esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work  
### Delay Tolerant Networking (DTN) Routing Protocols  
DTN routing protocols can be classified in relation to the type of information collected by nodes to make routing decisions. Finding the destination requires two different properties: replication and knowledge [9]. Replication uses multiple copies of each message to increase the chance of delivery probability and reduce delivery latency. Knowledge makes forwarding decisions based on information about the network. The DTN routing protocols are classified into two categories based on these properties [9; 10]:  
* Flooding routing protocols;
* Forwarding routing protocols.  
In the flooding protocols, each node spreads a number of copies of each message to other nodes without any information about the structure of the network. The nodes keep and store the copies of the messages in their buffer until they encounter the next node or the destination. Message replication helps to increase the probability of message delivery because more nodes are responsible for delivering the message to the base station. The flooding routing protocols include Epidemic Routing, Two-Hop Relaying, Tree-Based Flooding and Prioritized Epidemic Routing [10; 11; 12].  
Forwarding routing is a knowledge-based approach without replication. In this strategy, knowledge about the network is required by the routing protocol and the nodes collect information about other nodes in the network to select the best path to forward the message to the destination [9]. Location-Based Routing, Source Routing, Per-Hop Routing and Per-Contact Routing belong to the forwarding family [10; 12].  
The next paragraphs provide more details about popular existing DTN routing protocols, which are used in this study, specifically, Direct Delivery, Spray and Wait (SAW), Epidemic, and Probabilistic Routing Protocol using History of Encounters and Transitivity (PROPHET). The purpose of presenting these protocols is to use them as baselines for adding our proposed energy awareness, and to compare their performance with and without energy awareness.
# Survey of Blockchain-Based Applications for IoT
## 10 Decentralized AI and IoT  
### Privacy-Preserving Decentralized ML Model Training  
Privacy-preserving in AI and ML means implementing strategies and technologies that protect sensitive data while still enabling powerful analytics and model training. In the context of machine learning as a service (MLaaS), it involves employing privacy-enhancing techniques that ensure data confidentiality throughout the entire AI lifecycle. Techniques such as homomorphic encryption (HE), Secure Multi-Party Computation (MPC), differential privacy (DP), federated learning (FL), and Trusted Execution Environments (TEEs) are employed to protect data without compromising on the performance and efficiency of AI models. The goal of privacy-preserving machine learning (PPML) is to build secure and effective systems where the data remain confidential, even while being processed by untrusted servers or third-party service providers [72].  
Another blockchain application in the IoT is PPML, particularly in smart cities, where sensitive data from IoT devices must be processed securely. A recent framework, SecureSVM, demonstrates this application by enabling support vector machine (SVM) training on encrypted IoT data stored in a blockchain network [73]. SecureSVM leverages a blockchain's decentralized structure to store encrypted IoT data from multiple providers. Homomorphic encryption allows SVM classifiers to be trained directly on the encrypted data. This approach addresses critical challenges in IoT applications, including data privacy, ownership, and integrity, by securely enabling polynomial operations and comparisons that are necessary for SVM algorithms.  
Experiments conducted with SecureSVM demonstrated that SVM classifiers can be effectively trained while preserving data privacy and security, making it a scalable solution for privacy-focused applications in IoT networks. This framework highlights blockchains' potential to support secure, decentralized machine learning solutions that meet IoT systems' unique privacy requirements and computational constraints within smart cities. Figure 15 below shows a block diagram for this system, with the blockchain connecting providers to a data analyst for model training and for classification.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 3 Classic Visual Saliency Models  
The classic visual saliency model considered the psychological and neurobiological basis, and most of them were handcrafted feature models. As a research basis of psychology, classic visual saliency models could be usually divided into two models according to the level of information processing: bottom-up saliency models (data-driven, task-agnostic model), and top-down saliency models (task-driven, task-specific model).
