esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### Pressure Vessel Design  
There are countless real-world engineering problems that engineers are working to solve every day [47; 48; 49]. This section analyzes how well the suggested algorithms perform when it comes to minimizing the cost of producing a pressure vessel. A schematic view of the problem can be seen in Figure 6, and its formulation is expressed in Equations (15)-(20) [22].  
\[f(X)=0.6224x_{1}x_{3}x_{4}+1.7781x_{2}x_{3}^{2}+3.1661x_{1}^{2}x_{4}+19.84x_{1} ^{2}x_{3} \tag{15}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{ABC} & Number of onlooker bees & 80 \\ \cline{2-3}  & Number of employed bees & 120 \\ \cline{2-3}  & Number of scout bees (population size) & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \multirow{6}{*}{BBO} & The probability range for migrating & [0, 1] \\ \cline{2-3}  & Maximum emigration (I) and immigration (E) rates & 1 \\ \cline{2-3}  & Elitism percent & 10\% \\ \cline{2-3}  & Mutation rate & 0.12 \\ \cline{2-3}  & Population size & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \multirow{6}{*}{PSO} & The inertial movement rate (\(\alpha\)) & 0.11 \\ \cline{2-3}  & The movement toward the best personal experience rate & 0.65 \\ \cline{2-3}  & The movement toward the best global experience rate & 0.93 \\ \cline{2-3}  & Population size & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 6: Schematic view of the pressure vessel design.  
\[g_{1}(X)=-x_{1}+0.0193x_{3}\leq 0 \tag{16}\]  
\[g_{2}(X)=-x_{2}+0.00954x_{3}\leq 0 \tag{17}\]  
\[g_{3}(X)=-\pi x_{3}^{2}x_{4}-\frac{4}{3}\pi x_{3}^{3}+129,600\leq 0 \tag{18}\]  
\[g_{4}(X)=x_{4}-240\leq 0 \tag{19}\]  
\[0\leq x_{1},\ \ \ x_{2}\leq 100,\ \ 10\leq x_{3},\ \ \ x_{4}\leq 200 \tag{20}\]  
where \(x_{1}\) = the shell thickness (\(T_{s}\)), \(x_{2}\) = the head thickness (\(T_{h}\)), \(x_{3}\) = the radius of the cylindrical shell (\(R\)), and \(x_{4}\) = the length of the shell (\(L\)).  
Table 2 presents the outcomes of the meta-heuristic algorithms applied to the pressure vessel system. Among them, the HABC achieved the best outcome for the cost function, with a value of USD 5910/8253. This result was superior to that obtained by other algorithms. Additionally, HABC outperformed the others in the Mean F(x) and SD F(x) measures. Figure 7 illustrates the convergence trend of algorithms for the pressure vessel system. HABC exhibited a faster convergence rate than the other algorithms.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Best F(x)** & **Mean F(x)** & **SD F(x)** \\ \hline HABC & 5910.8253 & 5922.8652 & 0.0041590 \\ \hline OA & 5948.1589 & 5975.9625 & 0.0098562 \\ \hline GWO & 6072.3698 & 6220.1946 & 0.0856987 \\ \hline ABC & 6143.8965 & 6486.1785 & 2.1896527 \\ \hline BBO & 6128.1289 & 6759.7563 & 4.8965232 \\ \hline PSO & 6286.3214 & 7169.7248 & 8.7452328 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The outcomes of the algorithms proposed for the pressure vessel system.  
Figure 7: The convergence trend of algorithms for the pressure vessel system.
## 6 Conclusions  
Previous research that was carried out in the last two decades of the 21 st century was focused on trying to solve the problem of failure detection, its localization, the most effective locations of measuring sensors, and minimizing their number. Many authors have also touched upon the issue of appropriate time intervals of the performed measurements. Due to the time-consuming and costly methods of the direct detection of water supply network failures, the world's research focuses mainly on software solutions based on the analysis of time series of parameters of the water supply network operating status--hydraulic or acoustic phenomena. Devices used in the hardware approach are the domain of commercial companies, which compete in patents for tools for precise leak localization.  
None of the methods currently used to detect water main failures are universal. This is due to the specific characteristics of water supply systems. Various methods have numerous advantages and disadvantages and are diverse in terms of implementation costs, speed of failure detection, and the possibility of assessing the amount of water outflow due to an uncontrolled leak.  
