esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Software Platform and Simulation Tools
### Network Scenario  
Since the purpose of this paper is not merely to provide a method for simple and one hop networks, the basis of this research is based on a multi-hop random network in order to make the proposed method as practical as possible. In the multi-hop random scenario, nodes are exposed to different numbers of neighbors, so interference has a greater impact on nodes. Nodes also have different amounts of RSSI. On the other hand, the network packets are more likely to be lost. In addition, each node experiences a different number of retries in sending packets [39]. Therefore, in the random scenario, the power consumption in the nodes is not monotonous. Figure 1 shows a scenario and network graph based on RPL routing protocol such that 20 sender nodes and one sink node or border router that they are scattered in the environment dimensions 100 square meters (\(100\times 100\) m) randomly. In the current study, nodes are considered to be fix. The transmitter range at all nodes is equal to 50 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Type** & **ON0** & **RSSI0** & **OFF0** \\ \hline _SRXON_ & register & & & \\ _SRFOFF_ & register & & & ✓ \\ _RSSI_ & register & & ✓ & \\ SNOP & register & & ✓ & ✓ \\ _SFLUSHRX_ & register & & & ✓ \\ _RXFIFO_ & register & & & ✓ \\ _RSSI\_VALID_ & status bit & & * & \\ \(T_{X\_ACTIVE}\) & status bit & & & * \\ _CSN_ & pin & & * & \\ _FIFOP_ & pin & * & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants in one idle listening based on CC2420 driver source code in Contiki OS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Power Current Consumption State** & **Value** & **Unit** \\ \hline VCC & Supply voltage & 3 & volt \\ PC\_CPU & MCU on, Radio off & 1.8 & mW \\ PC\_LPM & MCU idle, Radio off & 0.0545 & mW \\ PC\_Tx & MCU on, Radio Tx & 17.7 & mW \\ PC\_Rx & MCU on, Radio Rx & 20 & mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power parameters in Tmote-Sky based on [38].
## 4 ML Applications for RIS-Assisted Communications
### Estimation of CSI
The authors in [75] developed a DL framework based on CNN architectures for this task. The first was the single-enhanced deep super-resolution neural network (ESDR), which obtained an accurate CSI for a single-ray scenario. The second architecture, called multi-scale deep super-resolution neural network (MSDR), was proposed to estimate CSI on multi-scale sparse low-resolution channel scenarios. Both proposed architectures used ReLU as the activation function. The output was an estimated channel matrix. The framework proposed in [76] is also a CNN-based architecture called fast and flexible denoising network (FFDNet). The dataset used as input comes from synthetic channel realizations that are separated into real and imaginary components. FFDNet uses noise variance information, 2D convolutional layers, and ReLU activations with the Adam optimization. The distributed ML (DML) framework was introduced in [77] to build a downlink CSI estimation. The CNN-based scheme improved the estimation accuracy by extracting different channel features from different scenarios. This model worked when the user moved from one channel scenario to another cell, and the system built a global model shared by the BS with all users that could be jointly trained based on local training sets available to all users. The model was generated using the outdoor tracing scenario from the DeepMIMO dataset. For this architecture, the model used 32 filters of 3x3 kernel, batch normalization, and ReLU activation in combination with max-pooling and the Adam optimization. [PERSON] et al. [78] consider that the rank-deficiency property of the cascaded channel matrix can be used to develop a deep unfolding method architecture to reduce the training overhead and estimate CSI. The design is an ANN-based architecture, which consists of linear layers whose inputs are synthetic channel realizations. The model uses the Adam optimization and ReLU activation functions for all layers except the last one. The combination of precoding for multiuser (MU) downlink and CSI was addressed in [79], where the authors proposed an RIS-reflective network architecture of linear layers with a sigmoid activation and Adam optimization. The proposed model could be implemented when the LOS between the BS and user devices was blocked. In that framework, the CSI was acquired at the BS using the DL network with low pilot inputs and feedback signaling overhead. In Table 3, we list the analyzed frameworks to compare them by defining the type of ML algorithm used, the network architecture, and the DL parameters reported according to Figure 13. We also list the type of data generated, following the approaches describedin Section 4.1 and finally, the last column of Table 3 defines whether the authors share the source code with the community to reproduce the results described by their framework. Most of the codes are provided by the collection at the GitHub repository uploaded by [PERSON] [81], where we can also see the programming language utilized.
