esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Security and Privacy Requirements  
Both security and privacy are critical to securing communications for 5G-enabled vehicular networks. Therefore, the proposed scheme should satisfy the following security requirements:  
* Message Integrity and Authentication: The receiver must check the integrity and legitimacy of all received messages to ensure secure communication. It must also check messages for tampering during transit.
* Identity Privacy Preservation: The message sent from a registered vehicle should be anonymous and should not use the actual sender's identity to preserve the user's privacy and to prevent privacy breaches.
* Traceability and Revocability: Only the TA can disclose the identity of a vehicle to prevent attackers from forging broadcast messages to avoid accountability and liability for road accidents. Furthermore, TA should have the ability to revoke any malicious vehicle's certificate from future use.
* Unlinkability: To ensure user's privacy, third parties, including adversaries, should not be able to link multiple messages to the same sender.
* Resistance to Security Attacks: The proposed scheme must withstand various known attacks such as modify, replay, impersonation, and password-guessing attacks.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 2 Materials and Methods
### The Ensemble Feature Selection Approach  
A dataset could contain three types of features: relevant, redundant, and noise. The category of the feature selection (FS) method: filter, wrapper, or embedded, is defined by the mechanism that evaluates the relevance of the features: statistical tests or cross-validation. The analysis performed by FS methods defines a ranking of feature relevance in the filter-based techniques, a subset of relevant features in wrapper methods, or a subset of features with a learning model in the embedded methods. The rankings of features generated by filter methods are used to select the k highest-ranked features.  
Considering ensemble learning, the consensus of several experts improves the creation of a decision in a context [24]. Thus, we decided to use the results of our previous research, where we built a framework of ensemble feature selection [25]. This considers the pooling of \(n\) FS algorithms by aggregating their results in a unique subset of relevant features. This scheme is described in Figure 2 and defined in [26] as a heterogeneous centralized ensemble, where single methods represent each FS method used to select a subset of relevant features, outcomes of single methods are the subset generated, pooling is the process to aggregate all subsets of relevant features, and relevant features are the result of the pooling process.  
Figure 2: Heterogeneous centralized ensemble of FS algorithms.  
The EFS method described in [25] uses an importance index (II) to aggregate the subsets generated by the \(n\) FS algorithms. First, the subsets of features generated by each FS method build a set SUM with all selected features. Then, for each feature in the subset SUM, the importance index is computed according to Equation (15). Thus, the number of times that feature \(i\) is presented in the subset SUM (\(FF_{i}\)) is divided by \(n\) to calculate its importance index. Finally, the EFS selects the features with an importance index greater than a threshold defined by the user.  
\[IF_{i}=\frac{FF_{i}}{n} \tag{15}\]  
The main objective of the ensemble feature selection approach is to reach a consensus among several FS methods to generate a subset of relevant features capable of representing the advantages of all used methods and face the biases of the single methods by compensating their disadvantages with the benefits of the others. Thereby, the result of EFS is a subset of relevant features that could improve the performance of subsequent analyses, such as classification processes.  
Although the EFS implemented in the framework could be configured with different FS algorithms, in this study, we used five FS algorithms, three based on rankings of features (ANOVA, chi-squared, and mutual information), one wrapper (importance of features calculated by decision trees), and one embedded (recursive feature elimination--RFE). Each single FS algorithm generated a subset of relevant features, which the EFS aggregates.
