esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Performance of Hybrid Filter under Various Conditions  
The performance of hybrid filter during variation of solar irradiations and wind speed variation is illustrated in Figure 7a,b, which shows the characteristics waveforms of load voltage \((V_{L})\), current in the PMSG connected to a wind turbine \((I_{WT})\), load current \((i_{L})\), filter current \((i_{Ftr})\) supplied by the HSAPF, PV actual \((I_{PV})\) and reference currents \(\left(I_{PVref}\right)\), mechanical torque developed by the WT \((T_{m})\), wind speed \((V_{w})\), DC link voltage \((V_{dc})\), and SOC percentage, which is less than 100%. From the simulation results, it is noticed that, with the variation of solar irradiations and wind speed, the values of the PV current, torque (mechanical) developed by the generator, and the SOC percentage of the battery are also varying. The system is operated for a time period of 0 to 3 s. The variation of the behavior performance of different system parameters is noticed at 0.8 s during the active participation of HSAPF. Therefore, in this paper, the performance of the proposed system is observed during the period from 0.75 s to 0.85 s. In the beginning, at 0.75 s to 0.77 s, there is a variation of characteristics of system parameters, i.e., the variation of DC link voltage, filter current, PV current, load current, PMSG current, percentage of SOC, and mechanical torque developed. During the period where there is a variation of solar irradiations, and wind energy, the DC bus voltage, and its frequency are maintained constant because of load-leveling supplied by BESS through VSI.  
It is observed that the SOC percentage of BESS is rising from 0.75 s and maintain consistency up to 0.8 s, which is due to excess power produced from the renewable energy source (RES). After t = 0.8 s, the SOC percentage of BESS starts slightly decreasing due to the supply shortage of the load demands. The simulation waveforms, shown in Figure 7, from 0.75 s to t = 0.8 s, prove the dynamic characteristics of the proposed technique. Further, it is also noticed that the PV current monitors closed tracking of its reference and authorized the smooth performance of the proposed LINC technique.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 5 Results
### Results
#### 5.2.2 Evaluating Incrementality  
We evaluated the effectiveness of the _incremental approach_, contrasting it to the batch approach. The batch computation approach looks at the entire dataset when new data are available to be processed. From this perspective, two different scenarios were evaluated: In the first scenario, \(Q\) was executed by the evaluation of the included queries individually (e.g., without rewriting), and in the second scenario, the rewritten set \(Q^{\prime}\) was executed as defined by the rewritten theory. The incremental computation approach is more efficient by examining only the new incoming data in the last time interval and incorporates the increment in the result. From this perspective we evaluate the two scenarios again: the first scenario required the execution of the \(Q\); the second scenario required the execution of rewritten \(Q^{\prime}\).  
Figures 8 and 9 illustrate the evaluation times when \(Q\) and \(Q^{\prime}\) were executed using the MapReduce execution model and the two different approaches; Figure 10 illustrates the evaluation time when \(Q\) and \(Q^{\prime}\) are executed using the SQL execution model and the two different approaches. For example, at time \(t+3\Delta t\), the batch computation approach requires one to execute the query set \(Q\) or the rewritten set \(Q^{\prime}\), over all data generated in range of \(t\leq+3\Delta t\). At time \(t+3\Delta t\), the incremental computation approach requires one to execute the query set \(Q\) or the rewritten set \(Q^{\prime}\) only on data generated in range of \(t+2\Delta t\leq t\leq t+3\Delta t\), and then incorporates the increment into the aggregated result.  
The experimental results presented in the figures demonstrate the effectiveness of our incremental computation. As shown, as the dataset grows, the evaluation cost remains stable independently of the overall increasing data size. On the other hand, when the queries are evaluated over a batch data, the evaluation cost grows linearly to the size of the input batch data. Furthermore, as shown, the execution time of the non-rewritten queries is dramatically higher as the dataset increases, independently of the applicable rewriting rule. However, when the rewritings are used, in total, querying consumes significantly less execution time, exploiting the shared common attributes. In addition, we can observe that the execution times are similar to when the MapReduce and the SQL execution models are used for the same rewritings (Figures 8 and 10).  
