esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When facing multi-objective optimization problems, the WOA is susceptible to premature convergence. Therefore, it is essential to enhance its performance by optimizing its search strategy. A lot of research has been conducted to improve the WOA. [PERSON] et al. [35] combined a variety of chaotic maps to adjust the parameters to improve the performance of WOA. [PERSON] et al. [36] introduced a collision body optimization algorithm in WOA, dividing the population into exploratory and mimicry groups to enhance global optimality search capability. [PERSON] et al. [37] combined the Gray Wolf Algorithm to increase the number of best candidate solutions to improve WOA. However, there are still some problems in how to avoid local optimization, enhance convergence speed, and improve learning efficiency. The good point set method [38] was introduced as an alternative to the random initial population method to increase the variability within the initial population of whales. A cosine-based nonlinear convergence factor strategy [39] was used to replace the original model of linearly decreasing convergence factor, which balanced the global and local optimization performance. Levy flight strategy [40] and Quasi-oppositional learning strategy [41] were used to avoid premature convergence. The major contributions of this study are as follows:  
1. Three improvement strategies are proposed for the WOA to enhance accuracy and efficiency. The original population initialization method is replaced, the convergence factor is adjusted, and the reverse learning strategy is introduced. These improvement strategies enhance the global search performance and local development performance of WOA from different perspectives. 2. Numerical experiments are conducted to demonstrate the accuracy and effectiveness of the improved whale optimization algorithm (IWOA). 3. SVR is employed to establish a nonlinear mapping relationship between hydraulic head at seepage monitoring points and hydraulic conductivity. By combining IWOA with SVR, a new model for inverting hydraulic conductivity is proposed. 4. The inversion model is applied to the inversion analysis of Dono Dam in Sichuan Province, China. The three-dimensional seepage field and hydraulic head in the dam site area are calculated to verify the accuracy of the parameters obtained from the inversion. The rest of the paper is organized as follows. In Section 2, we introduced the principle and improvement strategy of the whale optimization algorithm and verified the algorithm's performance with classical test functions. In Section 3, a seepage parameter inversion model combining IWOA and SVR was proposed. In Section 4, the proposed method was applied to an engineering case to invert the target hydraulic conductivity, and simulation results were provided. Section 5 discussed the applicability and limitations of the research results. Finally, conclusions were drawn in Section 6.
## 5 Experiments
### Discussion  
Our approach, though simple, successfully fulfills the four requirements outlined in the Introduction section:  
* Firstly, the requirement concerning both \"class-incremental learning\" and \"class-decremental learning\" is met with our strategy. By exclusively utilizing pre-trained models without fine-tuning them on our specific dataset, our approach naturally excels at class-incremental/-decremental learning. In fact, we do not train the neural networks ourselves but harness the complexity of existing models by leveraging their pre-trained capabilities. For example, let us assume that 10 additional objects are added to our dataset, bringing the total to 79 objects. In this scenario, our approach allows us to perform multi-class image classification without the need for fine-tuning the model.
* Secondly, the requirement for \"one-shot learning\" is addressed effectively. Within our dataset, each class (or sprite) is represented by only one sprite image. Despite this limitation, experimental results have shown that our method performs admirably even with sparse data.
* Thirdly, our approach succeeds on unnatural datasets where the semantic relationships among labels are either non-existent or difficult to utilize. The random generation of our dataset precludes the use of semantic label relationships, yet our method still yields strong performance.
* Lastly, the need for compatibility with low-performance computers is also met. We demonstrate that our approach can be efficiently run on a standard laptop computer. Moreover, the use of lightweight models like ResNet-18 and YOLOS-Ti ensures that we can execute our tasks with both effectiveness and efficiency.  
However, we also found one main limitation of our approaches, which we will address in future work: As shown in Figure 11, the unsupervised object detection model (CutLER) is very effective on transformed images (e.g., rotated images). However, even complex Vision Transformer-based pre-trained embedding models do not handle transformed images effectively. To address this problem with image transformations, we either need to embed more sprite images with transformations or train a more robust pre-trained model capable of handling image transformations.  
Figure 10: Accuracy comparison using the ten data samples, with and without object cropping.
