esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Coatings
At the same time, more uniformly distributed particles are embedded on the surface, i.e. more active nucleation sites of less distance between them. As such, the deposition of the metallic matrix will follow a pattern of many, close together initial perturbations and will, in turn, lead to a landscape that will not show that extensive landscape. Heat treatment seems to have a dual effect. In the case of the plain coatings (no SiC reinforcement), heat treatment seems to increase the intensity of the surface landscape and, as such, the surface roughness. P incorporation during the plating stage induces amorphisation, as already mentioned [1; 2; 3; 4; 5; 6]. This amorphisation is reported to be initiated by the severe crystalline grain growth restriction. Severe grain growth restriction leads to extreme grain refinement, nanocrystallinity, and amorphisation in order of appearance. Lack of crystalline grains means the lack of grain boundaries and a lack of preferential directional crystal orientation upon plating, i.e., a smooth surface finish and morphology. On the other hand, heat treatment induces crystallization, i.e., grain boundaries and preferential growth orientations, i.e., induces features that can account for the development of a distinguishing surface landscape. Heat treatment causes an opposite effect in the case of the SiC reinforced coatings, since it significantly reduced the initial as-coated surface roughness. This tendency is most likely associated with the fact that the involved mechanisms during heat treatment have a beneficial effect on the healing the imperfections that are induced by the presence of particles in the as-deposited stated, according to what has been mentioned in previous paragraphs. Conclusively, in the case of SiC reinforced coatings, an initial high SiC content within the plated coating, along with the subsequent treatment, may lead to a smooth, uniform coating of low roughness and well accepted surface finish. Figure 6 shows a typical two-dimensional (2D) and three-dimensional (3D) surface reconstruction from the profilometry measurements, for the samples of non-treated Ni-P and heat-treated Ni-P/SiC electroplating coatings.
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 4 Application of the GSGWO in Path Planning of Obstacle-Crossing Robot
### Fitness Function  
The fitness function is a function that measures the pros and cons of candidate solutions in solving problems. A better fitness function can play an important role in guiding the convergence process of the algorithm. The general path-planning algorithm uses the path length equation as the fitness function, and some algorithms also introduce the influencing factors of the inflection point. In the path planning of an obstacle-crossing robot, the cost of obstacle-crossing action must be introduced as one of the evaluation criteria. Therefore, the fitness function used in this paper is as follows:  
\[V_{f}=L+\lambda\sum_{i=1}^{k}V_{ti}+\mu\sum_{j=1}^{l}V_{pj}, \tag{15}\]  
where \(V_{f}\) is the total cost of the path, and the greater the cost, the lower the fitness. \(L\) is the total path length. \(V_{ti}\) and \(V_{pj}\) are the turning cost and obstacle-crossing cost of the path, respectively. \(\lambda\) and \(\mu\) are the weight coefficients of turning and obstacle crossing, respectively.  
The evaluation of turning cost adopts the method of hierarchical processing. The larger the turning angle is, the larger the cost coefficient is. The specific rules are shown in Equation (16). The obstacle-crossing cost is classified: if the path passes through the center point of the obstacle area, it is regarded as a bilateral obstacle crossing, and the obstacle-crossing cost is 1. If the connection of the path passes through the obstacle area and does not pass through the center point of the area, it is regarded as a unilateral obstacle crossing, and the obstacle-crossing cost is 0.5.  
\[V_{t}=\left\{\begin{array}{ll}\varphi&,\varphi\leq\pi/6\\ 3\varphi&,\pi/6\textless\varphi\leq\pi/3\\ 9\varphi&,\pi/3\textless\varphi\leq\pi/2\\ 100\varphi,\varphi\geq\pi/2\end{array},\right. \tag{16}\]  
where \(\varphi\) is the corner size, and the unit is the radian system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Before Optimization** & **After Optimization** \\ \hline Path length & 14.4853 & 13.7509 \\ Number of inflection points & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Node optimization data comparison.  
Figure 4: Node optimization.