Figure 8: Evaluation of CGMRR and CGRR when the MapReduce execution model is used over and unstructured dataset.  
Figure 9: Evaluation of CMORR and BRR when the MapReduce execution model is used over and unstructured dataset.
## 2 Numerical Models
### Governing Equation
\begin{array}{l}\Phi_{C}=\mu_{c}p_{c}u\\ \Phi_{W}=\eta\left[\left(\frac{\partial\varphi_{W}\theta}{\partial\theta} \right)^{2}+\left(\frac{\partial\varphi_{W}}{\partial z}\right)^{2}\right] \end{array} \right. \tag{15}\]  
where \(\mu_{c}\) denotes the friction coefficient of the asperity contact, and its value was assumed to be 0.15 in this study. Subsequently, the heat source, \(\Phi\), can be obtained as follows:  
\[\Phi=\frac{\Phi_{W}\mathrm{d}V+\Phi_{C}\mathrm{d}A}{\mathrm{d}V} \tag{16}\]  
In Equation (16), \(\mathrm{d}V\) is the differential volume for the water film, and its corresponding area at the journal-rubber interface is represented as \(\mathrm{d}A\). Consequently, the thermal properties of the JWR system can be determined by coupling Equations (13)-(16). Similar to the evaluation of the thermal deformation, the influence function method was also employed to obtain the temperature distribution of the JWR system. The corresponding influence functions can be referred to in Ref. [34]. The effect of the temperature rise on the water viscosity can be evaluated by:  
\[\eta(\theta,z,r)=1.005\times 10^{-3}\left(\frac{T_{W}(\theta,z,r)}{293}\right)^{ 8.9}e^{[4700(1/T_{W}(\theta,z,r))-1/293]} \tag{17}\]  
where \(\eta(\theta,z,r)\) and \(T_{W}(\theta,z,r)\) are the 3D average viscosity and 3D average temperature of the water film along the radial direction, respectively. It should be noted that the unit of the temperature is K in Equation (17). Once the hydrodynamic and asperity contact pressures are obtained, the external force acting on the propeller shaft can be calculated as follows:  
\[\left\{ \begin{array}{l}F_{x}=\int\limits_{0}^{L}\int\limits_{0}^{2\pi}p( \theta,z)\sin(\theta-\varphi)R_{B}\mathrm{d}\theta\mathrm{d}z\\ F_{y}=-\int\limits_{0}^{L}\int\limits_{0}^{2\pi}p(\theta,z)\cos(\theta- \varphi)R_{B}\mathrm{d}\theta\mathrm{d}z\end{array} \right. \tag{18}\]  
where \(F_{x}\) and \(F_{y}\) represent the force, including hydrodynamic and contact forces, in the \(x\) and \(y\) directions, respectively. Subsequently, the load capacity can be described by \(F=\sqrt{F_{x}^{2}+F_{y}^{2}}\), which is equal to the external force \(M\). The friction force, \(F_{f}\), considering the water film shearing in the circumferential direction and the asperity contact at the journal-rubber interface can be estimated by:  
\[F_{f}=\int\limits_{0}^{L}\int\limits_{0}^{2\pi}\left(\frac{\eta\omega{R_{1}}^ {2}}{h}+\frac{h}{2}\frac{\partial p_{h}}{\partial\theta}\right)\mathrm{d} \theta\mathrm{d}z+\int\limits_{0}^{L}\int\limits_{0}^{2\pi}\mu_{c}p_{c}(\theta,z)R_{B}\mathrm{d}\theta\mathrm{d}z \tag{19}\]  
where \(\omega\) is the rotation speed of the propeller shaft. Subsequently, the average friction coefficient, \(\mu\), that combines the effects of viscous shearing and asperity contact is given by:  
\[\mu=\frac{F_{f}}{F} \tag{20}\]