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 2 Materials and Methods
### Data Analyses
#### 2.3.2 Convolutional Neural Networks
A total of 9500 images of each labeled particle class were used as a training set to train the network to predict the particle class. Two thousand images of each labeled particle class were used as a validation set to tune the parameters of the classifier. Additionally, 2000 images of each labeled particle class were used as a test set to assess the performance of the classifier. The particle images were resized to 150 \(\times\) 150 pixels and normalized in terms of intensity before entering the network. Table 1 shows the specific structure of the network, which added three custom fully connected layers to the traditional VGG16 convolutional base, contained a total of 23.63 million parameters, and used categorical cross-entropy as the loss function, RMSprop as the optimizer, and accuracy as the metrics. The networks were trained for 100 epochs, and the batch size was 128. All layers were implemented in Keras 2.3.1 (TensorFlow 1.14.0 backend). The calculations were performed using Windows 10 on an NVIDIA GeForce GTX 2070 machine. Figure 1: Illustration of the transfer learning workflow. The VGG16 network was trained on the ImageNet dataset, and the convolutional base of the trained network plus the custom three-layer classifier formed the network used in this study. The fully connected layer of this network was trained using particle images obtained from FlowCam (the transferred convolutional base was frozen) to achieve the classification of seven classes of particles. CNN: convolutional neural network.
## 4 Results and Discussions
### Experimental Settings  
In this work, the maximum number of fitness evaluations (Max_FEs), which is set to 50,000 [29], serves as the terminating criterion. In addition, to verify the effectiveness of SOS, five state-of-the-art algorithms including across neighborhood search (ANS) [59], biogeography-based learning particle swarm optimization (BLPSO) [60], competitive swarm optimizer (CSO) [61], chaotic teaching-learning algorithm (CTLA) [62], and levy flight trajectory-based whale optimization algorithm (LWOA) [63] are used for performance comparison. These five methods keep the original algorithm parameters, except the population size _ps_, setting the same unified value 50 for fair comparison. For each case, each method runs 50 times independently.
## 5 Smart Radio Environment (SRE) with IRS
### Achieving a SRE through IRS
#### 5.1.2 Potential Concepts for SRE  
Based on the discussed use cases, SRE can support the following potential concepts:  
* Smart cities: An IRS with large transverse sizes can be used to coat the facades of building in cities to increase spectral efficiency, enhance coverage and reduce the exposure to EM radiation in outdoor environments by replacing most BS infrastructures with IRS.
* Smart clothing: Embedding smart sensors and metamaterials into clothing can create wearable body networks for monitoring the health of people.
* Smart homes: The interior walls or ceilings at home can be coated with sizeable IRSs to enhance the local connectivity of devices (mobile phones, tablets, IoT devices etc.).
* Smart buildings: In buildings, large windows can be replaced with low-cost IRSs to achieve a better indoor to outdoor communication.
* Smart malls: IRSs can be coated within a small that will improve spectral efficiency and provide the necessary connectivity, shop map and localization information to a large number of users simultaneously.
* Smart hospitals: By deploying an IRS in hospitals, local coverage can be enhanced without the need of increasing the transmitted power and enhance the local connectivity of IoT devices.
* Smart factories: An IRS can improve the performance of smart factories by enhancing the coverage and the transmission rate to support efficient low latency machine-type communication.
* Smart university campuses: An IRS can improve the connectivity within a university campus by coating the exterior of large buildings, and indoors of offices, classrooms delivering reliable connection around the campus.
* Smart airport: In airports, large numbers of users may take different directions when disembarking from airplanes. IRSs can be employed for steering different beams toward different hallways to enhance the quality of the received signals. Also, IRSs may be used to enhance the signals in high-speed download areas, e.g., Internet areas in close proximity of the gates.
* Smart Stadiums: In stadiums by deploying several IRSs, wireless network capacity can be maximized to provide the necessary connectivity to serve multiple devices of large numbers of users simultaneously.  
* Smart train station: In train stations, several users may be waiting on the platforms before the arrival of trains. IRSs can be deployed for illuminating areas of the platforms r to enhance the received signals of individual users or clusters of users.
* Smart underground car parks and tunnels: An IRS can be fitted in a location with clear LoS to the BS and serve users in undergrounds car parks and tunnels and provide localized information for a guiding map application.
* Smart cars: Cars can be coated with IRSs to provide reliable communications within the car itself, as well as serving as enhanced vehicle-to-vehicle and vehicle-to-infrastructure communications.