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 5 Performance Evaluation
### Phase One: Network Performance
#### 5.1.2 D-Ito  
D-ITG was used to send three simultaneous data flows between hosts H1 to H6, H2 to H5 and H3 to H4 applying Controller default and QoSComm data flow forwarding methods in the simulation model. We created a script to generate three processes to transfer simultaneous data flows with different constant packet rate (pps-C) and with 512 as constant payload size-c. In H6, H5, and H4 hosts, we launched a D-ITG Receive server. H1, H2, and H3 hosts were used to send data flows to corresponding receive server. We transferred the UDP data flows from H1 to H6, H2 to H5, and H3 to H4 hosts for 30 s. Figure 7 shows D-ITG transfer average results which are the sum of mbps transferred by the three processes for each test case. We performed two sets: individually (i) and simultaneously (s) tests.  
In most of the cases, it is observed that QoSComm network path selection obtained around 2-4% of a higher data transfer rate than Controller default path selection. Only in H1 to H6 individual tests did results shows less than 1% of difference between Controller and QoSComm. For network stress testing running host-to-host simultaneously transfers, QoSComm also obtained a higher data transfer rate compared to Controller default path selection. Using QoSComm, we improved the application data transfer performance.  
Figure 6: IPERF TCP data transfer comparison (average mbps). Tests execution: (i) individually; (s) simultaneously. Time interval: 30 s. STDEV: H1-H6i (2.03, 0.85), H2-H5i (0.67, 0.57), H3-H4i (4.29, 6.36); H1-H6s (1.23, 0.54), H2-H5s (0.41, 0.87), H3-H4s (3.20, 3.64).  
Figure 7: D-ITG data transfer comparison (average mbps). Tests execution: (i) individually; (s) simultaneously. Time interval: 30 s. STDEV: H1-H6i (0.36, 0.60), H2-H5i (0.13, 0.21), H3-H4i (0.50, 0.05); H1-H6s (0.78, 0.05), H2-H5s (0.42, 0.16), H3-H4s (0.13, 0.07).
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 2 Theoretical Foundation
### Ensemble Learning  
In the application of machine learning, the ideal scenario involves training a single model that learns the patterns or underlying rules within the provided data and makes accurate predictions for the task at hand. However, in reality, this is often not the case. The complexity of data, imbalanced data distributions, and the presence of significant noise, among other factors, make it challenging for a single model to achieve ideal predictive outcomes. As per the research presented by [PERSON] [23], these challenges can limit the performance of a single model.  
Ensemble learning is a machine learning strategy that combines predictions from multiple machine learning models to improve overall predictive accuracy. According to a review study [24], ensemble learning can enhance generalization capability by training multiple models and combining their predictions. It has found wide application across various domains. The following are some common ensemble methods, including bagging, boosting, stacking, and voting. (1) Bagging is based on random sampling from the raw data to create multiple different training datasets. Several models are then trained on these datasets, and the predictions from each model are averaged or voted on to enhance model performance. Random sampling is achieved through the bootstrap method, where  
Figure 1: Cutting-tool wear processes.  
subsets are randomly selected, with replacement, from the raw data. For classification problems, the final output is determined by voting according to the predictions of each model. For regression tasks, the final output is the average of the predictions from the models. (2) Boosting is an iterative ensemble learning method. It relies on adjusting sample weights and iteratively optimizing the model. Each round of learning depends on the previous round's results. Errors in predictions are given higher weights to improve performance in the subsequent round. Each model learns from and corrects errors in the previous model, resulting in improved predictive accuracy. (3) Stacking aims to combine the strengths of multiple base models. This approach first trains multiple different base models, and the final output is derived from a meta-learner that is trained on the predictions of the base models and their correspondence with the true values. This method leverages the power of multiple models to enhance overall predictive accuracy. (4) Voting combines predictions from multiple models. For classification tasks, each model makes predictions, and the final output is determined by majority voting. In regression problems, the final prediction is the average of the base models' predictions. Voting can be effective when the base models have different strengths and weaknesses, allowing them to complement each other.  
Ensemble learning is widely applied and studied in the field of machine learning. It leverages the combination of different machine learning models to enhance predictive capabilities and achieve more accurate results.
