esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results  
### Dataset  
As shown in Table 1, the dataset used in this study comprises 2800 images. The original image size was 1500 \(\times\) 1500 pixels, and the input image size was adjusted to 640 \(\times\) 640 pixels for model training. The resolution of 640 \(\times\) 640 pixels used in this study was selected considering the characteristics of the dataset, computational efficiency, and practical usability. Our test results indicate that despite compressing the initial image resolution from 1500 \(\times\) 1500 pixels to 640 \(\times\) 640 pixels, there was no significant differencefound between these two resolutions. Increasing the resolution could improve the detail in images, but this would also increase the computational cost, processing time, and memory usage. Specifically, the computational cost showed an increase of more than four times. Therefore, in this study, we determined that choosing a balanced resolution is crucial, and through experimentation, 640 \(\times\) 640 pixels was confirmed as the optimal resolution. Moreover, the dataset was split into training, validation, and test sets in a 60%, 20%, and 20% ratio, respectively. By allocating relatively larger validation and test sets, we can more accurately assess how well the model generalizes to new data. This is especially pertinent in manufacturing, where there is a high risk of overfitting. Thus, larger validation and test sets help mitigate this risk and enhance the reliability of the model. Additionally, to ensure consistency and reproducibility of the experiments, random seeds were fixed during the data-splitting process.  
Table 2 lists the number of labels for each class. The b_edge, burr, broken, b_bubble, etc_abnormal, and no_lens classes contain 483, 411, 431, 427, 423, and 400 labeled images, respectively. In this dataset, selective adjustments are made to ensure a balanced number of labels for each class, helping prevent bias during model training and ensuring fair and accurate classification of all abnormal types. Additionally, the dataset includes images that do not belong to any of the mentioned classes, which are labeled as \"good\" products, indicating they do not exhibit any defects.
## 4 Numerical Simulation Verification and Discussion
### Comparative Analysis of Numerical Simulation and Computational Theory Results  
Figure 13 shows the finite element simulation and theoretical calculation results under different working conditions. From Figure 13a,b, it can be seen that the difference between the theoretical calculation and the finite element simulation results in this paper is relatively small. The vertical deformation of the pipeline caused by shield tunneling in the left and right tunnels is more consistent. It proves the correctness of the theoretical formula. The deformation of the pipeline obtained from the theoretical calculation is slightly smaller than that of the finite element calculation because the deformation of the soil body reaches its critical value during the operation of the shield tunneling machine, and thus a certain plastic deformation occurs. In the derivation of the theoretical formula, it was assumed that the foundation reaction force and displacement change linearly, which makes the foundation reaction force play a large role, resulting in a prediction of pipeline deformation slightly lower than the numerical simulation results.  
As shown in Figure 13a, the deformation of the first half of the pipeline is larger than that of the second half under Case 1 and Case 2 conditions, and the deformation of the second half of the pipeline is slightly larger than that of the first half under Case 3 conditions. The reason for this is that the additional load on the pipeline gradually changes from concentrating on the front half to concentrating on the back half as the shield machine keeps digging.  
As shown in Figure 13b, the vertical deformation of the pipeline under Case 4, 5, and 6 conditions shows a \"single peak\". This is mainly due to the fact that in the process of shield tunneling, the additional load generated by the shield machine gradually shifted from the two ends to the center section of the pipeline, making the additional load concentrated in the center section of the pipeline.