For these reasons, in the authors' opinion, failure detection processes should be carried out in two stages. The first one consists of a global detection of failures and the initial selection of their localization by means of software solutions, using the analysis of hydraulic measurements made with high-precision sensors. Next, it is necessary to implement tools for the precise localization of the method in order to detect the place of leakage. These issues are a new field that is subject to continuous scientific and technological development, which gives hope for the application of increasingly recognized advanced methods on a wider scale in the practice of operating water supply networks.  
[PERSON] conceived of the presented idea. [PERSON] and [PERSON] developed the theory and performed the review. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Wroclaw University of Environmental and Life Sciences (Poland) under the Ph.D. research program Innowacyjny Naukowice, no. N060/0014/21.  
Not applicable.  
The authors wish to thank [PERSON] for consultation, valuable comments, and tips.  
The authors declare no conflict of interest.
## 4 Numerical Simulation
### Study of Boundary Conditions  
The temperature field of the batteries and coolant under a working condition of an effective current at 25 A and \(\delta\) = 0.973, Q = 5 L/min, N = 6 is shown in Figure 12. It can be seen that the maximum temperature difference T\({}_{\rm diff}\) on the same monitoring surface between the battery cells is below 3 \({}^{\circ}\)C, and the maximum temperature T\({}_{\rm max}\) of the battery is 36.86 \({}^{\circ}\)C. The maximum and minimum cell temperatures (at the surface opposite the cooling area) exist at the two corners of the pack. Because the flow consistency in each branching serpentine pipe was optimized in the previous section, the temperature difference T\({}_{\rm i}\) between cells corresponding to the different branching serpentine pipes is well controlled. The temperature difference T\({}_{\rm j}\) between the cells increases as the temperature of the coolant in the cooling channel gradually increases, but the total temperature difference T\({}_{\rm diff}\) correctly meets the design expectations.  
Using the same methods and parameters, simulations were also performed at other boundary conditions, including different flow rates, different root mean square (RMS) currents, and different coolant temperatures. A matrix list of these simulations is shown in Table 5. The simulation results will be compared and discussed together with the test results in the next section of the experiment.
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Manual RFF Estimation  
Five technicians were trained to manually estimate RFF using methodology described in [PERSON] and [PERSON] (2009). The dataset used to train individuals in manual RFF estimation was a separate dataset from that described here and may be downloaded from [[https://sites.bu.edu/stepplab/research/rff/](https://sites.bu.edu/stepplab/research/rff/)]([https://sites.bu.edu/stepplab/research/rff/](https://sites.bu.edu/stepplab/research/rff/)) accessed on 21 April 2021. Technicians were required to meet an interrater reliability criterion \(\geq\)0.93, as described in [PERSON] et al. (2018). Table 2 shows the number of participants that each of the five technicians rated throughout the course of data collection. Two trained technicians carried out manual RFF estimation on each participant (7721 total /ifi/ productions). Mean RFF values were computed across two technicians to use as the gold standard for RFF estimates.  
Intrarater reliability was assessed via Pearson's correlation coefficients within each technician when instructed to reanalyze 20% of participants in a separate sitting, whereas interrater reliability was computed via two-way mixed-effects ICCs for consistency of agreement. The average intrarater reliability was calculated as \(r\) = 0.90 (SD = 0.05, range = 0.84-0.97), and the average interrater reliability was computed as _ICC(3,1)_ = 0.93 (SD = 0.04, range = 0.87-0.98). Rater reliability was also examined by assessing the difference between selected boundary cycles (i.e., voicing offset cycle 10, voicing onset cycle 1) of original and reanalyzed samples. The mean intrarater error was 0.64 vocal cycles (SD = 0.44 cycles, range = 0-5 cycles), and the mean interrater error was 0.71 vocal cycles (SD = 0.41 cycles, range = 0-6 cycles).
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 2 Virtual Inertia and Current Control Techniques
### Hysteresis Controller  
The hysteresis current control technique is a simple and effective technique to achieve fast dynamics within the current control loop [27]. The inherent current limiting capability, robustness, and model-free implementation are some of the main benefits of the hysteresis control approach [28]. In this technique, a \"hysteresis band\" is defined around the reference current that the controller needs to track. The switches of the inverter are then controlled in such a manner that the output current of the inverter always stays within this band; hence, there is no need for a PWM modulator. This greatly improves the transient response time of this control approach, but this also means that the switching frequency is variable, depending largely on the selected hysteresis band and load/operating conditions, making the design of the output filter difficult [29]. Alternate techniques to keep the switching frequency constant are proposed in [30; 31]. In [32], a method of using a sinusoidally-varying hysteresis band is proposed to reduce the output current and power ripple. However, for simplicity, a generic hysteresis current controller as shown in Figure 4 is discussed in this paper.  