## 1 Introduction
Thus, the final aim of this study is twofold: first, to demonstrate that the SOM-SARIMA-SVR outperforms the rest of possible hybrid or combined models in forecasting the daily number of containers passing through BIP of a maritime port; and second, to test the generalization capabilities of SVR in forecasting logistic tasks, especially the container inspection process that can be a bottleneck in the supply chain. To train the new model, a three-step procedure was developed. In the first step a SOM algorithm allows to divide the database into different clusters or regions. On a second step, a SARIMA model is fitted to the data of each cluster in order to capture the seasonality and the linear behavior. Finally, a SVR model is applied over each obtained cluster considering several hybrid approaches (different input configurations) which can consider the original database and the outputs of the first step (residual and predicted values). The rest of the paper is organized as follows: The second section gives an overview of self-organizing maps (SOM), seasonal autoregressive integrated moving average (SARIMA) and support vector machines for regression (SVR). The empirical data, the combined-hybrid methodology, and the experimental procedure are presented in Section 3. The fourth section discusses the results obtained. Finally, the last section summarizes the important conclusions of this work.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions
### Impact of Rainfall Patterns on Parameter Sensitivity  
Model parameter sensitivity varies significantly with different rainfall patterns. In cxzd50a and Chicago50a rainfall events, where precipitation intensifies early before tapering off, minimum infiltration rate and pipe roughness exhibit the highest sensitivity. In contrast, the szzbl50a rainfall pattern, characterized by two distinct peaks, results in greater sensitivity of the Manning's coefficient of impervious surfaces while reducing the influence of minimum infiltration rate and pipe roughness. This suggests that the temporal distribution of rainfall peaks plays a crucial role in parameter sensitivity, reinforcing the need for scenario-based calibration strategies in flood modeling.
## 2 Methodology
### Extracting \(t_{A}\)
#### 3.1.3 Extracting \(t_{A}\)  
After Knowing the range migration curve \(R(t,\tau)\), we could construct the fitted function \(R_{f}(t,\tau)\). The process for extracting \(t_{A}\) is as follows:  
1. Find N points far away from the minimum of the curve at one side, and note the abscissa (Pulse number position) as well as the ordinate values (Interpolated range cell position).
2. Find other N points at the other side, whose ordinates are equal to the first N points, and note their abscissas.
3. Obtain the satellite's ceiling time \(t_{A}\) by calculating the mean value of the abscissas of the 2N points.  
The larger the N is, the more accurate the obtained \(t_{A}\) is.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Environmental Monitoring
By utilizing the EOS507F equipped with MOX gas sensors, the authors showcase the efficacy of electronic nose technology in assessing odor impacts, particularly in scenarios where traditional dispersion modeling methods are impractical. Their work contributes to the standardization of odor measurement tools and offers novel strategies for managing odor pollution. Figure 5: (**a**) The e-nose system hardware, showing the inlet and outlet ducts for the constant mixture flux training phase that is made in the laboratory. (**b**) Block diagram of the proposed system [123]. The emissions produced by these industries not only pose risks to their employees but also contribute to urban discomfort. Presently, efforts are underway to develop e-noses that provide ongoing monitoring of urban odors. The Port of Rotterdam, the largest port in Europe, features extensive industrialization, including four major oil refineries and over 40 chemical/petrochemical firms [129]. This high level of industrial activity has resulted in elevated odor levels, leading to an annual average of 5000-6000 odor complaints from residents [129]. To address this issue and pinpoint odor sources, a collaborative initiative known as the We-Nose network was established, bringing together companies, municipalities, and residents. To combat odor nuisances and identify their origins, a network of over 250 e-noses, each equipped with four semiconductor-based sensors, was strategically placed across the expansive 10 km \(\times\) 10 km port area in Rotterdam. These e-noses underwent training to recognize the distinctive odor patterns associated with residents' complaints [110]. Following a learning phase, the sensors were able to successfully match over 90% of reported odor complaints [129]. By comparing the identified odor patterns with an existing database, the system gained the ability to attribute odors to specific sources. The placement of the e-noses considered topography and meteorological conditions, enabling a spatial understanding of odor origins. Additionally, Figure 7 demonstrates another instance of an e-nose system, this time designed for the continuous monitoring of environmental odors. This figure includes a map that indicates the locations of the EOS507F electronic noses in relation to water treatment plants, as cited in reference [130]. This system plays a crucial role in reducing accidental emissions and has substantially decreased the number of odor complaints from nearby residents. Electronic nose (e-nose) systems are also utilized to comprehensively assess various parameters related to environmental sources of malodors and air pollution. This extensive application covers sources including waste disposal sites, wastewater treatment facilities, incineration plants, composting operations, and livestock farms. A detailed review of these varied uses is thoroughly presented in Table 4.  