# MOCA: A Network Intrusion Monitoring and Classification System
## 5 Measurement Results
### Binary Classification Accuracy  
Table 1 shows the classification accuracy of an XGBoost-based classifier trained on different numbers of features. The table shows that using top-10 important features, including three connection features, can detect almost all abnormal samples in both datasets. Combining top-2 basic features (\"Bwd Packet Length Min\", \"Subflow Fwd Bytes\") with top-3 connection features (\(dport\_count\), \(dst\_src\_count\), \(src\_dport\_count\)) can distinguish abnormal traffic from normal traffic with an accuracy of 98.9% in the CICIDS2017 dataset and 99.98% in the CICDDOS2019 dataset. Using the three connection features detects 95% of abnormal cases in the CICIDS2017 dataset and 99.91% of attacks in the CICDDOS2019 dataset. MOCA has a higher accuracy rate for the CICDDOS2019 dataset. All attacks in the CICDDOS2019 dataset are DDoS attacks, which are sensitive to connection features. One aspect to highlight regarding the importance of connection features is that using top-5 basic features cannot detect all attacks.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 4 Verification of the Proposed Method  
In this section, firstly, an initial finite element model that needs to be modified is established, and the model is regarded as the initial structure; secondly, the physical parameters in the model are adjusted appropriately, and the modified parameters are used as the physical parameters of the structure to establish a new finite element model, and the model is regarded as the actual structure; then, based on the difference in static and dynamic responses of the two models, the model is modified based on the WDO and CNN; finally, the updating results are compared and analyzed: If the difference between updated and actual values is slight, the model updating is completed.
# Reliability Assessment of RC Bridges Subjected to Seismic Loadings
## 4 Example of Application
### Mean Annual Demand Exceedance Rate  
Demand hazard curves indicate the number of times that a certain drift threshold is exceeded per unit of time. The inverse of a certain value of demand hazard is the return period; this implies how many years a level of damage is expected to be exceeded. The above results are helpful for decision-making processes when the limit state under consideration is reached. Figure 9 illustrates the demand hazard curves obtained by closed-form expressions considering aleatory and epistemic uncertainties Equation (9) and numerical integration Equation (7). The mean annual rate of exceedance of the minimum intensity, \(v\), is equal to 0.2308. The parameters \(a\) and \(b\) are obtained in Section 4.5, while parameters \(k\) and \(r\) are defined in Section 4.6. Epistemic uncertainties are considered with a value of 0.25 [38]. Demand hazard curves are built considering the maximum drift at the bridge deck for values from 0.001 to 0.012. In general, a difference of 5% between the two proposed approaches is observed. Such difference is mainly associated with the following: (a) epistemic uncertainties were considered in the closed-form expression, whereas the numerical solution does not consider such kind of uncertainties; and (b) there might be a bias when SAHC is fitted. If the values given by the closed-form expression are considered, a return period of 28 years is observed for threshold equal to 0.002, implying a mean annual demand rate of 0.03608; on the other hand, a mean annual demand frequency of 0.01738 is obtained for the serviceability limit state, which is associated with a return period of 58 years. For threshold \(d=0.006\), a mean annual demand rate of 0.00837 is obtained, which is associated with a return period of 119 years, whereas a mean annual exceedance rate of 0.000935 is obtained for the limit state of collapse, implying a return period of 1068 years. Thus, it is inferred that it takes a long time to exceed higher drift thresholds.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example  
### Data Applied  
Throughout this paragraph, the process of implementing Machine Learning algorithms to a set of maintenance data will be detailed and explained.  
First, the data set is presented and the choice is justified. Then, the objectives of this Machine Learning application are rigorously and clearly established. Subsequently, the data set is processed through feature engineering, creating new features in order to seek better performance from the models. The data set used is key to solving Machine Learning problems. A sensible choice of what data to use and how to handle it is crucial to improving the performance of the algorithms. According to [PERSON] (2019), feature engineering is the key to Machine Learning projects and that, often, the measured signals are not suitable for the learning process, and it is necessary to build features from those that are.  
Then, the data set is divided into training, validation and testing subsets and the first application of Machine Learning models was carried out, where a variety of algorithms were trained and evaluated. The training process of the algorithms is carried out in the training subset and the validation subset provides an impartial assessment of the fit of the models to the training data, while simultaneously fine-tuning the model and its hyper-parameters in order to seek better performance. Finally, the test set is used to obtain an estimate of the model's performance, simulating its behavior for future data.  
The implementation described in this chapter will be carried out using the Python programming language, using the packages Matplotlib, Numpy, Pandas and Scikit-Learn (2019); (2020).
## 2 The Composition and Mathematical Model of Doubly-Fed Variable-Speed Pumped Storage Units
### Model of DFIM  
For the convenience of analysis and control, the model of DFIM in three static coordinate systems is transformed into the model of two-phase rotating coordinate system by using the constant amplitude transformation. In addition, the nonlinear strongly coupled system is converted into linear decoupling system [23; 24; 25].  