Figure 4a shows the control structure for a hysteresis current controller. The reference current in the \(dq\) domain is transformed into the \(abc\) domain. The reference current, \(i_{abc}^{*}\), is then compared with the feedback current \(i_{abc}\), and the error is fed into the hysteresis band to generate the switching signals for the three-phase inverter. Figure 4b gives a more detailed description of the gate signals' generation processes. When the actual current output of the inverter exceeds the pre-defined hysteresis band, the gate signal goes low, causing the current to decay. The rate of decay depends on the time-constant of circuit (\(r\) and \(L\) parameter values). As the current decays, the current exceeds the lower hysteresis band, and the gate signal goes high, causing the current to rise. In this manner, the actual current waveform  
Figure 3: Schematic diagram of a PR-based current controller; PR: proportional-resonant; PWM: pulse width modulation; “*” represents the reference values of the signals.  
is made to follow the reference current within the pre-defined hysteresis band. The hysteresis band selection is often selected to be between 2 and 5% of the reference current [33]. The band is usually limited by the switching capability of the switches and the electromagnetic interference concerns. The lower the band, the higher the switching frequency, and vice versa.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Robustness and Transferability  
Due to the difficulty in obtaining a building damage assessment dataset other than the xBD dataset, we selected four independent disaster events outside the xBD dataset to verify the transferability and robustness of the proposed method. The details of the events are listed in Table 3. For each instance, the pre-disaster and post-disaster images are fed into our framework. The results are shown in Figure 11.  
In the cases of the tornadoes, the buildings are less dense. Due to the seriously damaged buildings, most of the buildings in the image are labeled as destroyed in our model, which is consistent with the damage situation. The results of RescueNet and MaskRCNN cannot accurately outline the buildings and therefore fail to predict the damage levels.  
In the cases of typhoon Yutu, the results of our model shows that the majority of the buildings in the image are well detected and correctly located. Some multi-story buildings cast large areas of shadows on the upper right side, which limits the ability to locate buildings and causes some errors around these buildings. In the damage assessment stage, it can be seen that most of the single-story houses are correctly classified as reasonable damage levels from no damage to destroyed. However, for the multi-story buildings located in the right of the image, the majority of them are recognized as destroyed or major damage. It can be seen from the post-disaster image that the texture features of some of the roofs are changed. For some buildings which are detected as destroyed, the top floors are damaged but the structure of the buildings still remains. Moreover, the effect of side shooting distracts the assessment, which is common on high buildings. On the whole, the predicted results of our method are more precise than other compared methods.  
Figure 10: The visual comparison of prediction results on palu-tsunami in xBD dataset. (**a**) Pre-disaster; (**b**) post-disaster; (**c**) ground truth; (**d**) baseline; (**e**) SDAFormer (without SF); (**f**) SDAFormer (with SF).
## 3 Datasets and Methodology
### Methods
#### 3.2.2 Pixel Dichotomy Model  
Vegetation cover (FVC) refers to the percentage of the total area covered with ground vegetation [52]. The vegetation cover calculated using the NDVI-based pixel dichotomy model has high accuracy and is widely used [53]. In this study, the FVC was determined using MODIS13A1 monthly synthetic NDVI data for the period spanning from April to October, which represents the vegetation growing season, from 2000 to 2022. The model of the pixel dichotomy was adopted for the sake of calculating the FVC, and the average monthly FVC values were combined to calculate the annual FVC. The formula for the FVC is as follows:  
\[\text{FVC}=(\text{NDVI}-\text{NDVI}_{\text{soil}})/\big{(}\text{NDVI}_{\text{ veg}}-\text{NDVI}_{\text{soil}}\big{)} \tag{1}\]  
NDVI\({}_{\text{soil}}\) is nearly 0, and it refers to the NDVI value of pure bare soil pixels. NDVI\({}_{\text{veg}}\) is roughly 1, and it refers to the NDVI of pure vegetation pixels. In fact, noise frequently impacts remote-sensing images, which makes it hard for them to be up to standard in real-world environments. In the calculation process, the gray value of the image element at the 5 th and 95 th percentiles is typically used to calculate the FVC in desert areas, taking into account the environmental characteristics of arid regions [54; 55]. In this study, in combination with previous research experience, the first 5% of the NDVI was chosen as the NDVI\({}_{\text{soil}}\), while the NDVI value corresponding to the latter 95% was selected as NDVI\({}_{\text{veg}}\).