Figure 6: Electronic noses used for the tests in laboratory (**a**) and EOS 507 in the field (**b**) [128].
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks
### Proposed Estimation Scheme
During the disturbance, the output power of IBDG is assumed to be constant; hence, \(K_{i}=\dot{U}_{0}/\dot{U}_{i}\), which can be numerically calculated. Denote the vector \(x=\left[x_{1},\ x_{2}\right]^{T}=\left[Z_{eq},\ l_{DC0}Z_{eq}\right]^{T}\), and the following matrix equation is obtained by subtracting the first equation from the other equations in Equation (1). \[\mathbf{Ax}=\mathbf{B}\Rightarrow\left[ \begin{array}{cc}\Delta\dot{I}_{1}&K _{0}-K_{1}\\ \Delta\dot{I}_{2}&K_{0}-K_{2}\\ \ldots\\ \dot{\Delta}_{n}&K_{0}-K_{n}\end{array} \right]\left[ \begin{array}{c}x_{1}\\ x_{2}\end{array} \right]=-\left[ \begin{array}{c}\Delta\dot{U}_{1}\\ \Delta\dot{U}_{2}\\ \ldots\\ \Delta\dot{U}_{n}\end{array} \right] \tag{2}\]If \(n=2\) and the rows of matrix \(\mathbf{A}\) are independent to each other, Equation (2) is solvable. That is to say, two independent disturbances are exactly-enough to determine the vector \(x\) in theory. If \(n>2\), however, the vector \(x\) can also be determined as a least-square solution. Considering the possible noises and modelling errors, it is better to use more than exactly-enough measurements from different disturbance to promote the confidence level in results. Then, there are two questions to be addressed. Firstly, the direction of the disturbance needs to be identified so as to take advantage of the useful fluctuations occurring on the downstream to form the coefficient matrix in Equation (2). As discussed above, only \(Z_{eq}\) and \(I_{DC0}\) upstream to the studied circuit breaker are required to be estimated, so it should be guaranteed that no change happens in the power grid upstream. The proposed method can only depend on the disturbances downstream to derive the results. If the disturbance upstream is detected, the initial measurements \(\dot{U}_{0}\) and \(\dot{I}_{0}\) should be updated, indicating the upstream grid is re-configured and new \(Z_{eq}\) or \(\dot{I}_{DC0}\) should be expected. Hence, it is important to distinguish the direction of disturbance. Secondly, the amplitudes of the voltage and the current variation in coefficient matrix in Equation (2) are also crucial for the estimation accuracy. Disturbances occur in the network time to time, while the experience shows that, as to the deviations, at least 5% are apparent enough to produce the qualified variation on the voltage and current. Moreover, there are also a series of naturally occurring disturbances, such as large motor starting-up, electric arc furnace operation, rapid load fluctuation, etc. An effective way to capture and rank the useful disturbance is also important for the proposed method. These two questions are to be discussed in the following section.
## 4 Results and Discussion  
### Experimental Setup  
#### 4.1.1 Dataset  
To evaluate the usability of the proposed method, we conducted experiments on two different public datasets, the MIT-BIH dataset and the SPH dataset.  