* Smart trains: The interior of trains may be coated with IRSs to provide a better signal coverage for passengers and reduce the levels of EM exposure of passengers.
## 3 The Proposed Protocol
### Dynamic Increase in Sensor Nodes  
In order to adapt or meet the continuous medical needs of WBANs, the addition of new body area sensor nodes is undoubtedly necessary. When a new body area sensor node \(S_{t}\) joins WBANs, \(S_{t}\) only needs to initiate a registration request as in Section 3.2.1. After \(S_{t}\) is successfully registered, \(GWN\) broadcasts \(S_{t}^{\prime}\)s identity \(SID_{t}\) and stores \(SID_{t}\) in identity set \(S_{M}\).
# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Applications, Methods, and Tools
Only a few studies address the challenge by introducing XAI for effective human-machine collaboration. [PERSON] et al. [72] proposed a context-aware recommendation system for battery management that integrates XAI principles through natural language. Prescriptive actions, like advising a lower charging rate, are paired with contextual cause (e.g., high state of charge) and human-readable rationale, enhancing user compliance and understanding. Similarly, [PERSON] et al. [88] emphasise integration of XAI to ensure technical validation and inform replacement versus repair decisions under economic and environmental constraints. [PERSON] [73] highlights that interpretable ML methods help operators understand when and why specific maintenance actions are recommended, reinforcing decision accuracy. A significant body of PsM relies on correlation-based ML models for the prediction. These models are often used to answer business-relevant questions, such as \"If we perform a certain maintenance action, how will it affect our operational goals?\", to inform subsequent decisions. However, the approach has a fundamental challenge, and that is, most of the AI (and ML) approaches in PsM used for classification and regression are purely associative; they can recognise patterns and associate them with a particular label but are unable to distinguish cause from effect. This limitation is critical because the goal of PsM is inherently causal--it attempts to intervene in a system to achieve a desired outcome. To put it in perspective, in a technical value chain, a process fault can originate long before it is detected. The ML algorithm that only _detects_ a fault after it has occurred is far less valuable than one that _predicts_ it from the earliest process step. Hence, this limits the true detection period and delays preventive actions. The need for a dual-layer architecture, i.e., \"Predict-and-Prescribe,\" combined with causally grounded explainability, positions PsM as a pillar for a sustainable, safe, and cost-effective asset management strategy. Although this depicts the future of PsM, given that it converges neural-symbolic learning, causal inference, and a human-in-the-loop approach to ensure technically robust and socially acceptable solutions, the work is still theoretical.
## 4 Artificial Intelligence (AI) in Sports Performance Analysis
### How AI Can Contribute to Sports Performance Analysis
For example, wearable devices coupled with AI can track an athlete's heart rate, speed, and oxygen levels in real time, predict potential health risks based on past data, and suggest personalized training programs to optimize performance and recovery [108]. In this way, AI offers an unprecedented leap forward in the field of sports science. AI's insights are not merely theoretical; they have practical applications in real-world scenarios, influencing field strategies and becoming a transformative force in the sports sector [109]. AI's predictive capabilities play a pivotal role in injury prevention, with algorithms assessing data from wearable devices to predict potential injury risks and recommend appropriate training modifications [101]. Over the years, the integration of AI technology into sports analysis has led to a growing number of products catering to various solutions. Examples include GPS-based systems, video analysis tools, and motion tracking solutions like those offered by Catapult SportsTM, STATSportsTM, HudIM, Second SpectrumTM, ShotTrackerTM, and KINEXONTM. These innovations have significantly expanded the options available for coaches, athletes, and analysts in their pursuit of data-driven performance improvements. Below we have listed the main points where AI can transform current sports performance analysis:  
* **Data Collection:** AI transforms traditional data collection methods into precise, large-scale processes, reducing manual bias. * **Advanced Data Analysis:** AI automates data analysis, identifying hidden patterns and trends within large datasets for performance enhancement and strategy planning. * **Enhanced Video Analysis:** AI-driven video analysis is faster, more precise, and more comprehensive than manual reviews, offering insights into player movements, team formations, and tactics. * **Notational Analysis:** AI streamlines notational analysis by automating the recognition and annotation of specific actions, improving both efficiency and accuracy. * **Time-Motion Analysis:** AI revolutionizes time-motion analysis by providing real-time insights into athlete energy expenditure and fatigue levels, leading to better player substitution strategies. * **Wearable Device Data Analysis:** AI analyzes real-time data from wearable devices, predicting potential health risks and suggesting personalized training programs. * **Injury Prevention:** AI in computer vision analyzes player biomechanics, identifies harmful patterns, and suggests technique modifications, enhancing player safety.