## 2 Simplified Vulnerability Assessment Methods for Masonry and RC Building Structures
### Methods Belonging to Group A
#### 2.1.4 DM 58/17 Method  
The guidelines DM 58/17 [17] for the classification of seismic risk have been introduced in Italy following the Centro-Italia 2016-2017 seismic events in order to provide operational tools aimed to spread a more organic preventive culture and identify a simplified method for assessing the seismic vulnerability of masonry buildings. The method can be used for a quick evaluation of the seismic vulnerability of masonry buildings and is based on the same typological classification provided by EMS-98 [22], which, as said above, identifies seven and six types of masonry and RC buildings (see Figure 2), respectively. Each type is associated to one of the six vulnerability classes variable from A to F according to the EMS-98. Such classes are indicated as V\({}_{\text{i}}\) in the Italian guidelines, where \(i=1,2\),..., \(6\) corresponds to classes F, E,..., A, according to a decreasing vulnerability level.  
Furthermore, fluctuations are expected around the identified vulnerability class. As reported in Figure 2, the EMS-98 identifies, for each type and each vulnerability class, the most credible value (circle) and the dispersion around this value, expressed with the most probable values (solid lines) and less probable or even exceptional (dashed lines). The Italian guidelines adopts the same approach for assessing any class variance, but provides a change of class only in the case of a vulnerability increase. Table 4 reports the parameters analyzed in order to assess the minimum vulnerability class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Type of Masonry Building**} \\ \cline{2-6}  & \multicolumn{2}{c}{**V\({}_{\text{5}}\) to V\({}_{\text{6}}\)**} & \multicolumn{2}{c}{**V\({}_{\text{4}}\) to V\({}_{\text{5}}\)**} & \multicolumn{2}{c}{**V\({}_{\text{3}}\) to V\({}_{\text{4}}\)**} \\ \hline Poor quality of & simple stone & \
\begin{tabular}{c} URM with \\ manufactured \\ stone units \\ \end{tabular} & massive stone & URM with RC &
\begin{tabular}{c} reinforced or \\ floors \\ \end{tabular} \\ \hline High level of & & & & & \\ damage and/or & ✓ & ✓ & ✓ & ✓ & ✓ \\ degrade & & & & & \\ \hline Large scale & & & & & \\ openings & & & & & \\ interspersed by & ✓ & ✓ & ✓ & ✓ & ✓ \\ small-sized & & & & & \\ piers & & & & & \\ \hline Presence of & & & & & \\ numerous & & & & & \\ hollows & ✓ & ✓ & ✓ & ✓ & ✓ \\ reducing the & & & & & \\ resistant area & & & & & \\ \hline Non-counteracted & & & & & \\ horizontal & ✓ & ✓ & ✓ & & \\ thrusts & & & & & \\ \hline Poor walls & ✓ & ✓ & ✓ & ✓ & ✓ \\ connections & ✓ & ✓ & ✓ & ✓ \\ \hline Poor wall-slab & ✓ & ✓ & ✓ & ✓ & ✓ \\ connections & & & & & \\ \hline Large scale & & & & & \\ non-breaced & ✓ & ✓ & ✓ & ✓ & ✓ \\ walls & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for vulnerability class worsening provided by Italian guidelines DM 58/17 [17].
## 3 The Methodology
### Simulation Study
#### 4.1.4 Impact of Packet Delivery Ratio  
It is the fraction of a source node's packets that reach its associated CH node. The PDR, or packet delivery rate, is calculated as  
\[PDR=\frac{N_{S}}{N_{R}} \tag{23}\]  
Figure 8: Simulation rounds vs. energy consumption.  
Figure 7: Number of nodes vs. energy consumption.  
The percentage of packets effectively delivered to the target node is known as the delivery success ratio. When the data are sent via the best possible route, this proportion increases. Figure 10 shows a comparison between the success rates of the suggested method and those of the existing studies. According to the results, the suggested work has a better delivery success percentage than the existing literature. The proportion of successful deliveries drops as the amount of nodes grows. This is because of the sheer volume of data packets being sent to the network when a big number of nodes is present. As the number of nodes increases, the percentage of successful deliveries decreases across all the works. While the delivery ratio drops by 40% as the number of nodes grows in EIR-CIoT, it drops by only 9% in the proposed work. This study demonstrates that the suggested approach may be scaled to a high number of sites without experiencing any loss of data.