The MIT-BIH dataset [32] consists of 48 records from 47 individuals. The database contains a 30 min, two-channel ECG recording with a sampling rate of 360 Hz. Each heartbeat is annotated at the R-peak, providing beat-level information. Additionally, arrhythmia rhythm annotations are provided at the rhythm level. In this study, we utilized the MLII lead among the two leads and incorporated both beat-level and rhythm-level annotations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Type** & **Output Size** & **Kernel Size** & **Strides** \\ \hline Layer 1 & Inputs & \((150\times 360\times 1)\) & \(7\times 7\) & \(2\times 2\) \\ Layer 2 & 2D Convolution layer & \((75\times 180\times 64)\) & \(3\times 3\) & \(2\times 2\) \\ Layer 3 & Max pooling & \((37\times 89\times 64)\) & \(3\times 3\) & \(2\times 2\) \\ Layer 4 & SE-Identity Block & \((37\times 89\times 64)\) & \(3\times 3/5\times 5\) & \(1\times 1/1\times 1\) \\ Layer 5 & SE-Identity Block & \((37\times 89\times 64)\) & \(3\times 3/5\times 5\) & \(1\times 1/1\times 1\) \\ Layer 6 & SE-Convolution Block & \((19\times 45\times 128)\) & \(3\times 3/5\times 5\) & \(1\times 2/1\times 1/2\times 2\) \\ Layer 7 & SE-Identity Block & \((19\times 45\times 128)\) & \(3\times 3/5\times 5\) & \(1\times 1/1\times 1\) \\ Layer 8 & SE-Convolution Block & \((10\times 23\times 256)\) & \(3\times 3/5\times 5/1\times 1\) & \(2\times 2/1\times 1/2\times 2\) \\ Layer 9 & SE-Identity Block & \((10\times 23\times 256)\) & \(3\times 3/5\times 5\) & \(1\times 1/1\times 1\) \\ Layer 10 & SE-Convolution Block & \((5\times 12\times 512)\) & \(3\times 3/5\times 5/1\times 1\) & \(2\times 1/1\times 1/2\times 2\) \\ Layer 11 & SE-Identity Block & \((5\times 12\times 512)\) & \(3\times 3/5\times 5\) & \(1\times 1/1\times 1\) \\ Layer 12 & Global Average Pooling & \((512)\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The architecture of the rhythm classifier.  
Figure 4: Examples of PI-BSM for various arrhythmia rhythm classes, exhibiting clear pattern distinctions.  
The SPH dataset [33] is a publicly available, large-scale ECG database. It comprises 12 lead recordings from 10,646 patients, each lasting 10 s, with a sampling rate of 500 Hz. Cardiologists have annotated each recording into 11 rhythm classes and 56 cardiovascular conditions. In this study, we used only lead II data and rhythm annotations to evaluate our algorithm.
# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
## 3 Unknown Radar Emitter Identification Based on Semi-Supervised and Transfer Learning
### Training Effect Enhancement Based on Semi-Supervised Co-Training Algorithm  
The transfer learning-based support vector machine can select the appropriate samples from source domain \(D_{s}\) for the training on target domain \(D_{t}\), which can improve the final identification performance. Unlike the above, semi-supervised learning can use the unlabeled samples in target domain \(D_{t}\) to enhance the final training effect. This section constructs the semi-supervised co-training algorithm based on the base classifier SVM model. The specific steps are shown in Algorithm 2.  
```
1. For the radar emitter identification task, define and construct a feature set \(\mathbf{x}\), and divide it into two parts \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\).
2. Train the base classifier SVM model on the small number of labeled samples in target domain \(D_{t}\) by using the feature sets \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\) respectively, and obtain the classifiers \(f_{1}\) and \(f_{2}\).
3. For t = 1: N Perform identification on the unlabeled samples in target domain \(D_{t}\) by using the classifiers \(f_{1}\) and \(f_{2}\), respectively, and obtain the posterior probabilities of the samples belonging to each emitter category, and select p samples with the highest confidence for each category; Add the selected samples to the training set and retrain the classifiers \(f_{1}\) and \(f_{2}\) on the training set. End
```  
**Algorithm 2** Semi-supervised co-training algorithm  
The two feature sets \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\) in the co-training algorithm refer to two views and need to satisfy sufficient redundancy and conditional independence. Through continuous iterative training, unlabeled samples in the target domain \(D_{t}\) are available for labeling, which helps to enhance the training effect.