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 4 Simulation
Nodes are circularly selected as the cluster head. This procedure not only balances the energy consumption of the network nodes, but effectively prolongs the survival time of the network as well. Figure 6 demonstrates the DC-KH and DC-PSO algorithms in the network total energy consumption with the change of the number of rounds. The graph shows that the total energy consumption of the DC-PSO algorithm is always higher than that of the DC-KH algorithm. The proposed DC-KH is based on DC-PSO using non-uniform clustering and a dynamic hierarchical mechanism, which collaboratively addresses the network \"hot spots\". In addition, the appropriate master cluster heads and vice-cluster heads are selected through the krill group optimization selection principle. The integration of the above three procedures makes the total energy consumption of the  
Figure 4: Energy consumption comparison chart of the vice cluster head. Figure 5: Comparison of the number of survival nodes. network DC-KH algorithm to be significantly less than that of the DC-PSO algorithm, and it prolongs the network running time of the algorithm. Figure 7 shows the network survival cycle in the DC-KH and DC-PSO algorithms with the increase of the number of nodes. Network lifetime is an important basis for measuring the effectiveness of an algorithm [15]. In this paper, network lifetime is defined as the number of rounds that can satisfy the network coverage rate \(\mathrm{C_{or}}\) (\(\mathrm{C_{th}}\leq\mathrm{C_{or}}\leq 100\%\)). \(\mathrm{C_{th}}\) is the coverage threshold. When the coverage rate is lower than the threshold value, the network can hardly complete the normal monitoring function and the end of the life cycle. The graph shows that the DC-KH algorithm is always higher than the DC-PSO algorithm in terms of network lifetime, because the dynamic hierarchical mechanism of DC-KH balances the cluster head energy consumption and prolongs the network lifetime.
## 3 Methodology
### Dataset and Preprocessing  
The NSL-KDD dataset retained the original dataset's characteristics, such as its advantageous and challenging structure. The new version of the dataset addressed some drawbacks inherited from the previous version, reduced the number of instances, and maintained the diversity of selected samples. The NSL-KDD dataset was compiled to maximize its difficulty of prediction. In order to classify the records according to their difficulty level, several benchmark classifiers were used [25]. The number of selected records for each difficulty level group was inversely proportional to records' percentage from the original dataset. The KDDTrain+, KDDTest+, and KDDTest-21 sets were used to classify the records in this study. The KDDTrain+ set comprises 125,973 instances composed of 67,343 instances  
Figure 1: The framework of the PCA–Ensemble model.  
of normal traffic and 58,630 instances of attack traffic. The KDDTest+ set contains over 22,000 instances, while the KDDTest-21 set includes 11,850 instances.  
The details of the datasets' instances are shown in Table 1. Each instance of the KDDTrain+ set has 42 attributes representing the different features of the connection. The values of these attributes are labeled as an attack or a normal.  
NSL-KDD consists of four main attack types: denial-of-service (DOS), Remote to user (R2L), User to Root attack (U2R), and Probing attack (PRB).  
The most critical step in data mining is preprocessing the raw data. This process involves extracting the necessary details from the data. Unfortunately, the data coming from heterogeneous platforms are often noisy, incomplete, and inconsistent. This is why it is essential to transform them into a format that can be used for knowledge discovery. The preprocessing step of this research involves analyzing and transforming the data. Due to the varying requirements of the platforms, the data may contain redundant and anomalous instances. This redundancy can affect the accuracy of classification. Therefore, all the records containing redundant values should be removed from the dataset at the start of the experiments to prevent data duplication.  
