{TP}}{\text{P}}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{16}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **KNN** & **GaussianNB** & **LogisticRegression** & **SVC** & **CNN** & **CNN (GA)** \\ \hline Accuracy & 0.921 & 0.715 & 0.896 & 0.915 & 0.932 & 0.940 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of recognition accuracy of multiple models. Figure 6: Loss values for training and prediction. Figure 7: Accuracy of prediction results. \[\text{FPR}=\frac{\text{FP}}{\text{N}}=\frac{\text{FP}}{\text{TN}+\text{FP}} \tag{17}\]  
calculated, the AUC area of the model is 0.99, which has a predictive value. Malicious URL classification using SVM and random forest models. The ROC curve is obtained by calculating TPR and FPR through formula (16) and formula (17). As shown in Figure 8, the AUC of SVM is 0.95, the AUC of random forest is 0.98, and the AUC of the CNN model used in this paper is 0.99. It is concluded that the prediction results of this model are valuable and better than other models.
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 3 General Scattering Method for Suspended Particles
### Numerically Exact Methods
The DDA method has been extensively used to simulate light scattering of oceanic particles. For instance, the light scattering of _Emiliania luxleyi_ coccolithophore was simulated while using the DDSCAT by [PERSON] et al. [41] and using the ADDA by [PERSON] et al. [42]. Another example of the use of ADDA is the light scattering of dinoflagellates by [PERSON] and [PERSON] [43], where the chiral structure of the chromosomes is implemented by using discrete dipoles. This chiral structure leads to optical activity for certain dinoflagellates and another reason for measuring the complete single scattering Mueller matrix, which should be a fruitful area of research in remote sensing of the oceans. The typical feature of a numerically exact method is that the error asymptotically approaches zero if the corresponding numerical grid that is associated with the method asymptotically reaches zero. Another feature for oceanic particles is that the convergence rate is much faster than the convergence rate of atmospheric particles because the relative refractive indices with respect to oceanic particles are close to unity. Moreover, the composition of a particle using the numerically exact methods can be arbitrary, homogeneous or inhomogeneous, or even different grid by grid. For all of the numerically exact methods, the Mueller matrix is given in terms of the amplitude scattering matrix and they both depend on the incident direction. Consequently, the light scattering computation of a particle in random orientation while using these methods can usually be given by  
Figure 2: Discretization of a particle volume in the discrete-dipole approximation (DDA) method. numerically summing the light scattering for different orientations. The convergence in the random orientation computation becomes significantly more difficult with increasing particle size so the computation will become time-consuming.
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Mining Slope Stability Analysis Lower Extension Mining
#### 4.2.2 Analysis of Slope Displacement Evolution  
As shown in Figure 9a,b, the displacement contour maps in the z-direction and x-direction, respectively, after the completion of slope excavation are presented, while Figure 9c shows the displacement diagrams for each excavation stage. Due to the substantial disturbance caused by excavation activities and the resulting critical surface effects, the slope body inevitably experienced deformation. In the vertical direction, the slope exhibited significant settlement, with a maximum displacement of 72 cm, indicating a clear settlement trend. In the horizontal direction, a displacement of 65 cm was recorded, reflecting considerable deformation. The displacement monitoring curve further reveals a sharp transition during the 13 th excavation stage, highlighting a significant shift in the slope structure. This underscores the profound impact of excavation activities on slope stability and the critical transitions that occur during deeper excavation stages.