## 4 Results Achieved Following Our Pipeline
### Description of the Resulting Detection Models  
After replicating the results described in the original FastAudio study, we took advantage of their training tool to use it with our data as described below. For our tests, we used FastAudio to train models using the configuration provided in its official repository ([[https://github.com/magnumresearchgroup/Fastaudio](https://github.com/magnumresearchgroup/Fastaudio)]([https://github.com/magnumresearchgroup/Fastaudio](https://github.com/magnumresearchgroup/Fastaudio)) (accessed on 23 October 2024), without modifying the model's hyperparameters or architecture. The only changes made were to the training and testing datasets.).  
Implemented Models  
We developed four specific models, each trained exclusively with the data of the person of interest. This make it possible to fine-tune the model's detection ability to adapt to each person's vocal characteristics.  
Additionally, we created a general model that integrates the real and fake audios of the four aforementioned personalities and other fake audios collected from social networks. This model seeks to provide the possibility of classifying voices different from those considered as persons of interest.  
The development of these models follows the following pipeline:  
* Audio Labeling: Each audio sample is labeled as \"authentic\" or \"fake\". This process is crucial to ensure the quality of training and evaluation of the model, since the model will need to know whether the audio is real or fake at the time of training.
* Pre-processing: Audios are processed to normalize features such as volume and to extract relevant features using FastAudio. This step is critical to transforming data into a form that models can process effectively.
* Model training: The model is trained with 80% of the data we have, labeled and preprocessed.
* Testing: the models are tested with the remaining 20% of the data to evaluate their accuracy and generalization ability.
* Deployment: once validated, the models are deployed on a server, where we can access them through an API to perform verifications sending an audio from the web tool.  
Figure 3 shows a diagram of this pipeline.  
Figure 3: Pipeline of the model creation process.
## 3 Experiments
### Ablation Experiment  
To verify the effectiveness of the Capsule-Transformer method, based on the DEAP dataset, this paper compares the performance of this method with the Transformer and the original CapsNet for emotion recognition. In Transformer, the final emotion classification is based on class token, and cross-entropy is used as the loss function. In the original CapsNet, margin loss is used as the loss function, and the EEG signal features are directly used as the primary capsules after dimensional alignment, and then the emotion capsules are generated through the dynamic routing mechanism for emotion state classification. The results are shown in Table 4. The performance of the Capsule-Transformer method for emotion recognition in all three dimensions is significantly improved compared to the Transformer and capsule network.
## 4 Results and Experiments
### Results  
The evaluation of the proposed models was conducted in two key phases, training and testing, using the train and test subsets of the CICIDS2017 dataset, with additional evaluation on other datasets like NF-BoT-IoT-v2 to demonstrate the models' generalizability. The experiments focused on both binary and multi-class classification tasks, aimed at effectively detecting attacks and accurately identifying various attack types. The performance of the proposed models was benchmarked against existing intrusion detection systems as reported in the literature, providing a contextual understanding of their strengths and limitations. The results on the CICIDS2017 and NF-BoT-IoT-v2 datasets highlight the effectiveness of the models in both binary and multi-class classification tasks. The Transformer-DNN and Autoencoder-CNN models consistently demonstrated superior performance, excelling in accurately detecting attacks and correctly identifying various attack types. Other models, such as CNN, Autoencoder and DNN, also performed well, but the Transformer-DNN and the Autoencoder-CNN model stood out as the most robust across all evaluation metrics. These findings underscore the impact of the implemented preprocessing techniques and the strength of the proposed models in handling the classification challenges presented by the datasets.  