## 2 Theory
### THM Coupling Equation of CO\({}_{2}\) Displacement CBM
#### 2.2.2 Coupling Equation of Permeation and Diffusion of Binary Gas  
Gas seepage in coal seams is driven by a pressure gradient and gas gravity is ignored. The gas seepage velocity expression in coal is derived from [PERSON]'s law [25]:  
\[q_{S}=-\frac{KRT}{\mu_{i}}\
abla c_{i} \tag{8}\]  
where \(q_{S}\) is seepage velocity(m\({}^{3}\)/s); \(\
abla=\left[\frac{\partial}{\partial x^{\prime}}\frac{\partial}{\partial y^{ \prime}}\frac{\partial}{\partial z}\right]\); and \(\mu_{i}\) is viscosity coefficient of single phase.  
Gas diffusion follows [PERSON]'s law. Its diffusion flux is expressed as follows [39]:  
\[J_{i}=-D_{i}\
abla c_{i} \tag{9}\]  
where \(J_{i}\) is the diffusion component of single phase; \(D_{i}\) is the diffusion coefficient of single phase (m\({}^{2}\)/s).  
CO\({}_{2}\) is injected into coal seams containing CH\({}_{4}\) driven by a pressure gradient, and the flow of binary gas in coal seam conforms to the convection-diffusion equation [25]:  
\[\frac{\partial(m_{i})}{\partial t}+\
abla\cdot(-\frac{kRT}{\mu_{i}}\
abla c_{ i}\rho_{i})+\
abla\cdot(-D_{i}\rho\
abla c_{i})=Q_{S} \tag{10}\]  
where \(i\) is the single component gas; \(i=1\) is CH\({}_{4}\); \(i=2\) is CO\({}_{2}\); \(m_{i}\) is the gas mass of each component (kg); \(c_{i}\) is the molar concentration of a single component (mol/m\({}^{3}\)); \(\rho_{i}\) is the density of a single component (kg/m\({}^{3}\)); k is permeability (m\({}^{2}\)); \(\varphi\) is porosity; and \(Q_{S}\) is the source term (kg/(m\({}^{3}\cdot\)s)).  
For the ideal state equation of binary mixed gas, the molar concentration of each gas is expressed as follows [24]:  
\[c_{i}=\frac{P_{i}}{RT} \tag{11}\]  
CO\({}_{2}\) and CH\({}_{4}\) gases exist simultaneously in the coal seam during CO\({}_{2}\) displacement. The mass of the gas is the sum of adsorbed and free gases considering the influence of temperature on the adsorption capacity. The Langmuir adsorption equation can be used to represent the adsorption mass as follows:  
\[m_{i}=\rho_{c}\rho_{a}\frac{a_{i}b_{i}c_{i}RT}{1+\sum_{i=1}^{2}b_{i}P_{i}}+ \varphi M_{i}c_{i} \tag{12}\]  
Combined with Equations (8)-(12), the binary gas seepage and diffusion equation can be obtained as follows:\[\frac{\partial\bigg{(}\rho_{c}\rho_{a}\frac{a_{i}b_{i}cRT}{1+\sum_{i=1}^{2}b_{i}P_{ i}}+\varphi M_{i}c_{i}\bigg{)}}{\partial t}+\
abla\cdot(-\frac{kRT}{\mu_{i}}\
abla c _{i}\rho_{i})+\
abla\cdot(-D_{i}\varphi\
abla c_{i})=Q_{8} \tag{13}\]
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Relocation of the Edge in Subpixel-Level
#### 2.2.1 Improvement of the Edge Model  
Most existing ZOM-based edge-detection methods adopt the simple step function as the edge model, assuming that the gray values at the edge change abruptly as seen in Figure 2a. In the figure, \(v^{\prime}\) is the axis parallel to the edge, \(u^{\prime}\) is the axis perpendicular to the edge, and \(l\) represents the pixel intensity. However, the intensity at the edge changes gradually in real images. Therefore, the linear ramp edge model has been developed for the intensity profile across an edge [31], as shown in Figure 2b, where \(l\) is the distance from the center to the ideal edge.  