Voltage equation  
\[\left\{\begin{array}{l}u_{sd}=R_{s}i_{sd}+p\Psi_{sd}-\omega_{s}\Psi_{sq}\\ u_{sq}=R_{s}i_{sq}+p\Psi_{sq}+\omega_{s}\Psi_{sd}\\ u_{rd}=R_{r}i_{rd}+p\Psi_{rd}-s\omega_{s}\Psi_{rq}\\ u_{rq}=R_{r}i_{rq}+p\Psi_{rq}+s\omega_{s}\Psi_{rd}\end{array}\right. \tag{6}\]  
where \(u_{sd}\), \(u_{sq}\), \(u_{rd}\) and \(u_{rq}\) are the d, q-axis voltage of stator and rotor; \(i_{sd}\), \(i_{sq}\), \(i_{nd}\) and \(i_{rq}\) are the \(d\), \(q\)-axis current of stator and rotor; \(\psi_{sd}\), \(\psi_{sq}\), \(\psi_{rd}\) and \(\psi_{rq}\) are the \(d\), \(q\)-axis flux linkage of stator and rotor; \(R_{s}\), \(R_{r}\) are the resistance of stator and rotor; \(p\) is Laplace operator; \(\omega_{s}\) is synchronous speed of DFIM; \(s\) is the slip of DFIM.  
Flux equation  
\[\left\{\begin{array}{l}\Psi_{sd}=L_{s}i_{sd}+L_{m}i_{nd}\\ \Psi_{sq}=L_{s}i_{sq}+L_{m}i_{rq}\\ \Psi_{rd}=L_{m}i_{sd}+L_{r}i_{nd}\\ \Psi_{rq}=L_{m}i_{sq}+L_{r}i_{rq}\end{array}\right., \tag{7}\]  
where \(L_{s}\), \(L_{r}\) are the inductance of stator and rotor; \(L_{m}\) is the mutual inductance.  
Electromagnetic torque equation  
\[T_{e}=\frac{3}{2}p_{n}L_{m}(i_{sq}i_{rd}-i_{sd}i_{rq}), \tag{8}\]  
where \(p_{n}\) is the number of poles.  
Motion equation  
\[\frac{J}{p_{n}}\frac{d\omega_{m}}{dt}=T_{e}-T_{m}-B_{m}\omega_{m}, \tag{9}\]  
where \(\omega_{m}\) is speed of DFIM; \(J\) is rotational inertia of DFIM; \(B_{m}\) is friction coefficient of DFIM.  
Figure 7: Pump mode model under small guide vane opening.  
For the convenience of analysis and control, voltage oriented vector control strategy is adopted, and then:  
\[\left\{\begin{array}{l}u_{sd}=U_{s}\\ u_{sq}=0\end{array}\right., \tag{10}\]  
where \(U_{s}\) is effective value of grid voltage.  
When the influence of resistance and the dynamic change process of flux on the transition process is ignored, the relationship between the quantities is derived, and:  
\[\left\{\begin{array}{l}P_{s}=\frac{3}{2}(u_{sq}i_{sq}+u_{sd}i_{sd})=-\frac{3 }{2}\frac{L_{m}U_{s}}{L_{s}}i_{rd}\\ Q_{s}=\frac{3}{2}(u_{sq}i_{sd}-u_{sd}i_{sq})=\frac{3}{2}U_{s}[\frac{U_{s}}{L_{ s}\omega_{s}}+\frac{L_{m}}{L_{s}}i_{rd}]\end{array}\right., \tag{11}\]  
\[T_{e}=\frac{3}{2}p_{n}L_{m}(i_{sq}i_{rd}-i_{sd}i_{rq})=-\frac{3}{2}\frac{p_{n}U _{s}L_{m}}{L_{s}\omega_{s}}i_{rd}. \tag{12}\]  
Decoupling control of active power and reactive power of DFIM is realized.  
The values of the parameters of DFIM in this paper are shown in Table 1.