(i) Binary Classification  
The performance metrics for binary classification across different models on the CICIDS2017 and NF-BoT-IoT-v2 datasets are presented in Table 30. On the CICIDS2017 dataset, the CNN model achieved accuracy, precision, recall, and F-score of 99.83% across all metrics, while the Autoencoder model recorded slightly lower values, at 99.73%. The DNN model performed better, achieving 99.88% for all metrics. The Autoencoder-CNN model demonstrated notable improvements, achieving accuracy, precision, recall, and F-score of 99.90%. The Transformer-DNN model further outperformed the other models on this dataset, with all metrics reaching 99.92%. With the NF-BoT-IoT-v2 dataset, the CNN model performed exceptionally well, with accuracy, precision, recall, and F-score values of 99.97%. The Autoencoder model followed closely with 99.94% across all metrics, while the DNN model achieved slightly higher results at 99.96%. The Autoencoder-CNN model delivered superior performance, achieving 99.98% for all metrics, matched only by the Transformer-DNN model, which also achieved 99.98% for accuracy, precision, recall, and F-score. These results emphasize the effectiveness and reliability of our Autoencoder-CNN and Transformer-DNN models in achieving consistently high metrics across both datasets for binary classification.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure  
### Spatial Deformation of Retaining Structure  
Based on the foundation pit measurements, it was observed that the retaining structure exhibited distinct spatial deformation characteristics. The structure as a whole underwent lateral bending due to the active soil pressure. However, the deformation at the corners and fulcrum positions of the foundation pit was limited due to the constraints, resulting in significantly smaller deformation of the wall compared to the middle section of the foundation pit. It is evident that the spatial deformation characteristics of the retaining structure are mainly attributable to its lateral bending resistance. Hence, the collective lateral bending resistance of the retaining wall is referred to as the spatial deformation effect of the retaining structure.  
The pile-anchorretaining structure consists of a crown beam, a waist beam, piles, and anchor cables. The crown beam is a reinforced concrete continuous beam arranged on top of the retaining structure surrounding the foundation pit. Its purpose is to connect all of the piles together (such as bored piles and rotary excavation piles) to prevent the top edge of the foundation pit from collapsing. The waist beam is a reinforced concrete or steel beam located below the top of the retaining structure. Its function is to transmit the bearing capacity of the support and anchor cable, which changes the support effect from a point to a straight line, thereby improving the stability and integrity of the retaining structure. Therefore, the spatial deformation of the pile-anchorretaining structure is formed by the combined action of the crown beam, waist beam, piles, and anchor cables, as shown in Figure 1.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 3 Case Study
### Economic and Environmental Assessment  
Under the economic parameters outlined in Table 2, the required initial investment and the annual operation and maintenance costs are summarized accordingly. For the grid-connected system, the costs related to grid installation and synchronization with the national grid's frequency are not applicable, as the site currently operates as a fully grid-dependent system. The grid-connected configurations, referred to as GC1 and GC2, exhibit a stable annual cash flow, determined by the net difference between energy sales to the grid and purchases from it.  
The annual cash flows for GC1 and GC2 are \(-\)2956.3 and \(-\)123,2 \(\upxi\), respectively. These values are contingent upon the assigned grid tariffs, which were simplified for this case study. It is important to note that the import of grid energy entails the consideration of taxes associated with carbon dioxide emissions. The CO\({}_{2}\) costs for GC1 and GC2 are 1012.6 and 367.2 \(\upxi\), respectively. The final net present value of each microgrid solution is presented in Table 2, alongside the levelized cost of energy (LCOE). The grid-connected solution of wind and pumped hydro storage presents the lowest net present value (NPV) and required power installed, 125 kW, wherein 105 kW corresponds to energy generation sources, such as wind and hydropower.  
Concerning the selected site for the examination of a small energy community, the grid-connected solution, designated as GC2, remains a viable option and the most economical. Since the site is not located on an island or in an extremely remote area and it benefits from an existing electric grid infrastructure, this solution is particularly well suited to the configuration of a small energy system. In scenarios where integration with the general grid is not possible, alternative stand-alone configurations must be considered. If that restriction was imposed, then the first stand-alone solution (SA1) would be the most appropriate. The stand-alone solution with the battery auxiliary (SA2) possesses the highest NPV, whilst the integration of a BESS does not have a significant impact on the performance and flexibility of the system. It still requires a superior overall installed power, increasing initial investment and O&M costs, as can be seen in Table 2. The integration of pumped hydropower storage in this study proved advantageous in reducing grid dependency and the required installed power of primary energy sources, contributing to an energy storage capacity of 217 MWh. This storage potential can ensure hourly maximum loads for two days.