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 2 The Best Mix Ratio Design of Permeable Concrete Piles
### Experiment Method
#### 2.1.1 Experiment Material  
In composite foundations, pervious concrete piles are primarily used for drainage consolidation and secondarily for load bearing. Therefore, this experiment selects coarse aggregates with smaller particle sizes and designs higher porosity to achieve good permeability and strength. The aggregates are crushed stones with particle sizes ranging from 4.75 to 9.5 mm, supplied by Wuhan Zhongjian Ready-Mixed Concrete Co., Ltd. in Wuhan, China. The \"Standard for Quality and Testing Methods of Sand and Stone for Ordinary Concrete\" (JGJ 52-2006) [15] aggregate was tested to obtain its basic physical parameters.  
The results are presented in Table 1. The cement used is PO42.5 Portland cement with a 28-day compressive strength of 47 MPa and a density of 3145 kg/m\({}^{3}\), with a standard water-cement ratio of 26.45%. The water-reducing agent is a polycarboxylate-based admixture, incorporated at 0.9% by mass of cement, which significantly enhances the workability of the concrete. The mixing water is municipal tap water.
## 2 Basic Theoretical Parameters of the FE-SEA Method
### Modal Density
#### 2.1.1 Lateral Vibration Modal Density of a One-Dimensional Beam  
Modeling of one-dimensional elements is often encountered in mixed models. In the process of modeling, the transverse vibration of a one-dimensional beam is applied more often, and its free-state vibration equation is [1; 2]:  
\[\frac{\partial^{2}u(x,t)}{\partial t^{2}}+R^{2}C_{l}^{2}\frac{\partial^{4}u(x,t)}{\partial t^{4}}=0 \tag{1}\]  
where \(u(x,t)\) is the displacement of point \(x\), \(R\) is the radius of rotation at point \(x\), \(C_{l}\) is the longitudinal wave velocity, and:  
\[C_{l}=\sqrt{\frac{E}{\rho}} \tag{2}\]  
In the above formula, \(E\) is the elastic modulus of the material and \(\rho\) is the material density. In Equation (1), \(u(x,t)=\varphi(x)e^{i\omega t}\); thus, the equation can be rewritten as:  
\[\frac{\partial^{4}\varphi}{\partial x^{4}}-K_{B}^{4}\varphi=0 \tag{3}\]  
In the above formula, \(K_{B}\) is the number of bending waves, and:  
\[K_{B}=\sqrt{\frac{\omega}{RC_{l}}}=\frac{\omega}{\sqrt{\omega RC_{l}}}=\frac {\omega}{C_{B}} \tag{4}\]  
In the above equation, \(C_{B}=\sqrt{\omega RC_{l}}\) is the bending wave velocity; the modal density of the transverse vibrating beam is expressed as:  
\[n(\omega)=\frac{l}{2\pi C_{B}} \tag{5}\]  
\[n(f)=2\pi n(\omega)=\frac{l}{C_{B}} \tag{6}\]
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design and Performance Simulation of the Adaptive RC-LDPC Coding Scheme
#### 2.2.3 Reconstructed image quality simulation
When the channel environment is good, high code rate coding is used to improve the overall efficiency of the system. In addition, by comparing the data of scheme 2 and scheme 3, we can also see that since scheme 3 adopts UEP, the reconstructed image quality can be further improved compared with scheme 2, and the PSNR of the reconstructed image can obtain an average of about 0.5 dB. At an image compression ratio of 0.5, Figure 21 shows reconstructed images obtained by using the three different schemes under the conditions of the channel environment of Class 2. Image (a) is the original image, and image (b), image (c) and image (d) are reconstructed images using the three schemes, respectively. We can see that the image quality of image (b), image (c) and image (d) is sequentially improved. Moreover, image (d) and the original image (a) are almost identical.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
### Motivation  
In 2023, Douglas AutoTech Corp, Hopkinsville, KY, USA, a prominent automotive manufacturer, upgraded its entire production line to utilize IoT technology in a smart factory setup. This transformation was intended to boost efficiency and lower expenses by linking machinery, sensors, and production systems into a centralized IoT network. While the transition yielded substantial operational improvements, it also introduced several cybersecurity vulnerabilities. AutoTech Corp soon found that its IoT network had become a prime target for malicious actors using increasingly sophisticated tactics, prompting the need for strong cybersecurity measures to protect its operations.  