## 2 Materials and Methods
### Inductor Sizing in a Conventional Boost Converter for PV Applications  
As we can see from Figure 2, the operating point of the PVG strongly depends on the solar radiation (G) and the characteristics of the associated load (R\({}_{1}\), R\({}_{2}\), R\({}_{3}\), and R\({}_{4}\) in Figure 2) [2; 3; 11]. For a given radiation value, there is a particular operating point, called \"Maximum Power Point\" (MPP), noted MPP1 and MPP2 in Figure 2, which leads to getting the maximum power from the PVG. We can observe that there is a difference between the MPP that the PVG can produce and the power transferred to the load in direct connection mode.  
The commonly adopted solution to solve this problem is to introduce a static converter to force the PVG working in its MPP, which acts like a source-load adapter [3; 15; 16], as shown in Figure 1.  
The action to the DC/DC converter to track the maximum power point is delivered by the MPPT controller for any climate condition. The converter structure is chosen depending on the load and the generated PVG voltage. Among the various methods adopted to get MPP, the P&O method is widely adopted. It requires the measurement of the PVG output voltage V\({}_{\rm pv}\) and output current I\({}_{\rm pv}\). It detects the MPP and gives the output voltage where the power is maximum [1; 6].  
In our case, we use a boost topology controlled by the P&O_MPPT controller to ensure the adaptation between the load and the PV generator. The electric circuit of the boost converter is shown in Figure 3. The operation of the boost converter in the continuous conduction mode is described in detail in the Appendix A.  
The sizing of the components in the boost converter depends on the input voltage V\({}_{\rm i}\), and the input current I\({}_{\rm i}\). The components to be sized and selected accordingly are the inductance of the inductor L, the diode D, the values of capacitors C\({}_{1}\) and C\({}_{2}\), and the switching transistor K. When these components are properly chosen, the energy loss can be neglected [18; 19]. The element mainly influencing the boost converter losses is the inductor because its sizing depends directly on the input voltage as well as on the input current. In the case of PV applications, the voltage and current changes depend on climate parameters G and T.  
Figure 3: Simplified schematic of a boost converter.
## 5 Conclusions  
The VLL loop filter only processes velocity and is insensitive to acceleration and jerk, which leads to the difficulty of maintaining steady-state tracking in high dynamic conditions. Therefore, after analyzing the accumulated phase tracking error generated by acceleration and jerk, the HD-VLL is proposed in this paper. The HD-VLL loop filter adds an acceleration compensation module and a jerk compensation module to the VLL loop filter, which compensates the phase variation generated by acceleration and jerk with the system clock. As for the engineering implementation, the HD-VLL-NCO is presented. The formula of each order of the frequency word and the bit width selection scheme are derived. The simulation results show that, compared with the VLL, the HD-VLL has better dynamic adaptability and steady-state tracking performance, and by setting appropriate truncation widths, the HD-VLL-NCO can achieve comparable performance to the HD-VLL. Specifically, the research results of this paper will provide technology support for frequency agile radar applications.  
Conceptualization, [PERSON] ([PERSON]); methodology, Y.L. and J.W. ([PERSON]): software, Y.L. and X.X.; writing--original draft preparation, Y.L. and J.W. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and Y.Z.; supervision, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant number 61971043).  
The data are available from the corresponding author upon reasonable request. The data are not publicly available due to the fact that there are still graduate students using them for research.  
Figure 17: The tracking results of the HD-VLL-NCO and HD-VLL (5 Hz). (**a**) The velocity estimate curves. (**b**) The velocity error curves.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
## 2 Materials and Methods
### Nitrogen and Mercury Intrusion Porosimetry  
Nitrogen porosimetric analysis was carried out using a Micromeritics ASAP 2460 apparatus (Micromeritics, Norcross, GA, USA). Before each measurement, all the specimens were subjected to a thermal treatment (degas procedure) under vacuum (\(p\leq 10\times 10^{-6}\) mbar) at 100 \({}^{\circ}\)C for 24 h. The \(N_{2}\) adsorption/desorption isotherms of each specimen were acquired at \(-\)196 \({}^{\circ}\)C up to 1 bar of pressure. Surfaces and porosity features were then evaluated from the experimental \(N_{2}\) adsorption/desorption isotherms. In particular, the first was calculated according to the Brunner-Emmett-Teller model (BET) [45] within relative pressure range 0.05-0.3, while pore size distribution (PSD), cumulative, and pore volume (CPV) were evaluated by Barrett, Joyner, and Halenda (BJH) analysis using the Kelvin model of pore filling [46; 47; 48; 49].  