# Experimental and Numerical Study of Thermal Performance of an Innovative Waste Heat Recovery System
## 3 Results
### The Efficiency of the Equipment  
The efficiency of the heat pipe heat exchanger can be evaluated as its thermal effectiveness which is the ratio between the heat transfer rate, Q, and the maximum heat transfer rate of the equipment, Q\({}_{\text{max}}\). The equation can be expressed as:  
\[\epsilon=\frac{\text{Q}}{\text{Q}_{\text{max}}} \tag{1}\]  
\[\text{Q}=\text{C}_{\text{Ev}}(\text{T}_{\text{Ev, 1}}-\text{T}_{\text{Ev, 2}})=\text{C}_{\text{Co}}(\text{T}_{\text{Co, 2}}-\text{T}_{\text{Co, 1}}) \tag{2}\]  
By using the Equation (2) we can determine the heat transfer rate for the heat recovery system. C\({}_{\text{Ev}}\) and C\({}_{\text{Co}}\) represent the heat capacity rate for the evaporator and the condenser section and can be determined as the product of the mass flow rate and the specific heat capacity for the referred section.  
\[\text{C}_{\text{Ev}}=\text{\dot{m}}_{\text{Ev}}\times\text{c}_{\text{p,Ev}} \tag{3}\]  
\[\text{Q}_{\text{max}}=\text{C}_{\text{min}}(\text{T}_{\text{Ev, 1}}-\text{T}_{\text{Co, 1}}) \tag{4}\]  
\[\text{Q}_{\text{max}}=\text{\dot{m}}_{\text{Co}}\times\text{C}_{\text{p,Co}}( \text{T}_{\text{Ev, 1}}-\text{T}_{\text{Co, 1}}) \tag{5}\]  
Figure 9: 2D section plane.  
For the expression of Q\({}_{\text{max}}\) we have used C\({}_{\text{min}}\) instead of C\({}_{\text{max}}\) because Q\({}_{\text{max}}\) represents the maximum heat transfer rate for the heat exchanger but for this case, we have a limiting factor represented by the minimum heat capacity rate between the two fluids.  
By applying the Equations (2) and (5), the Equation (1) can be written as follows:  
\[\varepsilon=\frac{\text{Q}}{\text{Q}_{\text{max}}}=\frac{\dot{\text{m}}_{\text {Ev}}\times\text{C}_{\text{p,Ev}}\times(\text{T}_{\text{Ev,1}}-\text{T}_{ \text{Ev,2}})}{\dot{\text{m}}_{\text{Co}}\times\text{C}_{\text{p,Co}}\times( \text{T}_{\text{Ev,1}}-\text{T}_{\text{Co,1}})} \tag{6}\]  
The highest efficiency of the heat recovery system was in test no. 7, when the temperature of the secondary agent after 1 h was 56.1 \({}^{\circ}\)C compared to 70 \({}^{\circ}\)C, the temperature of the primary agent, which means an efficiency of 76.7% (Figure 10).
## 3 RPL Overview
### DODAG Construction  
RPL builds and maintains network structures called Destination Oriented Directed Acyclic Graphs (DODAGs). A DODAG is defined as a graph composed of nodes and links that constitute paths that point towards and terminate at a special node called the root. The DODAG root represents a node that generally performs the tasks of a sink or a gateway. RPL has been designed and optimized for the transmission of data from sensor nodes towards the root node. In theory, cycles do not exist in a DODAG, which inherently provides protection against the problem of loops. However, researchers have shown that there may exist transitory loops in RPL DODAGs [23,24].  
For the construction and maintenance of a DODAG, RPL nodes locally multicast DODAG Information Object (DIO) messages pseudo-periodically, by using the Trickle algorithm [25] (see Section 3.3). A DIO message contains information that allows a node to discover an existing DODAG, learn node and network configuration parameters, and maintain the network topology.  
In order to join a DODAG, a node must receive at least one DIO message from a neighbor. To this end, the node may either wait to receive DIO messages from its neighbors or may multicast a DODAG Information Solicitation (DIS) message to request the immediate transmission of DIO messages from neighboring nodes. When a DODAG node receives a DIS message, it schedules itself to send out quickly a DIO message. Once a node has sent a DIS message, it may send further DIS messages until the node receives a DIO message in response. If no DIO message is received after a given period of time, the node may decide to become the root of a new DODAG.  
Each node in a DODAG has a rank, which abstracts the topological distance between a node and the DODAG root. Each DODAG node indicates its rank in the DIO messages it transmits. A node calculates its rank by applying the Objective Function (OF) in use in the DODAG, and using the rank of its neighbors as an input. The OF defines how nodes use one or more metrics and constraints in order to determine their own rank. Any RPL implementation must support OF Zero (OF0), a metric-agnostic OF that allows interoperability between implementations in a wide spectrum of use cases [26].  
Based on the DIO messages received, each DODAG node chooses the members of its DODAG parent set, which is a subset of the DODAG node neighbors that provide a path towards the root. All members of the parent set of a node must have a lower rank than the node. For example, in Figure 1, the parent sets of nodes \(e\) and \(f\) are {_b,c,d_} and {_c,d_}, respectively.