The ideal edge spread function (ESF) for the pixel intensity in a unit circle is expressed as  
\[I(u^{\prime})=h+\frac{k}{2}\bigg{[}1+erf(\frac{u^{\prime}-l}{\sigma\sqrt{2}}) \bigg{]}, \tag{1}\]  
where \(\sigma\) controls the edge width, \(h\) represents the background intensity, and \(k\) is the step height.  
Figure 2: Edge models: (**a**) step edge model; (**b**) linear ramp edge model.  
After introducing the linear ramp edge model, the ESF in a unit circle can be expressed as a piecewise function as  
\[I(u^{\prime})=\begin{cases}h&u^{\prime}\leq l-\omega\\ h+k[u^{\prime}-(l-\omega)]/(2\omega)&l-\omega<u^{\prime}<l+\omega,\\ h+k&u^{\prime}\geq l+\omega\end{cases} \tag{2}\]  
where \(2\omega\) is the edge width. The function is approximate to the Gaussian ESF when \(\omega=1.66\sigma\).
## 1 Introduction
* A new ECOA-FS technique is designed for the selection of the optimal subset of features to accomplish enhanced classification performance. * JSSO is employed with the ANFIS model for the classification of intrusions into different class labels. * The performance of the proposed model is validated on the benchmark NSL-KDD dataset. The rest of the paper is organized as follows. Section 2 provides the related works, and Section 3 introduces the proposed model. Later, Section 4 offers experimental validation and Section 5 draws the conclusions.
# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 4 Experiments and Result Analysis
### Scenario 1: Parameter Tuning  
This first scenario aims at tuning the values of our method's parameters that ensure sufficient performance. We firstly tune the most suitable aggregation method among the three well-known methods: bag of visual words (BoVW), vector of linearly aggregated  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Corel-5k** & **MSRC v2** \\ \hline Dataset size & 5000 & 591 \\ Train set size & 4500 & 394 \\ Test set size & 500 & 197 \\ Number of labels & 371 & 23 \\ Mean labels per image & 3.4 & 2.5 \\ Mean images per label & 58.6 & 28.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the two datasets used, Corel-5k and MSRC v2.  
descriptors (VLAD), and Fisher vector (FV). Figure 4 represents the precision and recall yielded using features encoded by each of the aforementioned aggregation methods.  
From Figure 4, it appears that VLAD has the best performance among the others. FV, on the other hand, has yielded the worst performance due to the second-order information it takes into account which is not helpful in cases of segmented homogeneous regions. We opted for VLAD in the remainder of this section because of the sufficient performance and the fast vector quantization it provides.  
The K parameter of the KNN regression algorithm might be affected by different factors such as the task it is used for, the length of the feature vector, and the number of classes. To determine which value fits most for our task of automatic image annotation, we have evaluated the KNN algorithm with K values ranging from 1 to 50. Figure 5 shows the impact of changing \(K\) values on the final precision and recall.  
From Figure 5, it appears that our method grants the best performance at \(K=40\). However, \(K=17\) has rather been chosen to provide a trade-off between precision and computation speed.  
Figure 4: Precision and recall yielded using the three aggregation methods: BoVF, VLAD, and FV.  
Figure 5: The impact of changing the value of K of KNN regressor on (**a**) the precision and (**b**) the recall of our proposed method.  
Since our method engages off-the-shelf CNN-based features, we evaluate several CNN models to determine which is the best for our task. The performance is determined not only in terms of precision and recall, but also in terms of time consumed in image processing. Figure 6 shows the impact of using different CNN models on the precision and recall of our proposed method.  
From Figure 6, it appears that the best two CNN models are Vgg-16 and MobileNet. However, the latter suffers from the high complexity (huge number of parameters) which requires far more time of calculation (30 times slower) compared to the former. In our model, we have opted for MobileNet to achieve a better trade-off between accuracy and computation time.  
In this first scenario, we aimed at tuning parameters to obtain, to some extent, satisfactory results. Thus, VLAD aggregation method, \(K=17\), and MobileNet model have been considered in the following experiments.