MIP was performed on a Thermo Finnigan Pascal 240 Series porosimeter (maximum pressure, 200 MPa) coupled with a low-pressure unit (140 Series) able to generate a high vacuum level (10 Pa) and operate between 100 and 400 kPa. During the test, with increasing pressure, mercury gradually penetrates the bulk sample volume. Moreover, as the MIP data do not provide any information on the pore size distribution in the sample, an appropriate model has to be used. In this regard, if a system of cylindrical pores is considered, the Washburn equation (Equation (5)) can be applied; it allows us to estimate the diameter of cylindrical pores intruded at each pressuring step, namely:  
\[d=-\frac{4\gamma\cos\theta}{P} \tag{5}\]  
where \(d\) is the diameter of the cylinder being intruded, \(\gamma\) is the surface tension of mercury, \(\theta\) is the contact angle of mercury on the solid, and \(P\) is the applied pressure.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 3 Proposed Methods
### Densely Connected Atrous Spatial Pyramid Pooling Module  
In skin lesion image segmentation, both global and local information are equally important. However, due to the different sizes of lesions, it may cause the feature map to shrink. To handle features at different scales, it is necessary to utilize convolutional kernels of different scales to obtain different information, and leverage multi-scale information effectively for skin lesion image segmentation. The traditional parallel ASPP structure consists of five sub-branches, each using a different convolutional kernel to process the input feature map and cascading them at the output. The structure is shown in Figure 5.  
Figure 4: Cross-residual encoder module.  
DenseASPP shares feature information through a cascade method that differs from the parallel connection method using atrous convolutions in ASPP. In DenseASPP, the output of each atrous convolution layer needs to be cascaded with the outputs of all previous convolution layers and input feature maps as the input of the next atrous convolution layer. Before each atrous convolution layer, \(1\times 1\) convolution is used to integrate feature information. Based on Equation (2), the output of each atrous convolution layer depends on the outputs of all previous layers. This means that layers with smaller dilation rates and those with larger rates depend on each other and form a dense feature pyramid. Through this process, DenseASPP can perceive larger contextual information and use larger filters for feedforward propagation, thus improving the performance of image segmentation tasks.  
\[F_{kd_{i}}=D_{kd_{i}}C([F_{kd_{i-1}},F_{kd_{i-2}},\cdots,F_{kd_{0}}]) \tag{2}\]  
where \(k\) is the size of the convolutional kernel, \(d\) indicates the expansion rate of the atrous convolution, \([\cdot]\) indicates the cascade operation, \(C([\cdot])\) indicates the \(1\times 1\) convolution operation, \(D_{kd_{i}}\) represents the atrous convolutional layer of the layer \(i\), and \(F_{kd_{i}}\) is the atrous convolution output of the layer \(i\). Equation (3) shows the size of its receptive field. When \(K_{1}\) and \(K_{2}\) represent atrous convolution receptive fields with different expansion rates, respectively, the size of the convolutional kernel receptive fields after the superposition of the two atrous convolutions is shown in Equation (4).  
\[K_{d}=k+(k-1)\times(d-1) \tag{3}\]  
\[K=K_{1}+K_{2}-1 \tag{4}\]  
In order to solve the problem of atrous convolutions with excessive dilation rates potentially extracting many unimportant features, this paper proposes the use of four scales of atrous convolutions with dilations of 3, 5, 7, and 9. Using Equations (3) and (4), it is found that the combination of these four convolutional layers can form a structure with a receptive field size of 51. This structure extends the range of the receptive field yet does not reduce the local dimensionality, enabling the acquisition of feature information of various sizes. The structure is presented in Figure 6.  
Figure 5: Structure of the ASPP network.