## 5 Case Study and Discussion
### Experiment Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Quality** & \multicolumn{10}{c}{**Causes**} \\ \cline{2-13}
**Defects** & **om11** & **om13** & **mat** & **mt** & **pw11** & **pw13** & **pw53** & **cm1** & **cm2** & **ht1** & **ht2** & **tem1** & **tem2** \\ \hline sr & 4 & 3 & 2 & 5 & 4 & 2 & 1 & 2 & 4 & 4 & 0 & 3 & 1 \\ mc1 & 1 & 3 & 7 & 3 & 1 & 1 & 3 & 10 & 0 & 0 & 0 & 2 & 0 \\ ce1 & 0 & 2 & 3 & 3 & 2 & 3 & 3 & 3 & 0 & 1 & 0 & 1 & 1 \\ de1 & 0 & 0 & 1 & 2 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 1 & 0 \\ de2 & 0 & 1 & 0 & 0 & 1 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ de3 & 1 & 2 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 2 & 0 \\ de4 & 2 & 1 & 1 & 2 & 2 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ bh & 2 & 3 & 0 & 0 & 2 & 3 & 2 & 1 & 2 & 2 & 0 & 3 & 2 \\ imp & 1 & 1 & 3 & 1 & 2 & 1 & 0 & 3 & 9 & 3 & 0 & 0 \\ hel1 & 1 & 1 & 5 & 3 & 0 & 1 & 5 & 4 & 2 & 6 & 0 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Empirical data of quality defects and causes. Here, the causes with bold are verified to be the diagnosis results of quality defect of the bearing spacer. Among them, \"om11\" and \"pw11\" only occur on \(GU(1)\). Therefore, in this experiment, we can get the diagnosis conclusions as follows: The age of the machine of rough turning or semi-finish turning, the material of the bearing spacer, the proficiency of worker of rough turning or semi-finish turning, the structure of casting mold and the holding time of quenching are the causes of defects in \(GU(1)\); the material of the bearing spacer, the structure of the casting mold and the holding time of quenching are the causes of defects in \(GU(2)\) and \(GU(3)\). In summary, the codes of information set \(KCL\) of \(GU(1)\), \(GU(2)\) and \(GU(3)\) in target gene should be (\"om11\", \"mt\", \"pw11\", \"cm1\", \"ht1\"), (\"mt\", \"cm1\", \"ht1\") and (\"mt\", \"cm1\", \"ht1\"). Then a new diagnosis knowledge should be established based on the information and diagnosis conclusions of the target gene for further application.
## 5 Modeling of Power Supply
### Multi-Criteria Decision-Making (MCDM) Example Using Promethee  
To select the best configuration for EV charging stations, the PROMETHEE method is applied. This method evaluates multiple criteria, such as cost, environmental impact, energy efficiency, and user convenience. The following steps outline the decision-making process:  
**Step 1: Define the List of Alternatives.** For this study, the alternatives (different charging station capacities) are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Alternatives** & **Description** \\ \hline
22 kW & Small charging station \\
50 kW & Medium charging station \\
150 kW & Large charging station \\ \hline \hline \end{tabular}
\end{table}
Table 1: Alternatives and criteria definition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Meaning** & **Weight** \\ \hline Cost & Solution cost in EUR & 0.4 \\ Environmental impact & CO\({}_{2}\) savings in kg/year & 0.3 \\ Energy efficiency & Panel efficiency (kWh/m\({}^{2}\)) & 0.2 \\ User convenience & Usage satisfaction (score) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria and assigned weights.  
**Step 3: Compile the Evaluation Matrix.** The evaluation matrix comparing different charging station alternatives across the given criteria is shown in Table 3.  