The Bildefender 2023 IoT Security Landscape Report underscores a significant event in 2023: a large-scale DDoS attack targeting a smart home network. The attack took advantage of weaknesses in different IoT devices, including security cameras, smart thermostats, and connected appliances. This attack interrupted the regular functioning of these devices, causing considerable inconvenience and posing a potential security threat to users. The attackers employed a botnet made up of compromised IoT devices to initiate the surge  
Figure 1: Operating principles of Intrusion Detection System.  
Figure 2: Elements of intrusion detection and prevention system.  
of malicious traffic. The report provides details on multiple attack vectors such as brute force, spoofing, and web-based attacks. These tactics are becoming increasingly common as the number of connected devices continues to rise. These incidents highlight the urgent requirement for strong security measures and ongoing monitoring in IoT environments to protect against emerging cyber threats.
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 4 Experimental Setup
### Evaluation Metric
#### 4.3.2 External Evaluation Metric  
To evaluate the classification performance using the selected feature sets, two widely used performance measures are used namely _Micro Average F1_ and _Macro Average F1_. The _Macro Average F1_ measure depends on _precision_ (\(P\)), _recall_ (\(R\)) and _F-measure_, which are calculated for each class as follows:  
\[P=a/(a+b)\]  
\[P=a/(a+b)\]  
\[F_{1}=\frac{2p\times r}{p+r}\]where \(a\) is the number of documents correctly classified; \(b\) is the number of documents incorrectly classified and \(c\) is the number of documents in the class. _Macro Average F1_ is calculated as below:  
\[F_{1}^{macro}=\frac{1}{m}\sum_{i=1}^{m}F_{1}(c_{i})\]  
where \(m\) is the number of classes and \(F1(c_{i})\) is the \(F1\)_measure_ for the \(i^{th}\) class.  
_Micro Average F1_ is calculated globally based on the global precision and recall. Calculations for precision and recall for micro averaging as given by [72] are shown below:  
\[\begin{array}{l}P^{\mu}=\frac{\sum_{i=1}^{m}a_{i}}{\sum_{i=1}^{m}(a_{i}+b_{ i})}\\ R^{\mu}=\frac{\sum_{i=1}^{m}a_{i}}{\sum_{i=1}^{m}(a_{i}+c_{i})}\end{array}\]  
where \(a\), \(b\), \(c\), \(m\) are the same variables used in the previous equations of _precision_ (\(P\)) and _recall_ (\(R\)), and \(\mu\) indicates Micro Averaging. _Micro Average F1_ is calculated as follows:  
\[F_{1}^{Micro}=\frac{2\times p^{\mu}\times R^{\mu}}{p^{\mu}\times R^{\mu}}\]  
Statistical Test  
The \(t\)-test is conducted as a statistical analysis to compare two groups of classification results. If the difference in the results is above a certain value, this indicates that the text-feature selection method is significantly efficient.