## 2 IMC with Synchronized Decoupled Controller
### Integration of PLL into Clarke and Park Transformation  
In this EV fast charging system, the PLL is considered as a critical and important part, where it is designed to detect the phase angle (\(\theta\)) of the utility voltages (\(\mathbf{v_{S_{abc}}}\)). This phase angle is then used for all \(dq\)-transformations in the charging system, as shown in Figure 4. The process of IMC is primarily executed in the synchronous reference frame (\(dq0\)) and alpha-beta transformation (\(\alpha\beta 0\)), using Clarke and Park transformation matrices using Equations (1) to (4). These transformation matrices are applied to the model charging system as shown in Equations (5) to (6):  
\[\left[\begin{array}{c}\alpha\\ \beta\end{array}\right]=\left[\begin{array}{ccc}\frac{2}{3}&-\frac{1}{3}&- \frac{1}{3}\\ 0&\frac{1}{\sqrt{3}}&-\frac{1}{\sqrt{3}}\end{array}\right]\left[\begin{array}[ ]{c}a\\ b\\ c\end{array}\right] \tag{1}\]  
\[\left[\begin{array}{c}a\\ b\\ c\end{array}\right]=\left[\begin{array}{ccc}1&0\\ -\ \frac{1}{2}&\frac{\sqrt{3}}{2}\\ -\frac{1}{2}&-\frac{\sqrt{3}}{2}\end{array}\right]\left[\begin{array}{c} \alpha\\ \beta\end{array}\right] \tag{2}\]  
\[\left[\begin{array}{c}d\\ q\end{array}\right]=\left[\begin{array}{ccc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}\alpha\\ \beta\end{array}\right] \tag{3}\]  
\[\left[\begin{array}{c}\alpha\\ \beta\end{array}\right]=\left[\begin{array}{ccc}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}d\\ q\end{array}\right] \tag{4}\]  
\[\left[\begin{array}{c}\mathbf{v_{S\alpha}}\\ \mathbf{v_{S\beta}}\\ \mathbf{v_{0}}\end{array}\right]=\sqrt{\frac{2}{3}}\left[\begin{array}{ccc}1& \frac{-1}{2}&\frac{-1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\\ \frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\end{array}\right] \left[\begin{array}{c}\mathbf{v_{Sa}}\\ \mathbf{v_{Sb}}\\ \mathbf{v_{Sc}}\end{array}\right] \tag{5}\]  
\[\left[\begin{array}{c}\mathbf{v_{d}}\\ \mathbf{v_{q}}\end{array}\right]=\left[\begin{array}{ccc}\sin\theta&\cos\theta \\ -\cos\theta&\sin\theta\end{array}\right]\left[\begin{array}{c}\mathbf{v_{Sa}}\\ \mathbf{v_{S\beta}}\end{array}\right] \tag{6}\]  
where \(\mathbf{v_{Sa}}\), \(\mathbf{v_{Sb}}\) and \(\mathbf{v_{Sc}}\) are input voltages in three-phase (\(\mathbf{abc}\)) frame, \(\mathbf{v_{S\alpha}}\), \(\mathbf{v_{S\beta}}\), \(\mathbf{v_{d}}\), \(\mathbf{v_{q}}\), and \(\mathbf{v_{0}}\) are the source voltages in \(\alpha\beta 0\) and \(dq0\) frames, as shown in Figure 5.
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 2 Methodology
### Probabilistic Analysis of Slope Stability  
For engineers with a basic working knowledge of probability and statistics, the Monte Carlo simulation technique is a particularly potent tool for assessing the reliability or risk of complicated engineering systems [25; 26]. It generates a set of geometric characteristic values based on their joint probability distribution function (PDF) and inserts them into the slope model. The resulting simulations are then used to obtain information on the distribution and moments of the response variable (i.e., _Fs_) [24]. In other words, the \(\mu_{Fs}\) (the mean of _Fs_) and \(\sigma_{Fs}\) (the standard deviation of _Fs_) are used to evaluate the average stability state of the slope.  
It is evident that the accuracy of the probabilistic analysis is determined by the number of simulations (\(N\)) performed, and it increases as the number of simulations increases. However, too many simulations will inevitably result in tedious work. As a result, determining the number of simulation calculations \(N\) in a reasonable manner is critical.