**Step 4: Normalize the Evaluation Matrix.** Normalization ensures that all criteria are on comparable scales. The normalized cost matrix \(C^{norm}\) and environmental impact matrix \(E^{norm}\) are presented:  
\[\begin{split} C^{norm}=\begin{bmatrix}0&0.214&1\\ 0.333&0&0.833\\ 0.167&0.5&0.167\\ \end{bmatrix}\\ E^{norm}=\begin{bmatrix}0.833&0.5&1\\ 0.666&1&0.333\\ 0.333&0&0.666\\ \end{bmatrix}\end{split} \tag{11}\]  
**Step 5: Calculate Preference Index and Flows.** For each criterion, compute the preference index for each pair of alternatives. The preference index for cost is given by  
\[P(a,b)=\frac{C_{b}-C_{a}}{C_{max}-C_{min}} \tag{12}\]  
**Step 6: Rank the Alternatives.** Using PROMETHEE, compute the positive, negative, and net flows for each alternative using Formulas (4) and (5).
## 4 Assessment of the Developed Approach
### Assessment of Segmentations and Point Classification
#### 4.2.3 Segmentation into Planes  
As explained in Section 3, several plane segmentations are necessary and the number of segmentations is determined automatically. In order to validate if this number is correctly determined, remaining point clouds were analysed for both datasets. At the end of plane segmentations of room point clouds, 6% of points classified as walls were not extracted for the individual house and 12% for the office building.  
It is interesting to compare remaining points with reference point clouds of \"walls\" and \"occluding objects\" categories. Among remaining points, 70% belong in reality to objects for the individual house whereas 30% for the office building. This means that quality criteria integrated in the approach allows rejecting points belonging to objects falsely classified as walls.
## 2 Theory and Models
### Instantentious Power Representation
#### 2.4.1 Average Power  
The coefficient at \(m=0\) in (30) is of particular interest due to the fact that \(P^{(0)}\) represents the constant, or DC component, of the power \(p(t)\), also referred to as average power \(P_{av}\), which can be evaluated by means of time averaging over one period:  
\[P_{av}=P^{(0)}=\mathcal{R}_{vi}^{0}(0)=\frac{1}{T}\int_{T}p(t)dt. \tag{32}\]  
Active power is traditionally interpreted as the rate of energy exchange in the long run. Provided the passive sign convention is assumed, an element of the circuit will be called a load if \(P_{av}>0\). In contrast, if \(P_{av}<0\), the element is an energy source. The remaining case, \(P_{av}=0\), means the element is neither an absorber nor a supplier in the long run. However, it is important to notice that \(P_{av}=0\) does not imply \(p(t)=0\).  
The average power expression via CSCD components is followed immediately from (31):  
\[P_{av}=\mathcal{R}_{vi}^{0}(0)=\sum_{k=-\infty}^{+\infty}\mathcal{S}_{vi}^{(0,k)}. \tag{33}\]  
Equation (22), remaining valid for any \(m\), can be specialized to the particular case where \(m=0\):  
\[\mathcal{S}_{vi}^{(0,k)}=M_{v}^{(RF)}\Big{[}M_{i}^{(RF)}\Big{]}^{*}. \tag{34}\]  
As long as both the voltage and current remain real valued functions, the coefficients in their expansions, (3) and (4), exhibit Hermitian symmetry:  
\[M_{v}^{(-kF)}=\Big{[}M_{v}^{(RF)}\Big{]}^{*},\ M_{i}^{(-kF)}=\Big{[}M_{i}^{( RF)}\Big{]}^{*}. \tag{35}\]  
This property also establishes the Hermitian symmetry of the weights of Dirac delta functions at zero cyclic frequency:  
\[\mathcal{S}_{vi}^{(0,-k)}=\Big{[}\mathcal{S}_{vi}^{(0,k)}\Big{]}^{*}. \tag{36}\]  
Using the well known identity held for any complex \(A\): \(A+A^{*}=2\Re\epsilon A=2\Re\epsilon A^{*}\), the average power can be rewritten as follows:  
\[P_{av}=\mathcal{R}_{vi}^{0}(0)=\mathcal{S}_{vi}^{(0,0)}+2\sum_{k=1}^{+\infty} \Re\epsilon\mathcal{S}_{vi}^{(0,k)}=\sum_{k=-\infty}^{+\infty}\Re\epsilon \mathcal{S}_{vi}^{(0,k)}, \tag{37}\]where \(\Re\epsilon\) marks the real part of the complex number, as well as \(\Im\mathfrak{m}\) is marking the imaginary part below.
# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 4 Case Study
### Data Collection and Pre-Processing  
This research focuses on inspecting the fastening bolts of steel structures, which have become increasingly important due to the structural safety of large-scale construction projects [27]. The conventional method requires inspectors to visually inspect each bolt to ensure proper fastening [40]. To develop an object detection model for bolt fastening inspection, two methods were employed to collect image data. Firstly, image data was collected during visits to university building construction sites where steel structures were being erected. Secondly, images of bolts in steel structures were obtained from internet websites. By using a web-crawling approach, images of bolts on steel frames were collected from a Google image search. This web-crawling method utilized several keywords for searching training images, such as 'T/S bolt', 'bolt on steel frame', and 'T/S bolt fastening'. After searching and collecting images, irrelevant images are manually deleted from the training dataset. This deletion process is decided based on the similarity with real images collected at the construction site. Next, 30 images were collected from construction sites, and a total of 174 relevant images were retrieved from internet searches, resulting in a dataset of 204 images. The dataset was then divided into 154 training images, 20 validation images, and 30 test images. After image collection, the LabelImg tool, implemented in Python, was utilized to annotate bounding boxes around the target objects. This process generated corresponding information in XML format, where the class indicated the type of object, (x, y) denoted the upper-left coordinate of the bounding box, and (w, h) represented the width and height of the bounding box. As the detection model aimed to identify fractures in T/S bolt pintails, the bolts could be categorized into two classes: \"no pintail\", indicating a fastened bolt where the pintail is not visible, and \"pintail\", indicating an unfastened bolt where the pintail is visible.
## 3 Results and Discussion
### PM10-Bound PAH Profiles
At the industrial site, the mean BaP concentration was significantly lower in the summer (0.19 \(\pm\) 0.09 ng m\({}^{-3}\), \(p\) = 0.047) than in the rainy season (0.45 \(\pm\) 0.36 ng m\({}^{-3}\)). At the urban background site, the mean BaP concentration was significantly lower in summer (0.15 \(\pm\) 0.09 ng m\({}^{-3}\), \(p\) = 0.005) than in the rainy season (0.34 \(\pm\) 0.24 ng m\({}^{-3}\)). Higher temperatures and available solar radiation could favor the loss of PAHs from photochemical reactions, resulting in the lowest mean BaP concentrations in the summer. The mean BaP concentrations in the rainy season and winter were not significantly different at all sampling sites. The highest BaP concentrations were observed in the rainy season at all sites, while the highest PM10 concentrations occurred in the winter. This implied that sources of BaP were particles distributed in a smaller size range than 10 \(\upmu\)m.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 3 Proposed Method
As shown in Figure 1, undergoing a series of SFT operations, data cubes streamed in the approximation branch are spatially adjusted  