Regarding symbolic values, for instance, in the NSL-KDD datasets, the feature \"protocol type\" includes values such as TCP, UDP, and ICMP protocols. Therefore, the conversion process is considered vital in order to improve the accuracy of IDS. In this paper, we replaced each symbolic feature with integer values. In addition, due to the varying scales of features, the classification performance can be affected by size. For instance, features with large numerical values can overwhelm the model's performance compared to features with small numerical values. Accordingly, we took normalization into account in our experiment.
## 4 Problem Space for DL Applications
### Application Scenarios with DL
#### 4.3.5 Application Performance  
Table 9 highlights key case studies and best practices of DL applications in the IoMT, showcasing their transformative impact on healthcare. It covers innovative implementations in areas such as remote monitoring, personalized medicine, diagnostic imaging, and  
Figure 3: Workflow of DL models in IoMT for healthcare. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & **Description** & **Example** & **Impact** \\ \hline \multirow{3}{*}{Smart hospitals} & IoMT systems optimize & \multirow{3}{*}{Smart beds detecting falls and monitoring patient vitals.} & Increased operational efficiency \\  & workflows and patient safety & & \\  & using connected devices and analytics. & & \\ \hline \multirow{3}{*}{IoMT in rehabilitation} & Smart prosthetics and sensors & \multirow{3}{*}{AI-powered prosthetics adapting to movement patterns.} & Enhanced rehabilitation \\  & assist in rehabilitation and progress tracking for patients. & & \\ \hline \multirow{3}{*}{Supply chain and asset management} & IoMT tracks medical equipment & \multirow{3}{*}{Trackers monitoring the location of ventilators and other equipment.} & Improved resource allocation \\  & and supplies for better availability and maintenance. & & \\ \hline \multirow{3}{*}{Infection control} & IoMT devices monitor hygiene & \multirow{3}{*}{Connected devices tracking hand hygiene compliance in hospitals.} & Lower infection rates and \\  & and sterilization practices to reduce infection risks. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._hospital resource optimization. By examining real-world examples, this table underscores the potential of the IoMT and DL to improve healthcare delivery and enhance patient outcomes, as well as lead to efficient operations. Table 10 presents a comparative analysis of various DL models applied in different IoMT domains. The table highlights the applications of these models, along with a performance comparison between traditional technologies and DL-enhanced approaches. To ensure a straightforward and quantifiable comparison, we primarily focus on accuracy rate as the key performance metric. For instance, the researchers in [202] deployed a CNN-based ResNet-50 model to assist in detecting COVID-19 from CT scans. The model was trained using a combination of the COVID-CT dataset and proprietary hospital data, achieving 97% accuracy with a 55 ms inference time. To enhance robustness, data augmentation and transfer learning were applied, improving model generalization. This case study highlights the critical role of CNNs in rapid and accurate COVID-19 detection in IoMT applications [202].
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 2 Degradation Modeling and Prognostic Hybrid
### Long-Term Cumulative Degradation Assessment with Cdd-Hi  
#### 2.1.1 Cumulative Dynamic Difference Feature Extraction  
For the single mode system, static features are extracted from the raw sensor data to represent the degradation trend. For multi-modal systems, different combinations of switching order have different effects on the degradation. So for multi-modal systems, the whole degradation cannot be built only based on stationary features. To solve it, the proposed cumulative dynamic difference features consist of four parts, which are sthe mouth feature (\(F_{1}\)), cumulative modes (\(F_{2}\)), difference times (\(F_{3}\)) and difference data (\(F_{4}\)).  
In order to leave out the noise in the original signal \(S\), smoothing is used to obtain the smoothed feature \(F_{1}\). The size of \(F_{1}\) is the number of sensor \(K\). \(F_{2}\) represents the cumulative action times of each operation mode. The size of \(F_{2}\) equals the number of modes \(M\). To present the information of mode switching, \(F_{3}\) and \(F_{4}\) are constructed. \(F_{3}\) represents the forward differences of action times of each operation mode, each of which is gained by calculating the time interval between the current action time and the last action time of each mode. \(F_{4}\) represents the forward differences of the sensor data of each operation mode (\(d\)). The differences of sensor data between the current action time and the last action time of each mode are calculated to characterize data changes between different modes.  