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
## 3 The Design of Predictive Controller  
To achieve the speed tracking control, a predictive speed controller is designed in this section. Firstly, define the cost function as follows [15].  
\[J=\frac{1}{2}\int_{0}^{T_{r}}\left(\hat{y}\left(t+\tau\right)-y_{r}\left(t+\tau \right)\right)^{T}\left(\hat{y}\left(t+\tau\right)-y_{r}\left(t+\tau\right) \right)d\tau \tag{3}\]  
where \(T_{r}\) is the predictive horizon, and \(\hat{y}\left(t+\tau\right)\), \(y_{r}\left(t+\tau\right)\) are respectively represent the predictive output and reference output.  
The input relative degree \(\rho\) of the output \(y\) is defined as the order of its derivative that reveals the input \(u\). According to the nominal system (2), the input relative degree of the output \(y\) is equal to 2. The control order \(r\) is chosen to zero. Repeated differentiation up to \(\rho+r\) times of the output \(\hat{y}\left(t\right)\) with respect to time  
\[\left\{\begin{array}{l}\hat{y}\left(t\right)=L_{f}^{0}\omega\\ \dot{y}\left(t\right)=L_{f}^{1}\omega\\ \ddot{y}\left(t\right)=L_{f}^{2}\omega+L_{g_{1}}L_{f}\omega u\end{array}\right. \tag{4}\]  
Figure 1: Control system diagram of the PMSM drive systems.  
where \(\omega_{r}\) is the reference speed. \(\mathbf{K}\in\mathbf{R}^{1\times 2}\) is the first row of matrix \(\bar{\mathbf{\Gamma}}_{rr}^{-1}\bar{\mathbf{\Gamma}}_{pr}^{T}\), and  
\[\bar{\mathbf{\Gamma}}\left(T_{r}\right)=\left[\begin{array}{cc}\bar{\mathbf{\Gamma}}_ {\rho\rho}&\bar{\mathbf{\Gamma}}_{\rho r}\\ \bar{\mathbf{\Gamma}}_{pr}^{T}&\bar{\mathbf{\Gamma}}_{rr}\end{array}\right] \tag{14}\]  
where \(\bar{\mathbf{\Gamma}}_{\rho\rho}\in\mathbf{R}^{2\times 2}\), \(\bar{\mathbf{\Gamma}}_{pr}\in\mathbf{R}^{2\times 1}\), \(\bar{\mathbf{\Gamma}}_{rr}\in R^{1\times 1}\). Then, \(\mathbf{K}\) can be get as  
\[\mathbf{K}=\left[\begin{array}{cc}\frac{10}{3T_{r}^{2}}&\frac{5}{2T_{r}}\end{array}\right] \tag{15}\]  
As can be seen in Equation (11), the load torque is included in the predictive control law. When the drive system exists in the load disturbance, it is obvious that the anti-interference performance of the GPC controller will be affected. To solve this problem, a novel extended-state observer is introduced in next section.
## 3 Experimental Results
### Maximum Speed of Locomotion  
Figure 9 depicts the speed of locomotion as a function of the frequency of oscillations \(f\) and angular limit of underactuation \(\delta\). As illustrated in Figures 8 and 9c, for the maximum angles of underactuation \(\delta\) equal to \(2.1^{\circ}\), \(4.2^{\circ}\), \(6.3^{\circ}\), \(8.4^{\circ}\), and \(10.5^{\circ}\) (corresponding to using one, two, three, four, and five washers, each 0.4 mm thick), the maximum speed is higher than that of the fully actuated joint (i.e., \(\delta=0\)). This observation confirms the hypothesis that the limited underactuation might have a favorable effect on the speed of the robot. We assume that the achieved reduction in the hydrodynamic drag during the braking phase  
Figure 8: Fishbot and its control system. The abbreviations AL, PL, CPG, CS, and PWM denote the abstract layer, physical layer, central pattern generator, current sensor, and pulse-width modulation, respectively.  