Figure 1: Overall structure of the spectral-aware pansharpening neural network (SA-PNN), which comprises a detail branch (green branch) and an approximation branch (blue branch). layer by layer and thus spectral processing is strengthened by spatial adaptiveness. With the detail injection being fulfilled layer-wise, a convolutional layer with a kernel in size \(1\times 1\times 1\) is applied at the top of the network to yield the final HR-MBO image. As mentioned above, the parameter pair \((\alpha,\beta)\) carries spatial information related to the observed scene and modulates the feature map data in the approximation branch according to (3) and (4). Intuitively, the modulation should be bidirectional, meaning that \(\alpha\) and \(\beta\) could be positive or negative. Therefore, the leaky ReLU (LReLU) [44] in the following  
\[\mathbf{LReLU}(x)= \begin{cases}x&x>0\\ ax&\text{else},\end{cases}  \tag{5}\]  
rather than the standard ReLU is used in the relevant convolution layers yielding \(\alpha\) and \(\beta\) (as shown in Figure 2), where \(a\) is a positive constant. In brief, the proposed SA-PNN adopt a two-branch network structure, where one is the detail branch to be mainly responsible for spatial detail distilling while the other is the approximation branch to extract spectral-spatial information from the data and to fulfill injecting details with SFTs in a layer-wise manner; thus, SA-PNN has clear interpretability of the detail injection. Moreover, to strengthen the spectral processing to alleviate spectral distortion in the final pansharpening result, a spectral-aware strategy is developed which comprises SFTs and 3D convolutional layers jointly, where, especially SFTs can make spectra of the processed data to be automatically adjusted with respect to the observed scene.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 3 LADRC-Based Synchronous Control Method
### LADRC Design  
On the basis of the preliminarily established phase dynamic model, we extended the fluctuation as an additional state variable, and the system state was extended to \(x=[x_{1},x_{2}]\). Letting \(h(t)=-\frac{1}{T_{2}}\tan\beta+\Omega_{z}\pm\omega_{r}\) represent the extended state of \(x_{2}\), then the original plant in (13) can be rewritten as,  
\[\begin{array}{l}\dot{x}_{1}=x_{2}+b_{0}u\\ \dot{x}_{2}=\dot{h}\\ y=x_{1}\end{array} \tag{20}\]The task of the observer design is not only observing the unmeasured system states (i.e., \(x_{1}\)) but also estimating the modeling uncertainty \(x_{2}\) for controller compensation in real time. Let \(\hat{x}\) denote the estimate of \(x\). The extended system model can be rewritten as  
\[\begin{array}{c}\dot{\hat{x}}_{1}=\hat{x}_{2}+b_{0}u+G(y-\hat{y})\\ \hat{y}=C\hat{x}\end{array} \tag{21}\]  
The error of the observer is defined as,  
\[e=x-\hat{x} \tag{22}\]  
Thereby,  
\[\dot{e}=(A-GC)e \tag{23}\]  
Assuming that the matrix (A-GC) is a Hurwitz matrix. The error of the observer \(e\) is converged to 0,  
\[\left\{\begin{array}{c}\lim\limits_{t\rightarrow\infty}|x_{1}-\hat{x}_{1}|= 0,\\ \lim\limits_{t\rightarrow\infty}|x_{2}-\hat{x}_{2}|=0\end{array}\right. \tag{24}\]  
Denoting that  
\[G=\left(\beta_{1},\beta_{2}\right)^{T} \tag{25}\]  
The characteristic equation of \((A-GC)\) is  
\[s^{2}+\beta_{1}s+\beta_{2}=0 \tag{26}\]  
The poles are placed at \(-\omega_{O}\),  
\[\left(s+\omega_{o}\right)^{2}=0 \tag{27}\]  
The gain matrix of the observer is obtained,  
\[G=\left(2\omega_{o},\omega_{o}^{2}\right)^{T} \tag{28}\]  
The LESO is formed as  
\[\begin{array}{c}\dot{\hat{x}}_{1}=\beta_{1}(y-\hat{x}_{1})+bu+\hat{x}_{2}, \\ \dot{\hat{x}}_{2}=\beta_{2}(y-\hat{x}_{1})\end{array} \tag{29}\]  
As it is shown in Equation (29), the LESO always converges as \(\omega_{o}>0\). The total disturbance can be estimated by the LESO and the feedback control value is designed as  
\[u=\frac{1}{b}(-\hat{x}_{2}+K_{c}(r-\hat{x}_{1})),K_{c}>0 \tag{30}\]  
where \(K_{c}\) is the feedback gain and \(r\) is the refer input.