The cumulative dynamic physical difference feature consists of the above four parts for the multi-modal system to extract the switching order of modes. Figure 2 illustrates an example of the cumulative dynamic difference feature extraction process for a sensor. The number of modes is \(m=6\); the length of data sequence is 40. The cumulative dynamic difference feature is the \(F_{t}\) = [\(F_{1}\), \(F_{2,1}\), \(F_{2,2}\), \(F_{2,3}\), \(F_{2,4}\), \(F_{2,5}\), \(F_{2,6}\), \(F_{3,1}\), \(F_{3,2}\), \(F_{3,3}\), \(F_{3,4}\), \(F_{3,5}\), \(F_{3,6}\), \(F_{4,1}\), \(F_{4,2}\), \(F_{4,3}\), \(F_{4,4}\), \(F_{4,5}\), \(F_{4,6}\)] for the system in \(t\)th action time.
# Piezoelectric Composite Vibrator with a Bilaminated Structure for Bending Vibration
## 1 Introduction
The low quality factor makes the piezoelectric composite material very suitable for making broadband narrow pulse transducer. (3) The dielectric constant of piezoelectric composites is much lower than that of ordinary PZT because of the low dielectric constant of polymers. As a result, the electrostatic capacitance of the piezoelectric composite material is smaller, and the input impedance of the transducer is higher when it works, so it has higher sensitivity of the receiving voltage. In addition, the low electrostatic capacitance makes the charging and discharging time shorter. Using piezoelectric composite materials, the transducer with larger first wave amplitude can be fabricated. Therefore, it has been widely used in these fields. (4) Piezoelectric constants \(\mathrm{d_{h}}\) and \(\mathrm{g_{h}}\) of high hydrostatic pressure. In underwater acoustic applications, the hydrostatic piezoelectric constant \(\mathrm{d_{h}}=\mathrm{d_{33}}+2\mathrm{d_{31}}\) of ordinary PZT. Because \(\mathrm{d_{33}}\) is approximately equal to \(-2\mathrm{d_{31}}\), \(\mathrm{d_{h}}\) is very small. In piezoelectric composites, \(\mathrm{d_{31}}\) is reduced by controlling the volume fraction of PZT phase, thus \(\mathrm{d_{h}}\) is increased. The dielectric constant of piezoelectric composites is very small, which makes \(\mathrm{g_{h}}=\mathrm{d_{h}}/\varepsilon\) much larger than that of ordinary PZT. Therefore, piezoelectric composite materials are ideal materials for hydrophones. (5) Smaller plane electromechanical coupling coefficient. In piezoelectric composites, PZT columns are coupled by polymers, and the plane electromechanical coupling coefficient of PZT columns is smaller than that of ordinary PZT columns. Therefore, the radial mode in piezoelectric composites is weaker than that in ordinary PZT, which makes the energy more concentrated on the thickness mode, which improves the time response of the pulse to a certain extent. (6) Flexible polymers, such as epoxy resins, are more flexible at high temperatures, which can make piezoelectric composites into some specific shapes to meet the special requirements of practical applications. In the past, in the fabrication of focusing transducers, acoustic lenses were added to the surface of PZT or piezoelectric single crystal to narrow or focus the sound beam at a certain point in space. Using piezoelectric composites, spherical or cylindrical concave surfaces with certain curvature can be pressed at high temperatures, so that sound waves can be directly focused at the center of curvature, generating point or line focused beams, thus avoiding the trouble of making sound lenses and calculating the location of focus. At the same time, it can reduce the loss caused by the acoustic lens and improve the sensitivity of the transducer. In the present work, piezoelectric composite materials were used to replace piezoelectric ceramics to fabricate a bilaminated vibrator, improve the vibration displacement, and reduce the resonance frequency.