of undulation (Figure 4c) overcompensates for the expected reduction in effectiveness of the thrusting phase of undulation (Figure 4a). Moreover, we can notice that the higher frequency of oscillation results in a higher maximum speed regardless of the maximum angle of limited underactuation (Figure 9b). Compared to the fully actuated joint, the maximum speedup of limited underactuation is achieved for a relatively low frequency of oscillation--around 0.8 Hz--as illustrated in Figure 9c. The maximum speedup of 1.4 times is achieved for the maximum angle of limited underactuation \(\delta=10.5^{\circ}\).  
Figure 9: Maximum speed of the fishbot during the trial as a function of the frequency of oscillations \(f\) (**a**) and maximum angle of the limited underactuation \(\delta\) (**b**), and in relation to the speed of the robot featuring a fully actuated (\(\delta=0^{\circ}\)) joint of the tail fin (**c**).  
Also, as Figure 9c illustrates, the fully underactuated joint (\(\delta=180^{\circ}\)) is somehow superior to the fully actuated one (\(\delta=0^{\circ}\)) for lower frequencies of oscillations (up to 1.2 Hz). However, as the frequency increases, the speed of the robot featuring a fully underactuated joint deteriorates rapidly and becomes inferior to both fully underactuated and limited underactuated robots.  
As the frequency of oscillation increases, the limited underactuation also has a favorable effect on the maximum speed of the robot. As depicted in Figure 9c, however, this effect is not as profound as the one observed at the lower frequencies of oscillation.
## 3 Results and Discussion  
### Microstructural Characterization  
Figure 1 shows SEM micrographs of pure Mg and the nanocomposites with a relatively uniform distribution of the Si\({}_{3}\)N\({}_{4}\) nanoparticles (indicated with white arrows) across the Mg matrix. The distribution is more homogeneous in the 0.5 vol%, and 1 vol% Si\({}_{3}\)N\({}_{4}\) particle-reinforced nanocomposites. The nanocomposites with 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles showed near uniform distribution with some clustered nanoparticles (indicated with white circles). The observed uniform distribution is mostly due to the excellent ultra-sonication treatment used during nanocomposite fabrication. The ultrasonic vibrations in the melt dispersed the Si3N4 nanoparticles substantially. Furthermore, the appropriate stirring parameters and nanoparticle preheating increased the wettability between the Mg and nanoparticles. The EDX spectrum results of the Mg-1.5 Si\({}_{3}\)N\({}_{4}\) nanocomposite (Figure 2) show the elemental composition of Mg, Si and N elements.  
Figure 1: SEM images depicting the dispersion of nanoparticles in the (**a**) pure Mg, (**b**) Mg-0.5 Si\({}_{3}\)N\({}_{4}\), (**c**) Mg-1.0 Si\({}_{3}\)N\({}_{4}\), (**d**) Mg-1.5 Si\({}_{3}\)N\({}_{4}\).  
Figure 3 shows the XRD analysis results of the samples. The primary peaks corresponding to pure Mg were observed in all the samples. However, the peaks corresponding to Si\({}_{3}\)N\({}_{4}\) were less intense in the XRD spectrum, due to a relatively low volume fraction (<2 vol%) of the particles in the matrix [26]. Nonetheless, the minor peak at 26 of 70.7\({}^{\circ}\) corresponds to a plane at (2 0 2) in the nanocomposites, confirming the existence of Si\({}_{3}\)N\({}_{4}\). Furthermore, there were no apparent peak shifts, suggesting that interfacial interaction was low throughout the casting process. The lack of reaction products and textural randomization caused by the inclusion of Si\({}_{3}\)N\({}_{4}\) nanoparticles aid in the plastic deformation of the nanocomposites.