# Perturbation-Based Explainable AI for ECG Sensor Data
## 1 Introduction  
Electrocardiography (ECG) is one of the most essential methods of examination in internal medicine. It is a non-invasive diagnostic method that records the electrical activity of the heart from the surface of the body. Electrodes placed on the skin measure the electrical potential differences that occur during the spread of the action potential through the myocardium. The ECG device then graphically records these changes as an ECG curve. According to the nature of the curve, it is possible to evaluate heart rate, heart rhythm, and many pathological conditions, including myocardial infarction. The correct evaluation of the ECG curve is crucial for determining the correct diagnosis, which often requires urgent treatment. Artificial intelligence methods could contribute to eliminating human error and to obtaining faster and more accurate diagnoses. Indeed, several models of artificial intelligence, especially deep neural networks, have produced significant results in solving various challenging tasks, including medical diagnostics. However, these complex models have the character of a \"black box\", meaning that, as a rule, they cannot provide a comprehensible explanation of the proposed decision for the doctor to assess. Therefore, in order to increase the credibility of these black-box models in the eyes of doctors, it is necessary to focus on their explainability.  
This article addresses the question of the explainability of artificial intelligence models of the deep neural network type for the diagnosis task on stream data from 12-lead ECG. For this purpose, in Section 2, we analyze relevant published papers focused on diagnostic models of ECG data which address the issue of explainability. As this analysis showed, most authors have trained deep neural network models with different architectures on an image representation of ECG data. Therefore, we focused on modeling ECG data in their initial form, in which they have the character of stream sensor data. The primary aim of the research presented in this article was to design a specific perturbation-based method to explain deep learning models trained on ECG signals, then verify the resultsby comparison to experienced medical students. In Section 3, we describe the dataset we used along with its preprocessing. In Section 4, we present the resulting model based on a convolutional neural network, which was trained on the available data. The main contribution is presented in Section 5, where the original explainability method is proposed and verified in a user study. Finally, the most significant contributions and open research problems are summarized in Section 6.
# Using Canola Oil Biodiesel as an Alternative Fuel in Diesel Engines: A Review
## 5 Results and Discussion  
### Spray Characteristics of Canola Oil Biodiesel  
[PERSON] et al. [59] conducted an in-depth study of the spray pattern of pure diesel (BD0) and pure canola oil biodiesel (BD100) under low and high injection pressures. The study results are shown in Figure 10. They reported that the spray tip penetration of BD0 and BD100 was longer at 150 MPa (high injection pressure), and the spray tip penetration of BD100 was similar to the spray tip penetration of BD0 at the same injection pressure. However, there was a minimal effect on the spray development of BD100 due to its higher viscosity; thus, the injection velocity of BD100 was lower than that of BD0. This behavior became more pronounced as the start of injection (SOI) time was advanced.  
Figure 10: The spray pattern of diesel and canola oil biodiesel at different injection pressures (adapted with permission from [59], Elsevier, 2017). SOI: start of injection.
# Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry
## 3 Experimental Methods
Figure 6: Experimental configuration of (**a**) the horizontal plane to investigate swing, and (**b**) the vertical plane to investigate the Magnus effect. Flow is from right to left. The yaw angle was set to \(\beta=0^{\circ}\) for the experiments investigating the Magnus effect. A mirror redirected the laser sheet to the \(x\)-\(z\) plane (passing through \(y=0\)), with the camera positioned from the side (shown in Figure 6b). The ball was rotated in the \(-\Omega\) direction followed by the \(+\Omega\) direction to capture the flow field around the upper and lower surfaces, respectively, with the camera and laser remaining fixed. The direction of \(+\Omega\) is shown in Figure 6. Time-averaged results were processed from 500 pairs of images collected at each station. All PIV measurements were processed on TSI Insight 4G, using the fast Fourier transform (FFT) correlation algorithm and a recursive Nyquist grid, which had a starting interrogation window of \(32\times 32\) pixels and 50% overlap, and a final window of \(16\times 16\) pixels (also with 50% overlap). For the swing experiments, the spatial resolution (the length scale corresponding to the size of the interrogation window) was less than 0.4% of the model diameter (\(<\)0.5 mm), and the resolution for the Magnus effect experiments was around 0.5% of diameter (\(\sim\)0.7 mm).
## 4 Design of the DRFTM
### Trust Evaluation Based on Random Forest
#### 4.2.1 Data Preprocessing  
The trust evidence collected may be affected by noise in the underwater acoustic channel, and the calculations for different trust evidence have varying standards. Therefore, it is not possible to directly input these trust evidence data into the model. To address this issue, all collected trust evidence needs to be normalized, meaning it is scaled to fall within the range of \([0,1]\).