# An IoT-Based System for Efficient Detection of Cotton Pest
## 1 Introduction  
Pests and related diseases are difficult challenges faced by farmers in developing countries. They can damage crops, reduce potential revenue, and have a negative effect on the crops' class. Farmers in developing countries, such as Pakistan, have been using legacy tricks to manage pests for various crops. The identification of the pest is crucial in order to approach pest control properly. The farmers use general pesticides; however, the use of pesticides without the identification of pests may lead to negative consequences, such as immunity development in pests. Furthermore, in some cases, pesticides kill numerous useful insects and natural opponents that are useful against the aggressive reproduction of harmful pests. Moreover, crops that heavily depend on insects for pollination, fail to bear fruits [1, 2].  
Cotton (also known as white gold) is a crop of green and juicy leaves with larger flowers. Over 1300 harmful insects have been recorded that infect cotton worldwide and around 125 species infect cotton in India according to a report [3]. According to a study [4], more than 125 dangerous insects affect cotton in the USA and Pakistan. Among them, more than 90 pests and stainers damage cotton crops every season [5]. According to an economicsurvey, cotton, wheat, rice, and sugarcane are major crops in Pakistan, which contribute to more than thirty percent of the overall agriculture sector, and seven percent of the country's gross domestic products [6]. Unfortunately, there has been no significant effort or investment towards using technologies for precision agriculture despite the importance of agriculture and specifically the cotton crop in South Asia. Accurate information about disease vectors and related pests is required to design an appropriate pest management strategy. In developed countries, the use of the Internet of Things (IoT) for precision agriculture has increased in the last few years. Less local effort and investment in using technologies for precision agriculture motivate the researchers to focus on an automatic pest and disease detection system that may assess the situation with a minimum delay.
# Simulation of the Marangoni Effect and Phase Change Using the Particle Finite Element Method
## 4 Conclusions  
A new approach for simulating industrial processes that involve localized melting of a solid, such as welding or additive manufacturing, is being developed based on the particle finite element method (PFEM). The use of PFEM is rooted in its robustness, which stems from the underlying FEM; at the same time, the particle character of this method allows simulation of large deformations, including complex fluid flow. Furthermore, PFEM is particularly suited for free surface deformation, as boundaries and interfaces are naturally captured. The free surface flows are not included in this work, but the first validation step for a thermo-fluid solver that can simulate such processes is shown and its suitability for some of the most important physics involved in these processes is demonstrated.  
The Marangoni effect and natural convection are the driving forces of flow in the melt pool of a welding or additive manufacturing process. Both effects are correctly captured by the presented method in 2D. Melting of metal with natural convection has been simulated here in order to prove the suitability of the methods utilized to model the phase transition in 2D. Minor discrepancies with results from the literature were found; however, the overall behavior observed in experiments has been well-captured. It can be concluded that the PFEM-based method is capable of simulating phase change.  
Further developments should aim to investigate the combined effects of free surface deformation, surface tension, natural convection and phase change. Extension to 2D axisymmetric cases and then 3D cases should follow thereafter. Once this point is reached, the thermo-fluid simulation of welding or additive manufacturing will be possible, and models of powders and elasto-plastic solids as well as metallurgical models could be added to complete the physics required at the mesoscale.  
Conceptualization, J.-P.P. and V.E.T.; methodology, R.B. and D.C.; software, R.B.; validation, B.-J.B. and R.B.; data curation, B.-J.B.; writing--original draft preparation, B.-J.B.; writing--review and editing, J.-P.P., V.E.T., D.C. and R.B.; visualization, B.-J.B.; supervision, J.-P.P. and V.E.T.; project ad-ministration, J.-P.P.; funding acquisition, J.-P.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FRIA-FNRS, grant number F 3/5/5-FRIA/FC-3425 for [PERSON] and by the WBI grant CMP Chill--Projet 5: annee 2019 from Wallonie-Bruxelles International and by the WBI-Bourse d'excellence 1 September 2019-31 August 2020 for [PERSON].  
The authors declare no conflict of interest.